02_lightgmb_kaggle.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "Taken from https://www.kaggle.com/code/sugghi/training-3rd-place-solution"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c110a13077def83c"
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Setup"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a563906b7f728e54"
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:12.843321100Z",
     "start_time": "2023-12-04T17:05:12.697415700Z"
    }
   },
   "id": "64cbb62e435a430"
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:12.891924700Z",
     "start_time": "2023-12-04T17:05:12.787970900Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import random\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import lightgbm as lgb\n",
    "import time\n",
    "import datetime\n",
    "from alpaca.data.historical import CryptoHistoricalDataClient\n",
    "from alpaca.data.requests import CryptoBarsRequest\n",
    "from alpaca.data.timeframe import TimeFrame\n",
    "from datetime import datetime, timedelta\n",
    "import config\n",
    "\n",
    "from feature_engineering import *\n",
    "from training import *\n",
    "\n",
    "import pickle\n",
    "import gc\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "n_fold = 7\n",
    "seed0 = 8586\n",
    "use_supple_for_train = True\n",
    "\n",
    "# If True, the period used to evaluate Public LB will not be used for training.\n",
    "# Set to False on final submission.\n",
    "not_use_overlap_to_train = False\n",
    "\n",
    "pd.set_option('display.max_rows', 100)\n",
    "pd.set_option('display.max_columns', 350)\n",
    "\n",
    "lags = [60,300,900]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [],
   "source": [
    "params = {\n",
    "    'early_stopping_rounds': 50,\n",
    "    'objective': 'regression',\n",
    "    'metric': 'rmse',\n",
    "#     'metric': 'None',\n",
    "    'boosting_type': 'gbdt',\n",
    "    'max_depth': 5,\n",
    "    'verbose': -1,\n",
    "    'max_bin':600,\n",
    "    'min_data_in_leaf':50,\n",
    "    'learning_rate': 0.03,\n",
    "    'subsample': 0.7,\n",
    "    'subsample_freq': 1,\n",
    "    'feature_fraction': 1,\n",
    "    'lambda_l1': 0.5,\n",
    "    'lambda_l2': 2,\n",
    "    'seed':seed0,\n",
    "    'feature_fraction_seed': seed0,\n",
    "    'bagging_fraction_seed': seed0,\n",
    "    'drop_seed': seed0,\n",
    "    'data_random_seed': seed0,\n",
    "    'extra_trees': True,\n",
    "    'extra_seed': seed0,\n",
    "    'zero_as_missing': True,\n",
    "    \"first_metric_only\": True\n",
    "         }"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:12.975839500Z",
     "start_time": "2023-12-04T17:05:12.882924300Z"
    }
   },
   "id": "3a627c09e23ae4ac"
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Data Processing"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "7c81a872d70ae97c"
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [],
   "source": [
    "def reduce_mem_usage(df):\n",
    "    \"\"\" iterate through all the columns of a dataframe and modify the data type\n",
    "        to reduce memory usage.        \n",
    "    \"\"\"\n",
    "    start_mem = df.memory_usage().sum() / 1024**2\n",
    "    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))\n",
    "\n",
    "    for col in df.columns:\n",
    "        col_type = df[col].dtype\n",
    "\n",
    "        if col_type != object:\n",
    "            c_min = df[col].min()\n",
    "            c_max = df[col].max()\n",
    "            if str(col_type)[:3] == 'int':\n",
    "                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
    "                    df[col] = df[col].astype(np.int8)\n",
    "                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
    "                    df[col] = df[col].astype(np.int16)\n",
    "                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
    "                    df[col] = df[col].astype(np.int32)\n",
    "                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
    "                    df[col] = df[col].astype(np.int64)  \n",
    "            else:\n",
    "                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
    "                    df[col] = df[col].astype(np.float16)\n",
    "                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
    "                    df[col] = df[col].astype(np.float32)\n",
    "                else:\n",
    "                    df[col] = df[col].astype(np.float64)\n",
