Previsão_de_Ações_do_Yahoo!.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Prevendo o valor de Fechamento de Ações do Yahoo!\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "DbviRHEeJgyd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nessa etapa da primeira atividade faremos a análise da série considerada mais complexa (ou não tão bem comportada).\n",
        "\n",
        "Esta série é dada pelo histórico dos valores das ações do Yahoo na bolsa de valores americana.\n",
        "\n",
        "Está disponível no Kaggle no link: https://www.kaggle.com/datasets/arashnic/time-series-forecasting-with-yahoo-stock-price\n",
        "\n",
        "Para fins de análise utilizaremos os valores de fechamento das ações, geralmente usados nas atividades de trade.\n",
        "\n",
        "A primeira etapa é proceder com as importações das bibliotecas necessárias para o desenvolvimento do projeto.\n",
        "\n",
        "Caso não tenha o statsmodels instalando pode usar o comando de temrminal: `pip install statsmodels`"
      ],
      "metadata": {
        "id": "iH_OAu8tJeYH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "crO5H5OWJUNw"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from pandas import Series\n",
        "import matplotlib.pyplot as plt\n",
        "from statsmodels.stats.stattools import jarque_bera\n",
        "\n",
        "#Libs com os modelos de previsao\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.tsa.exponential_smoothing.ets import ETSModel\n",
        "\n",
        "#Ignorar as Warnings do Codigo\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A função abaixo constrói e calibra um modelo de previsão de séries temporais conhecida como ETS. Um modelo de suavização exponencial muito utilizado na indústria e no âmbito acadêmico.\n",
        "\n",
        "As funções e parâmetros utilizados são definidos pela biblioteca statsmodels e podemos encontrar um guia completo para o seu entendimento no link:\n",
        "\n",
        "https://www.statsmodels.org/dev/examples/notebooks/generated/ets.html\n",
        "\n",
        "Os parâmetros centrais serão encontrados através da análise da própria série temporal. Outros parâmetros, considerados importantes para uma boa previsão, serão testados através do uso desta função.\n",
        "\n",
        "A função aqui definida tem como parâmetros a própria série temporal, uma lista de parâmetros e o intervalo de previsão (setamos como 15 para este ensaio)."
      ],
      "metadata": {
        "id": "uKQrApUtmax3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def previsao_ets(desp_serie, intervalo, parametros):\n",
        "\n",
        "    error = parametros[0]\n",
        "    trend = parametros[1]\n",
        "    seasonal = parametros[2]\n",
        "    initialization = parametros[3]\n",
        "    seasonal_periods = parametros[4]\n",
        "\n",
        "    arr_real =[]\n",
        "    inter_real = []\n",
        "\n",
        "    for i in range(intervalo):\n",
        "        max_size = max(desp_serie.index)\n",
        "        arr_real.append(desp_serie[max_size])\n",
        "        inter_real.append(max_size)\n",
        "        desp_serie = desp_serie.drop(max_size)\n",
        "\n",
        "    model = ETSModel(desp_serie, error=error, trend=trend, seasonal=seasonal,\n",
        "                     initialization_method=initialization, seasonal_periods=seasonal_periods)\n",
        "    model_fit = model.fit(disp=0)\n",
        "\n",
        "    start = len(desp_serie)\n",
        "    end = len(desp_serie)+intervalo-1\n",
        "\n",
        "    previsto = model_fit.predict(start = start, end = end)\n",
        "    serie_real = Series(arr_real, inter_real)\n",
        "    #Invertendo a serie\n",
        "    serie_real = serie_real.iloc[::-1]\n",
        "\n",
        "    size = len(previsto)\n",
        "    erro = []\n",
        "\n",
        "    for i in range(size):\n",
        "        parc_erro = (previsto[i] - serie_real[i])/serie_real[i]\n",
        "        erro.append(abs(parc_erro))\n",
        "\n",
        "    erro_medio = np.mean(erro)\n",
        "    return abs(erro_medio), previsto, model_fit"
      ],
      "metadata": {
        "id": "EoNMRo8PJzS-"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Neste momento fazemos a leitura do arquivo, o tratamento dos seus campos para que tenhamos apenas a série temporal que será considera neste ensaio!"
