MII/mai/lab3.1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Лабораторная работа 3\n",
    "\n",
    "Датасет - **Цены на золото**\thttps://www.kaggle.com/datasets/sid321axn/gold-price-prediction-dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Бизнес-цели**: \n",
    "1. Прогнозирование цены золота на момент закрытия для поддержки принятия решений по инвестициям.\n",
    "2. Оценка волатильности цен золота для долгосрочных стратегий инвестирования."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Цели технического проекта**: \n",
    "1. Создание модели машинного обучения для прогнозирования цены закрытия акций на золото на основе исторических данных (дат, цен открытия, максимальных и минимальных цен, объёма торгов).\n",
    "2. Разработка системы, которая вычисляет и анализирует волатильность на основе исторической ценовой информации и объёмов торгов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Date        Open        High         Low       Close   Adj Close  \\\n",
      "0     2011-12-15  154.740005  154.949997  151.710007  152.330002  152.330002   \n",
      "1     2011-12-16  154.309998  155.369995  153.899994  155.229996  155.229996   \n",
      "2     2011-12-19  155.479996  155.860001  154.360001  154.869995  154.869995   \n",
      "3     2011-12-20  156.820007  157.429993  156.580002  156.979996  156.979996   \n",
      "4     2011-12-21  156.979996  157.529999  156.130005  157.160004  157.160004   \n",
      "...          ...         ...         ...         ...         ...         ...   \n",
      "1713  2018-12-24  119.570000  120.139999  119.570000  120.019997  120.019997   \n",
      "1714  2018-12-26  120.620003  121.000000  119.570000  119.660004  119.660004   \n",
      "1715  2018-12-27  120.570000  120.900002  120.139999  120.570000  120.570000   \n",
      "1716  2018-12-28  120.800003  121.080002  120.720001  121.059998  121.059998   \n",
      "1717  2018-12-31  120.980003  121.260002  120.830002  121.250000  121.250000   \n",
      "\n",
      "        Volume     SP_open     SP_high      SP_low  ...    GDX_Low  GDX_Close  \\\n",
      "0     21521900  123.029999  123.199997  121.989998  ...  51.570000  51.680000   \n",
      "1     18124300  122.230003  122.949997  121.300003  ...  52.040001  52.680000   \n",
      "2     12547200  122.059998  122.320000  120.029999  ...  51.029999  51.169998   \n",
      "3      9136300  122.180000  124.139999  120.370003  ...  52.369999  52.990002   \n",
      "4     11996100  123.930000  124.360001  122.750000  ...  52.419998  52.959999   \n",
      "...        ...         ...         ...         ...  ...        ...        ...   \n",
      "1713   9736400  239.039993  240.839996  234.270004  ...  20.650000  21.090000   \n",
      "1714  14293500  235.970001  246.179993  233.759995  ...  20.530001  20.620001   \n",
      "1715  11874400  242.570007  248.289993  238.960007  ...  20.700001  20.969999   \n",
      "1716   6864700  249.580002  251.399994  246.449997  ...  20.570000  20.600000   \n",
      "1717   8449400  249.559998  250.190002  247.470001  ...  20.559999  21.090000   \n",
      "\n",
      "      GDX_Adj Close  GDX_Volume   USO_Open   USO_High    USO_Low  USO_Close  \\\n",
      "0         48.973877    20605600  36.900002  36.939999  36.049999  36.130001   \n",
      "1         49.921513    16285400  36.180000  36.500000  35.730000  36.270000   \n",
      "2         48.490578    15120200  36.389999  36.450001  35.930000  36.200001   \n",
      "3         50.215282    11644900  37.299999  37.610001  37.220001  37.560001   \n",
      "4         50.186852     8724300  37.669998  38.240002  37.520000  38.110001   \n",
      "...             ...         ...        ...        ...        ...        ...   \n",
      "1713      21.090000    60507000   9.490000   9.520000   9.280000   9.290000   \n",
      "1714      20.620001    76365200   9.250000   9.920000   9.230000   9.900000   \n",
      "1715      20.969999    52393000   9.590000   9.650000   9.370000   9.620000   \n",
      "1716      20.600000    49835000   9.540000   9.650000   9.380000   9.530000   \n",
      "1717      21.090000    53866600   9.630000   9.710000   9.440000   9.660000   \n",