    "        else:\n",
    "            df[col] = df[col].astype('category')\n",
    "\n",
    "    end_mem = df.memory_usage().sum() / 1024**2\n",
    "    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n",
    "    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n",
    "\n",
    "    return df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:13.068697300Z",
     "start_time": "2023-12-04T17:05:12.975839500Z"
    }
   },
   "id": "1f2b608dac54b8ec"
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [],
   "source": [
    "# no keys required for crypto data\n",
    "client = CryptoHistoricalDataClient()\n",
    "\n",
    "symbol = \"BTC/USD\"\n",
    "request_params = CryptoBarsRequest(\n",
    "    # symbol_or_symbols=[\"BTC/USD\", \"ETH/USD\"],\n",
    "    symbol_or_symbols=symbol,\n",
    "    timeframe=TimeFrame.Hour,\n",
    "    start=datetime.strptime(\"2017-03-01\", '%Y-%m-%d')\n",
    ")\n",
    "\n",
    "bars = client.get_crypto_bars(request_params)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:25.758311400Z",
     "start_time": "2023-12-04T17:05:13.069697600Z"
    }
   },
   "id": "a022c409dad50d4"
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of dataframe is 0.39 MB\n",
      "Memory usage after optimization is: 0.29 MB\n",
      "Decreased by 25.0%\n"
     ]
    }
   ],
   "source": [
    "# convert to dataframe\n",
    "btc = bars.df.loc[symbol]\n",
    "btc = btc[['close']].copy()\n",
    "btc.fillna(method='ffill', limit=100)\n",
    "btc = reduce_mem_usage(btc)\n",
    "\n",
    "df_train, df_val, df_test = (\n",
    "    btc.loc[:config.TRAIN_END_DATE].copy(), \n",
    "    btc.loc[config.VAL_START_DATE:config.VAL_END_DATE].copy(),\n",
    "    btc.loc[config.TEST_START_DATE:].copy()\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:26.020589600Z",
     "start_time": "2023-12-04T17:05:25.759312200Z"
    }
   },
   "id": "d5e8e7d7fca53251"
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "must be real number, not list",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[49], line 2\u001B[0m\n\u001B[0;32m      1\u001B[0m hours_ahead \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m24\u001B[39m\n\u001B[1;32m----> 2\u001B[0m df_test[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtarget\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[43mdf_test\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mclose\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrolling\u001B[49m\u001B[43m(\u001B[49m\u001B[43mwindow\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mhours_ahead\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43;01mlambda\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mx\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mx\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtolist\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241m.\u001B[39mshift(\u001B[38;5;241m-\u001B[39mhours_ahead \u001B[38;5;241m+\u001B[39m \u001B[38;5;241m1\u001B[39m)\n",
      "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\bitcoin-price-predictor-Frh5AYeV-py3.10\\lib\\site-packages\\pandas\\core\\window\\rolling.py:2000\u001B[0m, in \u001B[0;36mRolling.apply\u001B[1;34m(self, func, raw, engine, engine_kwargs, args, kwargs)\u001B[0m\n\u001B[0;32m   1979\u001B[0m \u001B[38;5;129m@doc\u001B[39m(\n\u001B[0;32m   1980\u001B[0m     template_header,\n\u001B[0;32m   1981\u001B[0m     create_section_header(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mParameters\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m   1998\u001B[0m     kwargs: \u001B[38;5;28mdict\u001B[39m[\u001B[38;5;28mstr\u001B[39m, Any] \u001B[38;5;241m|\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m   1999\u001B[0m ):\n\u001B[1;32m-> 2000\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m   2001\u001B[0m \u001B[43m        \u001B[49m\u001B[43mfunc\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   2002\u001B[0m \u001B[43m        \u001B[49m\u001B[43mraw\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mraw\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   2003\u001B[0m \u001B[43m        \u001B[49m\u001B[43mengine\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mengine\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   2004\u001B[0m \u001B[43m        \u001B[49m\u001B[43mengine_kwargs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mengine_kwargs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   2005\u001B[0m \u001B[43m        \u001B[49m\u001B[43margs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   2006\u001B[0m \u001B[43m        \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   2007\u001B[0m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\bitcoin-price-predictor-Frh5AYeV-py3.10\\lib\\site-packages\\pandas\\core\\window\\rolling.py:1422\u001B[0m, in \u001B[0;36mRollingAndExpandingMixin.apply\u001B[1;34m(self, func, raw, engine, engine_kwargs, args, kwargs)\u001B[0m\n\u001B[0;32m   1419\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m   1420\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mengine must be either \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mnumba\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m or \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mcython\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m-> 1422\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_apply\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m   1423\u001B[0m \u001B[43m    \u001B[49m\u001B[43mapply_func\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1424\u001B[0m \u001B[43m    \u001B[49m\u001B[43mname\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mapply\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1425\u001B[0m \u001B[43m    \u001B[49m\u001B[43mnumba_args\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mnumba_args\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1426\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\bitcoin-price-predictor-Frh5AYeV-py3.10\\lib\\site-packages\\pandas\\core\\window\\rolling.py:663\u001B[0m, in \u001B[0;36mBaseWindow._apply\u001B[1;34m(self, func, name, numeric_only, numba_args, **kwargs)\u001B[0m\n\u001B[0;32m    660\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m result\n\u001B[0;32m    662\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmethod \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124msingle\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[1;32m--> 663\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_apply_blockwise\u001B[49m\u001B[43m(\u001B[49m\u001B[43mhomogeneous_func\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mname\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnumeric_only\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    664\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    665\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_apply_tablewise(homogeneous_func, name, numeric_only)\n",
      "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\bitcoin-price-predictor-Frh5AYeV-py3.10\\lib\\site-packages\\pandas\\core\\window\\rolling.py:503\u001B[0m, in \u001B[0;36mBaseWindow._apply_blockwise\u001B[1;34m(self, homogeneous_func, name, numeric_only)\u001B[0m\n\u001B[0;32m    501\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_validate_numeric_only(name, numeric_only)\n\u001B[0;32m    502\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_selected_obj\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[1;32m--> 503\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_apply_series\u001B[49m\u001B[43m(\u001B[49m\u001B[43mhomogeneous_func\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mname\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    505\u001B[0m obj \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_create_data(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_selected_obj, numeric_only)\n\u001B[0;32m    506\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m name \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcount\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[0;32m    507\u001B[0m     \u001B[38;5;66;03m# GH 12541: Special case for count where we support date-like types\u001B[39;00m\n",
      "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\bitcoin-price-predictor-Frh5AYeV-py3.10\\lib\\site-packages\\pandas\\core\\window\\rolling.py:487\u001B[0m, in \u001B[0;36mBaseWindow._apply_series\u001B[1;34m(self, homogeneous_func, name)\u001B[0m\n\u001B[0;32m    484\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m (\u001B[38;5;167;01mTypeError\u001B[39;00m, \u001B[38;5;167;01mNotImplementedError\u001B[39;00m) \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[0;32m    485\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m DataError(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNo numeric types to aggregate\u001B[39m\u001B[38;5;124m\"\u001B[39m) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[1;32m--> 487\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[43mhomogeneous_func\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalues\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    488\u001B[0m index \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_slice_axis_for_step(obj\u001B[38;5;241m.\u001B[39mindex, result)\n\u001B[0;32m    489\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m obj\u001B[38;5;241m.\u001B[39m_constructor(result, index\u001B[38;5;241m=\u001B[39mindex, name\u001B[38;5;241m=\u001B[39mobj\u001B[38;5;241m.\u001B[39mname)\n",