      ],
      "metadata": {
        "id": "PMQUC4CcoGil"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class_file = '/content/drive/MyDrive/datasets/yahoo_stock.csv'\n",
        "df = pd.read_csv(class_file, index_col=False, sep=',')\n",
        "df.drop('High', inplace=True, axis=1)\n",
        "df.drop('Low', inplace=True, axis=1)\n",
        "df.drop('Open', inplace=True, axis=1)\n",
        "df.drop('Volume', inplace=True, axis=1)\n",
        "df.drop('Adj Close', inplace=True, axis=1)\n",
        "\n",
        "df.index = pd.to_datetime(df.Date, format=\"%Y-%m-%d\")\n",
        "df.drop(\"Date\", inplace=True, axis=1)\n",
        "\n",
        "serie_original = df.iloc[:,0]\n",
        "serie_size = len(serie_original)\n",
        "intervalo = 15"
      ],
      "metadata": {
        "id": "Girb0b5yJ7Sx"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "'''\n",
        "Aqui iniciamos a previsao utilizando o modelo ETS\n",
        "'''\n",
        "v_erro = ['add', 'mul']\n",
        "v_trend = ['add', 'mul']\n",
        "v_seasonal = ['add', 'mul']\n",
        "v_init = ['estimated', 'heuristic']\n",
        "v_period = [18]\n",
        "\n",
        "best_vlerro = 99\n",
        "best_para = []\n",
        "best_prev = []\n",
        "modelo = ''\n",
        "for erro in v_erro:\n",
        "    for trend in v_trend:\n",
        "        for seasonal in v_seasonal:\n",
        "            for init in v_init:\n",
        "                for period in v_period:\n",
        "                    parametros = [erro, trend, seasonal, init, period]\n",
        "                    vlerro, serie_prevista, modelo = previsao_ets(serie_original, intervalo, parametros)\n",
        "\n",
        "                    if vlerro < best_vlerro:\n",
        "                        best_vlerro = vlerro\n",
        "                        best_param = parametros\n",
        "                        best_prev = serie_prevista\n",
        "\n",
        "#Para uso no intervalo de confiança\n",
        "x = best_prev.index\n",
        "y = best_prev.values\n",
        "\n",
        "plt.figure(figsize=(16,8))\n",
        "serie_original[serie_size-2*intervalo:serie_size].plot(label=None)\n",
        "best_prev.plot(label='ETS Model')\n",
        "plt.fill_between(x, y*0.95, y+(y*0.05), color='g', alpha=.1)\n",
        "plt.fill_between(x, y*0.8, y+(y*0.2), color='r', alpha=.1)\n",
        "plt.title('MAPE Encontrado na Previsão: '+str(round(best_vlerro*100, 2))+'%')\n",
        "plt.show()\n",
        "\n",
        "print('============ Melhores Parametros ============')\n",
        "\n",
        "print(best_param)\n",
        "\n",
        "print('============ Analise de Ruido ============')\n",
        "print(' '*254)\n",
        "teste_ruido = jarque_bera(modelo.resid)\n",
        "print('Estatística Jarque-Bera :', teste_ruido[0])\n",
        "print('P-valor :', teste_ruido[1])\n",
        "print('Assimetria :', teste_ruido[2])\n",
        "print('Curtose :',teste_ruido[3])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 930
        },
        "id": "xA56RIBFKqGI",
        "outputId": "89a8c5d0-77de-468d-d1c5-e8c7bf6d85fb"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============ Melhores Parametros ============\n",
            "['add', 'mul', 'mul', 'heuristic', 18]\n",
            "============ Analise de Ruido ============\n",
            "                                                                                                                                                                                                                                                              \n",
            "Estatística Jarque-Bera : 46399.26534991293\n",
            "P-valor : 0.0\n",
            "Assimetria : -1.1074331014245546\n",
            "Curtose : 27.704923502641797\n"
          ]
        }
      ]
    }
  ]
}