      "\n",
      "      USO_Adj Close  USO_Volume  \n",
      "0         36.130001    12616700  \n",
      "1         36.270000    12578800  \n",
      "2         36.200001     7418200  \n",
      "3         37.560001    10041600  \n",
      "4         38.110001    10728000  \n",
      "...             ...         ...  \n",
      "1713       9.290000    21598200  \n",
      "1714       9.900000    40978800  \n",
      "1715       9.620000    36578700  \n",
      "1716       9.530000    22803400  \n",
      "1717       9.660000    28417400  \n",
      "\n",
      "[1718 rows x 81 columns]\n",
      "0       15323\n",
      "1       15324\n",
      "2       15327\n",
      "3       15328\n",
      "4       15329\n",
      "        ...  \n",
      "1713    17889\n",
      "1714    17891\n",
      "1715    17892\n",
      "1716    17893\n",
      "1717    17896\n",
      "Name: Date_numeric, Length: 1718, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "df = pd.read_csv(\"data/Gold.csv\")\n",
    "print(df)\n",
    "\n",
    "# Преобразование даты продажи в числовой формат (кол-во дней с 01.01.1970)\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df['Date_numeric'] = (df['Date'] - pd.Timestamp('1970-01-01')).dt.days\n",
    "print(df['Date_numeric'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume',\n",
      "       'SP_open', 'SP_high', 'SP_low', 'SP_close', 'SP_Ajclose', 'SP_volume',\n",
      "       'DJ_open', 'DJ_high', 'DJ_low', 'DJ_close', 'DJ_Ajclose', 'DJ_volume',\n",
      "       'EG_open', 'EG_high', 'EG_low', 'EG_close', 'EG_Ajclose', 'EG_volume',\n",
      "       'EU_Price', 'EU_open', 'EU_high', 'EU_low', 'EU_Trend', 'OF_Price',\n",
      "       'OF_Open', 'OF_High', 'OF_Low', 'OF_Volume', 'OF_Trend', 'OS_Price',\n",
      "       'OS_Open', 'OS_High', 'OS_Low', 'OS_Trend', 'SF_Price', 'SF_Open',\n",
      "       'SF_High', 'SF_Low', 'SF_Volume', 'SF_Trend', 'USB_Price', 'USB_Open',\n",
      "       'USB_High', 'USB_Low', 'USB_Trend', 'PLT_Price', 'PLT_Open', 'PLT_High',\n",
      "       'PLT_Low', 'PLT_Trend', 'PLD_Price', 'PLD_Open', 'PLD_High', 'PLD_Low',\n",
      "       'PLD_Trend', 'RHO_PRICE', 'USDI_Price', 'USDI_Open', 'USDI_High',\n",
      "       'USDI_Low', 'USDI_Volume', 'USDI_Trend', 'GDX_Open', 'GDX_High',\n",
      "       'GDX_Low', 'GDX_Close', 'GDX_Adj Close', 'GDX_Volume', 'USO_Open',\n",
      "       'USO_High', 'USO_Low', 'USO_Close', 'USO_Adj Close', 'USO_Volume',\n",
      "       'Date_numeric', 'Close_binned'],\n",
      "      dtype='object')\n",
      "Обучающая выборка:  (1030, 83)\n",
      "Close\n",
      "124.589996    4\n",
      "126.180000    3\n",
      "116.330002    3\n",
      "126.449997    3\n",
      "121.309998    3\n",
      "             ..\n",
      "115.489998    1\n",
      "131.759995    1\n",
      "121.169998    1\n",
      "118.989998    1\n",
      "124.500000    1\n",
      "Name: count, Length: 900, dtype: int64\n",
      "Контрольная выборка:  (343, 83)\n",
      "Close\n",
      "113.019997    2\n",
      "112.570000    2\n",
      "118.360001    2\n",
      "151.619995    2\n",
      "126.300003    2\n",
      "             ..\n",
      "170.770004    1\n",
      "117.550003    1\n",
      "124.279999    1\n",
      "157.429993    1\n",
      "121.339996    1\n",
      "Name: count, Length: 329, dtype: int64\n",
      "Тестовая выборка:  (344, 83)\n",
      "Close\n",
      "114.769997    3\n",
      "117.120003    2\n",
      "107.790001    2\n",
      "123.209999    2\n",
      "155.550003    2\n",
      "             ..\n",
      "160.440002    1\n",
      "117.599998    1\n",
      "113.419998    1\n",
      "119.750000    1\n",
      "114.830002    1\n",
      "Name: count, Length: 331, dtype: int64\n",
      "Обучающая выборка:  (1030, 83)\n",
      "Close_binned\n",
      "High      350\n",
      "Low       340\n",
      "Medium    340\n",
      "Name: count, dtype: int64\n",
      "Контрольная выборка:  (343, 83)\n",
      "Close_binned\n",
      "High      117\n",
      "Low       113\n",
      "Medium    113\n",
      "Name: count, dtype: int64\n",
      "Тестовая выборка:  (344, 83)\n",