      "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\bitcoin-price-predictor-Frh5AYeV-py3.10\\lib\\site-packages\\pandas\\core\\window\\rolling.py:658\u001B[0m, in \u001B[0;36mBaseWindow._apply.<locals>.homogeneous_func\u001B[1;34m(values)\u001B[0m\n\u001B[0;32m    655\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m func(x, start, end, min_periods, \u001B[38;5;241m*\u001B[39mnumba_args)\n\u001B[0;32m    657\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m np\u001B[38;5;241m.\u001B[39merrstate(\u001B[38;5;28mall\u001B[39m\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mignore\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[1;32m--> 658\u001B[0m     result \u001B[38;5;241m=\u001B[39m \u001B[43mcalc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalues\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    660\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m result\n",
      "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\bitcoin-price-predictor-Frh5AYeV-py3.10\\lib\\site-packages\\pandas\\core\\window\\rolling.py:655\u001B[0m, in \u001B[0;36mBaseWindow._apply.<locals>.homogeneous_func.<locals>.calc\u001B[1;34m(x)\u001B[0m\n\u001B[0;32m    646\u001B[0m start, end \u001B[38;5;241m=\u001B[39m window_indexer\u001B[38;5;241m.\u001B[39mget_window_bounds(\n\u001B[0;32m    647\u001B[0m     num_values\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mlen\u001B[39m(x),\n\u001B[0;32m    648\u001B[0m     min_periods\u001B[38;5;241m=\u001B[39mmin_periods,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    651\u001B[0m     step\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstep,\n\u001B[0;32m    652\u001B[0m )\n\u001B[0;32m    653\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_window_bounds(start, end, \u001B[38;5;28mlen\u001B[39m(x))\n\u001B[1;32m--> 655\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstart\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mend\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmin_periods\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mnumba_args\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\bitcoin-price-predictor-Frh5AYeV-py3.10\\lib\\site-packages\\pandas\\core\\window\\rolling.py:1449\u001B[0m, in \u001B[0;36mRollingAndExpandingMixin._generate_cython_apply_func.<locals>.apply_func\u001B[1;34m(values, begin, end, min_periods, raw)\u001B[0m\n\u001B[0;32m   1446\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m raw:\n\u001B[0;32m   1447\u001B[0m     \u001B[38;5;66;03m# GH 45912\u001B[39;00m\n\u001B[0;32m   1448\u001B[0m     values \u001B[38;5;241m=\u001B[39m Series(values, index\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_on)\n\u001B[1;32m-> 1449\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mwindow_func\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalues\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbegin\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mend\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmin_periods\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\bitcoin-price-predictor-Frh5AYeV-py3.10\\lib\\site-packages\\pandas\\_libs\\window\\aggregations.pyx:1410\u001B[0m, in \u001B[0;36mpandas._libs.window.aggregations.roll_apply\u001B[1;34m()\u001B[0m\n",
      "\u001B[1;31mTypeError\u001B[0m: must be real number, not list"
     ]
    }
   ],
   "source": [
    "hours_ahead = 24\n",
    "df_test['target'] = df_test['close'].rolling(window=hours_ahead).apply(lambda x: x.tolist()).shift(-hours_ahead + 1)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:23:01.533191400Z",
     "start_time": "2023-12-04T17:23:01.398744Z"
    }
   },
   "id": "ca6ae68d0b209784"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "e8d6b9cd3c440577"
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Feature Engineering"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "cdba275d250d98a3"