      "Close_binned\n",
      "High      117\n",
      "Medium    114\n",
      "Low       113\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Функция для разбиения на обучающую, валидационную, тестовую выборки\n",
    "def split_stratified_into_train_val_test(\n",
    "    df_input,\n",
    "    stratify_colname=\"y\",\n",
    "    frac_train=0.6,\n",
    "    frac_val=0.15,\n",
    "    frac_test=0.25,\n",
    "    random_state=None,\n",
    "):\n",
    "    #проверка, что сумма долей выборок равна 1\n",
    "    if frac_train + frac_val + frac_test != 1.0:\n",
    "        raise ValueError(\n",
    "            \"fractions %f, %f, %f do not add up to 1.0\"\n",
    "            % (frac_train, frac_val, frac_test)\n",
    "        )\n",
    "\n",
    "    #проверка наличия указанного столбца для стратификации\n",
    "    if stratify_colname not in df_input.columns:\n",
    "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
    "\n",
    "    #разделение на признаки х и целевую переменную у\n",
    "    X = df_input \n",
    "    y = df_input[\n",
    "        [stratify_colname]\n",
    "    ]  \n",
    "\n",
    "    #разделение данных на обучающую и временную выборку с учетом стратификации\n",
    "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
    "        X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
    "    )\n",
    "\n",
    "    #вычисление относительной доли тестовой выборки по отношению к временной\n",
    "    relative_frac_test = frac_test / (frac_val + frac_test)\n",
    "    #разделение временной выборки на валидационную и тестовую\n",
    "    df_val, df_test, y_val, y_test = train_test_split(\n",
    "        df_temp,\n",
    "        y_temp,\n",
    "        stratify=y_temp,\n",
    "        test_size=relative_frac_test,\n",
    "        random_state=random_state,\n",
    "    )\n",
    "    #проверка что общее кол-во данных равно сумме трех выборок\n",
    "    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
    "\n",
    "    return df_train, df_val, df_test\n",
    "\n",
    "#создаем бины (интервалы) для столбца Close и присваиваем метки в новый столбец\n",
    "bins = [df['Close'].min(), df['Close'].quantile(0.33), df['Close'].quantile(0.66), df['Close'].max()]\n",
    "labels = ['Low', 'Medium', 'High']\n",
    "df['Close_binned'] = pd.cut(df['Close'], bins=bins, labels=labels)\n",
    "#удаляем строки с пропущенными значениями\n",
    "df = df.dropna()\n",
    "# вызываем ф-ию для разделения данных на выборки с стратификацией по новому столбцу Close_binned\n",
    "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
    "    df, stratify_colname=\"Close_binned\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n",
    ")\n",
    "\n",
    "print(df_train.columns) \n",
    "   \n",
    "print(\"Обучающая выборка: \", df_train.shape)\n",
    "print(df_train.Close.value_counts()) \n",
    "\n",
    "print(\"Контрольная выборка: \", df_val.shape)\n",
    "print(df_val.Close.value_counts())\n",
    "\n",
    "print(\"Тестовая выборка: \", df_test.shape)\n",
    "print(df_test.Close.value_counts())\n",
    "\n",
    "print(\"Обучающая выборка: \", df_train.shape)\n",
    "print(df_train['Close_binned'].value_counts())\n",
    "\n",
    "print(\"Контрольная выборка: \", df_val.shape)\n",
    "print(df_val['Close_binned'].value_counts())\n",
    "\n",
    "print(\"Тестовая выборка: \", df_test.shape)\n",
    "print(df_test['Close_binned'].value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Обучающая выборка после undersampling:  (1020, 83)\n",
      "Close\n",
      "124.589996    4\n",
      "125.540001    3\n",
      "122.209999    3\n",
      "121.110001    3\n",
      "121.580002    3\n",
      "             ..\n",
      "126.989998    1\n",
      "156.279999    1\n",
      "157.929993    1\n",
      "127.150002    1\n",
      "149.460007    1\n",
      "Name: count, Length: 892, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#уменьшаем дисбаланс и количество уникальных значений в столбце Close\n",
    "rus = RandomUnderSampler(random_state=42)\n",
    "X_resampled, y_resampled = rus.fit_resample(df_train, df_train[\"Close_binned\"])\n",
    "# Создание датафрейма для результирующей выборки\n",
    "df_train_rus = pd.DataFrame(X_resampled)\n",
    "print(\"Обучающая выборка после undersampling: \", df_train_rus.shape)\n",
    "print(df_train_rus.Close.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Date        Open        High         Low       Close   Adj Close  \\\n",
      "1540 2018-04-16  127.739998  128.050003  127.570000  127.629997  127.629997   \n",
      "568  2014-05-16  124.349998  124.769997  124.290001  124.500000  124.500000   \n",
      "972  2015-12-30  101.470001  101.599998  101.349998  101.419998  101.419998   \n",
      "1087 2016-06-16  125.169998  125.669998  122.230003  122.379997  122.379997   \n",
      "401  2013-08-22  132.559998  133.460007  132.270004  132.809998  132.809998   \n",
      "...         ...         ...         ...         ...         ...         ...   \n",
      "524  2014-03-12  131.559998  132.119995  131.389999  131.759995  131.759995   \n",
      "1409 2017-10-04  121.209999  121.269997  120.709999  121.169998  121.169998   \n",
      "672  2014-10-17  119.059998  119.230003  118.419998  118.989998  118.989998   \n",
      "1333 2017-06-14  121.510002  121.879997  119.570000  119.820000  119.820000   \n",
      "1695 2018-11-27  115.550003  115.629997  114.599998  114.949997  114.949997   \n",
      "\n",
      "        Volume     SP_open     SP_high      SP_low  ...    USO_Low  USO_Close  \\\n",
      "1540   4600000  267.000000  268.200012  266.070007  ...  13.340000  13.380000   \n",
      "568    4052100  187.509995  188.130005  186.720001  ...  37.090000  37.230000   \n",
      "972    3745000  207.110001  207.210007  205.759995  ...  10.840000  10.930000   \n",
      "1087  26635000  207.750000  208.570007  205.589996  ...  11.110000  11.140000   \n",
      "401    5741800  164.899994  166.300003  164.889999  ...  37.090000  37.540001   \n",
      "...        ...         ...         ...         ...  ...        ...        ...   \n",
      "524   11094200  186.320007  187.350006  185.899994  ...  35.040001  35.349998   \n",
      "1409   6475200  252.690002  253.440002  252.559998  ...  10.060000  10.080000   \n",
      "672    8059000  188.419998  189.750000  187.619995  ...  31.030001  31.250000   \n",
      "1333  21124800  244.860001  244.869995  243.289993  ...   9.200000   9.230000   \n",
      "1695   9671100  266.339996  268.399994  265.660004  ...  10.640000  10.950000   \n",
      "\n",
      "      USO_Adj Close  USO_Volume  Date_numeric  Close_binned_Low  \\\n",
      "1540      13.380000    14024600         17637             False   \n",
      "568       37.230000     1574900         16206             False   \n",
      "972       10.930000    24796400         16799              True   \n",
      "1087      11.140000    31068700         16968             False   \n",
      "401       37.540001     4511400         15939             False   \n",
      "...             ...         ...           ...               ...   \n",
      "524       35.349998     6572600         16141             False   \n",
      "1409      10.080000    13078000         17443             False   \n",
      "672       31.250000     7509000         16360             False   \n",
      "1333       9.230000    60687800         17331             False   \n",
      "1695      10.950000    36086100         17862              True   \n",
      "\n",
      "      Close_binned_Medium  Close_binned_High  Volume_binned  Price_change  \n",
      "1540                False               True              0     -0.110001  \n",
      "568                  True              False              0      0.150002  \n",
      "972                 False              False              0     -0.050003  \n",
      "1087                 True              False              3     -2.790001  \n",
      "401                 False               True              1      0.250000  \n",
      "...                   ...                ...            ...           ...  \n",
      "524                 False               True              3      0.199997  \n",
      "1409                 True              False              1     -0.040001  \n",
      "672                  True              False              2     -0.070000  \n",
      "1333                 True              False              3     -1.690002  \n",
      "1695                False              False              2     -0.600006  \n",
      "\n",
      "[1030 rows x 87 columns]\n"
     ]
    }
   ],
   "source": [
    "df_train = pd.get_dummies(df_train, columns=['Close_binned'])\n",
    "df_train['Volume_binned'] = pd.qcut(df_train['Volume'], q=4, labels=False)\n",
    "#Создание нового столбца 'Price_change', который показывает изменение цены (разница между закрытием и открытием).\n",
    "df_train['Price_change'] = df_train['Close'] - df_train['Open']\n",
    "print(df_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Date      Open      High       Low     Close   Adj Close    Volume  \\\n",
      "1540 2018-04-16  0.043550  0.030843  0.067156  0.038908  127.629997 -0.713195   \n",
      "568  2014-05-16 -0.154408 -0.159690 -0.126032 -0.143968  124.500000 -0.814939   \n",
      "972  2015-12-30 -1.490481 -1.505617 -1.477167 -1.492459  101.419998 -0.871967   \n",
      "1087 2016-06-16 -0.106524 -0.107409 -0.247363 -0.267833  122.379997  3.378675   \n",
      "401  2013-08-22  0.325013  0.345106  0.343980  0.341559  132.809998 -0.501164   \n",
      "...         ...       ...       ...       ...       ...         ...       ...   \n",
      "524  2014-03-12  0.266619  0.267266  0.292149  0.280211  131.759995  0.492769   \n",
      "1409 2017-10-04 -0.337768 -0.363002 -0.336889 -0.338529  121.169998 -0.364973   \n",
      "672  2014-10-17 -0.463316 -0.481504 -0.471767 -0.465900  118.989998 -0.070863   \n",
      "1333 2017-06-14 -0.320249 -0.327568 -0.404033 -0.417405  119.820000  2.355439   \n",
      "1695 2018-11-27 -0.668282 -0.690625 -0.696760 -0.701944  114.949997  0.228502   \n",
      "\n",
      "         SP_open     SP_high      SP_low  ...  USO_Close  USO_Adj Close  \\\n",
      "1540  267.000000  268.200012  266.070007  ...  13.380000      13.380000   \n",
      "568   187.509995  188.130005  186.720001  ...  37.230000      37.230000   \n",
      "972   207.110001  207.210007  205.759995  ...  10.930000      10.930000   \n",
      "1087  207.750000  208.570007  205.589996  ...  11.140000      11.140000   \n",
      "401   164.899994  166.300003  164.889999  ...  37.540001      37.540001   \n",
      "...          ...         ...         ...  ...        ...            ...   \n",
      "524   186.320007  187.350006  185.899994  ...  35.349998      35.349998   \n",
      "1409  252.690002  253.440002  252.559998  ...  10.080000      10.080000   \n",
      "672   188.419998  189.750000  187.619995  ...  31.250000      31.250000   \n",
      "1333  244.860001  244.869995  243.289993  ...   9.230000       9.230000   \n",
      "1695  266.339996  268.399994  265.660004  ...  10.950000      10.950000   \n",
      "\n",
      "      USO_Volume  Date_numeric  Close_binned_Low  Close_binned_Medium  \\\n",
      "1540    14024600         17637             False                False   \n",
      "568      1574900         16206             False                 True   \n",
      "972     24796400         16799              True                False   \n",
      "1087    31068700         16968             False                 True   \n",
      "401      4511400         15939             False                False   \n",
      "...          ...           ...               ...                  ...   \n",
      "524      6572600         16141             False                False   \n",
      "1409    13078000         17443             False                 True   \n",
      "672      7509000         16360             False                 True   \n",
      "1333    60687800         17331             False                 True   \n",
      "1695    36086100         17862              True                False   \n",
      "\n",
      "      Close_binned_High  Volume_binned  Price_change  Volatility  \n",
      "1540               True              0     -0.110001   -0.036313  \n",
      "568               False              0      0.150002   -0.033658  \n",
      "972               False              0     -0.050003   -0.028451  \n",
      "1087              False              3     -2.790001    0.139953  \n",
      "401                True              1      0.250000    0.001127  \n",
      "...                 ...            ...           ...         ...  \n",
      "524                True              3      0.199997   -0.024883  \n",
      "1409              False              1     -0.040001   -0.026113  \n",
      "672               False              2     -0.070000   -0.009737  \n",
      "1333              False              3     -1.690002    0.076466  \n",
      "1695              False              2     -0.600006    0.006134  \n",
      "\n",
      "[1030 rows x 88 columns]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "# Нормализация значений для указанных столбцов\n",
    "# чтобы значения разных столбов в среднем были 0, а стандартное отклонение - 1\n",
    "scaler = StandardScaler()\n",
    "df_train[['Open', 'Close', 'High', 'Low', 'Volume']] = scaler.fit_transform(\n",
    "    df_train[['Open', 'Close', 'High', 'Low', 'Volume']])\n",
    "#Создание нового столбца 'Volatility', который показывает волатильность (разницу между максимальной и минимальной ценой).\n",
    "df_train['Volatility'] = df_train['High'] - df_train['Low']\n",
    "print(df_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\3 kurs\\МИИ\\1 лаб\\mai-main\\.venv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<Feature: Open>,\n",
       " <Feature: High>,\n",
       " <Feature: Low>,\n",
       " <Feature: Close>,\n",
       " <Feature: Adj Close>,\n",
       " <Feature: Volume>,\n",
       " <Feature: SP_open>,\n",
       " <Feature: SP_high>,\n",
       " <Feature: SP_low>,\n",
       " <Feature: SP_close>,\n",
       " <Feature: SP_Ajclose>,\n",
       " <Feature: SP_volume>,\n",
       " <Feature: DJ_open>,\n",
       " <Feature: DJ_high>,\n",
       " <Feature: DJ_low>,\n",
       " <Feature: DJ_close>,\n",
       " <Feature: DJ_Ajclose>,\n",
       " <Feature: DJ_volume>,\n",
       " <Feature: EG_open>,\n",
       " <Feature: EG_high>,\n",
       " <Feature: EG_low>,\n",
       " <Feature: EG_close>,\n",
       " <Feature: EG_Ajclose>,\n",
       " <Feature: EG_volume>,\n",
       " <Feature: EU_Price>,\n",
       " <Feature: EU_open>,\n",
       " <Feature: EU_high>,\n",
       " <Feature: EU_low>,\n",
       " <Feature: EU_Trend>,\n",
       " <Feature: OF_Price>,\n",
       " <Feature: OF_Open>,\n",
       " <Feature: OF_High>,\n",
       " <Feature: OF_Low>,\n",
       " <Feature: OF_Volume>,\n",
       " <Feature: OF_Trend>,\n",
       " <Feature: OS_Price>,\n",
       " <Feature: OS_Open>,\n",
       " <Feature: OS_High>,\n",
       " <Feature: OS_Low>,\n",
       " <Feature: OS_Trend>,\n",
       " <Feature: SF_Price>,\n",
       " <Feature: SF_Open>,\n",
       " <Feature: SF_High>,\n",
       " <Feature: SF_Low>,\n",
       " <Feature: SF_Volume>,\n",
       " <Feature: SF_Trend>,\n",
       " <Feature: USB_Price>,\n",
       " <Feature: USB_Open>,\n",
       " <Feature: USB_High>,\n",
       " <Feature: USB_Low>,\n",
       " <Feature: USB_Trend>,\n",
       " <Feature: PLT_Price>,\n",
       " <Feature: PLT_Open>,\n",
       " <Feature: PLT_High>,\n",
       " <Feature: PLT_Low>,\n",
       " <Feature: PLT_Trend>,\n",
       " <Feature: PLD_Price>,\n",
       " <Feature: PLD_Open>,\n",
       " <Feature: PLD_High>,\n",
       " <Feature: PLD_Low>,\n",
       " <Feature: PLD_Trend>,\n",
       " <Feature: RHO_PRICE>,\n",
       " <Feature: USDI_Price>,\n",
       " <Feature: USDI_Open>,\n",
       " <Feature: USDI_High>,\n",
       " <Feature: USDI_Low>,\n",
       " <Feature: USDI_Volume>,\n",
       " <Feature: USDI_Trend>,\n",
       " <Feature: GDX_Open>,\n",
       " <Feature: GDX_High>,\n",
       " <Feature: GDX_Low>,\n",
       " <Feature: GDX_Close>,\n",
       " <Feature: GDX_Adj Close>,\n",
       " <Feature: GDX_Volume>,\n",
       " <Feature: USO_Open>,\n",
       " <Feature: USO_High>,\n",
       " <Feature: USO_Low>,\n",
       " <Feature: USO_Close>,\n",
       " <Feature: USO_Adj Close>,\n",
       " <Feature: USO_Volume>,\n",
       " <Feature: Date_numeric>,\n",
       " <Feature: Close_binned_Low>,\n",
       " <Feature: Close_binned_Medium>,\n",
       " <Feature: Close_binned_High>,\n",
       " <Feature: Volume_binned>,\n",
       " <Feature: Price_change>,\n",
       " <Feature: Volatility>,\n",
       " <Feature: DAY(Date)>,\n",
       " <Feature: MONTH(Date)>,\n",
       " <Feature: WEEKDAY(Date)>,\n",
       " <Feature: YEAR(Date)>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#генерация новых признаков из данных с помощью featuretools\n",
    "import featuretools as ft\n",
    "# Создание EntitySet для объединения разных датасетов для удобного использования\n",
    "es = ft.EntitySet(id=\"stocks\")\n",
    "es = es.add_dataframe(\n",
    "    dataframe_name=\"stock_data\", \n",
    "    dataframe=df_train,                            \n",
    "    index=\"Date\")\n",
    "# Генерация признаков\n",
    "feature_matrix, feature_defs = ft.dfs(\n",
    "    entityset=es, \n",
    "    target_dataframe_name=\"stock_data\")\n",
    "\n",
    "feature_defs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Open                   float64\n",
      "High                   float64\n",
      "Low                    float64\n",
      "Adj Close              float64\n",
      "Volume                 float64\n",
      "                        ...   \n",
      "Close_binned_Medium       bool\n",
      "Close_binned_High         bool\n",
      "Volume_binned            int64\n",
      "Price_change           float64\n",
      "Volatility             float64\n",
      "Length: 86, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Оценка предсказательной способности\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "# Копирование датафрейма для регрессионного анализа\n",
    "df_train_regression = df_train.copy()\n",
    "# Определение признаков и целевой переменной\n",
    "X_train = df_train_regression.drop(['Close', 'Date'], axis=1)\n",
    "y_train = df_train_regression['Close']\n",
    "X_test = df_test.drop(['Close', 'Date'], axis=1)\n",
    "y_test = df_test['Close']\n",
    "# Преобразование категориальных признаков в дамми-переменные\n",
    "# (создание столбцов со значениями 0 или 1, если это булевой столбец)\n",
    "X_train_encoded = pd.get_dummies(X_train, drop_first=True)\n",
    "X_test_encoded = pd.get_dummies(X_test, drop_first=True)\n",
    "# Устранение различий в количестве столбцов между обучающей и тестовой выборками\n",
    "X_test_encoded = X_test_encoded.reindex(columns=X_train_encoded.columns, fill_value=0)\n",
    "# Проверка типов данных\n",
    "print(X_train_encoded.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Средняя абсолютная ошибка: 127.64575343475643\n",
      "Среднеквадратичная ошибка: 16571.79812749788\n"
     ]
    }
   ],
   "source": [
    "# Обучение модели линейной регрессии (поиск зависимостей между признаками)\n",
    "model = LinearRegression()\n",
    "model.fit(X_train_encoded, y_train)\n",
    "# Предсказание цены на тестовой выборке\n",
    "predictions = model.predict(X_test_encoded)\n",
    "# Оценка качества модели\n",
    "mae = mean_absolute_error(y_test, predictions)\n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "print(\"Средняя абсолютная ошибка:\", mae)\n",
    "print(\"Среднеквадратичная ошибка:\", mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "время, затраченное на обучение модели: 0.18988895416259766. Время, затраченное на предсказание: 0.002999544143676758\n"
     ]
    }
   ],
   "source": [
    "# Оценка скорости вычисления\n",
    "import time\n",
    "start_time = time.time()\n",
    "model.fit(X_train_encoded, y_train)\n",
    "training_time = time.time() - start_time\n",
    "\n",
    "start_time = time.time()\n",
    "predictions = model.predict(X_test_encoded)\n",
    "prediction_time = time.time() - start_time\n",
    "\n",
    "print(f'время, затраченное на обучение модели: {training_time}. Время, затраченное на предсказание: {prediction_time}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Оценка корреляции\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "corr_matrix = df_train_regression.corr()\n",
    "sns.heatmap(corr_matrix, annot=False)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}