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:72: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[f\"lag_{lag}\"] = df[\"close\"].shift(lag)\n",
      "C:\\Users\\User\\Documents\\1 Projects\\bitcoin_price_predictor\\feature_engineering.py:75: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[\"target\"] = df[\"close\"].shift(-full_week)\n"
     ]
    },
    {
     "data": {
      "text/plain": "                                  close  log_close/mean_60  log_return_60  \\\ntimestamp                                                                   \n2021-01-15 09:00:00+00:00  38638.250000           0.047538       0.108208   \n2021-01-15 10:00:00+00:00  38410.539062           0.039508       0.130221   \n2021-01-15 11:00:00+00:00  38066.011719           0.028678       0.112045   \n2021-01-15 12:00:00+00:00  37758.960938           0.018199       0.150924   \n2021-01-15 13:00:00+00:00  37635.718750           0.012897       0.128137   \n...                                 ...                ...            ...   \n2023-11-27 13:00:00+00:00  36955.343750          -0.017255      -0.022997   \n2023-11-27 14:00:00+00:00  36807.449219          -0.020803      -0.027946   \n2023-11-27 15:00:00+00:00  36766.648438          -0.021445      -0.028201   \n2023-11-27 16:00:00+00:00  37072.050781          -0.012845      -0.019770   \n2023-11-27 17:00:00+00:00  36927.945312          -0.016339      -0.024132   \n\n                           mean_close/mean_60  mean_log_returns_60  \\\ntimestamp                                                            \n2021-01-15 09:00:00+00:00            0.023853         1.489534e-10   \n2021-01-15 10:00:00+00:00            0.023853         1.489534e-10   \n2021-01-15 11:00:00+00:00            0.023853         1.489534e-10   \n2021-01-15 12:00:00+00:00            0.023853         1.489534e-10   \n2021-01-15 13:00:00+00:00            0.023853         1.489534e-10   \n...                                       ...                  ...   \n2023-11-27 13:00:00+00:00            0.023853         1.489534e-10   \n2023-11-27 14:00:00+00:00            0.023853         1.489534e-10   \n2023-11-27 15:00:00+00:00            0.023853         1.489534e-10   \n2023-11-27 16:00:00+00:00            0.023853         1.489534e-10   \n2023-11-27 17:00:00+00:00            0.023853         1.489534e-10   \n\n                           log_close/mean_60-mean_close/mean_60  \\\ntimestamp                                                         \n2021-01-15 09:00:00+00:00                              0.023685   \n2021-01-15 10:00:00+00:00                              0.015655   \n2021-01-15 11:00:00+00:00                              0.004825   \n2021-01-15 12:00:00+00:00                             -0.005654   \n2021-01-15 13:00:00+00:00                             -0.010956   \n...                                                         ...   \n2023-11-27 13:00:00+00:00                             -0.041108   \n2023-11-27 14:00:00+00:00                             -0.044656   \n2023-11-27 15:00:00+00:00                             -0.045298   \n2023-11-27 16:00:00+00:00                             -0.036697   \n2023-11-27 17:00:00+00:00                             -0.040192   \n\n                           log_return_60-mean_log_returns_60  \\\ntimestamp                                                      \n2021-01-15 09:00:00+00:00                           0.108208   \n2021-01-15 10:00:00+00:00                           0.130221   \n2021-01-15 11:00:00+00:00                           0.112045   \n2021-01-15 12:00:00+00:00                           0.150924   \n2021-01-15 13:00:00+00:00                           0.128137   \n...                                                      ...   \n2023-11-27 13:00:00+00:00                          -0.022997   \n2023-11-27 14:00:00+00:00                          -0.027946   \n2023-11-27 15:00:00+00:00                          -0.028201   \n2023-11-27 16:00:00+00:00                          -0.019770   \n2023-11-27 17:00:00+00:00                          -0.024132   \n\n                           log_close/mean_300  log_return_300  \\\ntimestamp                                                       \n2021-01-15 09:00:00+00:00            0.067505        0.157509   \n2021-01-15 10:00:00+00:00            0.061103        0.149166   \n2021-01-15 11:00:00+00:00            0.051418        0.213432   \n2021-01-15 12:00:00+00:00            0.042761        0.174858   \n2021-01-15 13:00:00+00:00            0.038988        0.156902   \n...                                       ...             ...   \n2023-11-27 13:00:00+00:00           -0.002428        0.040427   \n2023-11-27 14:00:00+00:00           -0.006557        0.036526   \n2023-11-27 15:00:00+00:00           -0.007789        0.037856   \n2023-11-27 16:00:00+00:00            0.000349        0.041053   \n2023-11-27 17:00:00+00:00           -0.003663        0.036048   \n\n                           mean_close/mean_300  mean_log_returns_300  \\\ntimestamp                                                              \n2021-01-15 09:00:00+00:00             0.121317          2.979069e-10   \n2021-01-15 10:00:00+00:00             0.121317          2.979069e-10   \n2021-01-15 11:00:00+00:00             0.121317          2.979069e-10   \n2021-01-15 12:00:00+00:00             0.121317          2.979069e-10   \n2021-01-15 13:00:00+00:00             0.121317          2.979069e-10   \n...                                        ...                   ...   \n2023-11-27 13:00:00+00:00             0.121317          2.979069e-10   \n2023-11-27 14:00:00+00:00             0.121317          2.979069e-10   \n2023-11-27 15:00:00+00:00             0.121317          2.979069e-10   \n2023-11-27 16:00:00+00:00             0.121317          2.979069e-10   \n2023-11-27 17:00:00+00:00             0.121317          2.979069e-10   \n\n                           log_close/mean_300-mean_close/mean_300  \\\ntimestamp                                                           \n2021-01-15 09:00:00+00:00                               -0.053812   \n2021-01-15 10:00:00+00:00                               -0.060214   \n2021-01-15 11:00:00+00:00                               -0.069899   \n2021-01-15 12:00:00+00:00                               -0.078556   \n2021-01-15 13:00:00+00:00                               -0.082329   \n...                                                           ...   \n2023-11-27 13:00:00+00:00                               -0.123745   \n2023-11-27 14:00:00+00:00                               -0.127873   \n2023-11-27 15:00:00+00:00                               -0.129105   \n2023-11-27 16:00:00+00:00                               -0.120967   \n2023-11-27 17:00:00+00:00                               -0.124980   \n\n                           log_return_300-mean_log_returns_300  \\\ntimestamp                                                        \n2021-01-15 09:00:00+00:00                             0.157509   \n2021-01-15 10:00:00+00:00                             0.149166   \n2021-01-15 11:00:00+00:00                             0.213432   \n2021-01-15 12:00:00+00:00                             0.174858   \n2021-01-15 13:00:00+00:00                             0.156902   \n...                                                        ...   \n2023-11-27 13:00:00+00:00                             0.040427   \n2023-11-27 14:00:00+00:00                             0.036526   \n2023-11-27 15:00:00+00:00                             0.037856   \n2023-11-27 16:00:00+00:00                             0.041053   \n2023-11-27 17:00:00+00:00                             0.036048   \n\n                           log_close/mean_900  log_return_900  \\\ntimestamp                                                       \n2021-01-15 09:00:00+00:00           10.561998        0.040444   \n2021-01-15 10:00:00+00:00           10.556087        0.036478   \n2021-01-15 11:00:00+00:00           10.547077        0.028930   \n2021-01-15 12:00:00+00:00           10.538978        0.018940   \n2021-01-15 13:00:00+00:00           10.535709        0.016423   \n...                                       ...             ...   \n2023-11-27 13:00:00+00:00            0.042008        0.224267   \n2023-11-27 14:00:00+00:00            0.037769        0.221354   \n2023-11-27 15:00:00+00:00            0.036436        0.216375   \n2023-11-27 16:00:00+00:00            0.044472        0.226338   \n2023-11-27 17:00:00+00:00            0.040348        0.221391   \n\n                           mean_close/mean_900  mean_log_returns_900  \\\ntimestamp                                                              \n2021-01-15 09:00:00+00:00             0.364721          4.766510e-09   \n2021-01-15 10:00:00+00:00             0.364721          4.766510e-09   \n2021-01-15 11:00:00+00:00             0.364721          4.766510e-09   \n2021-01-15 12:00:00+00:00             0.364721          4.766510e-09   \n2021-01-15 13:00:00+00:00             0.364721          4.766510e-09   \n...                                        ...                   ...   \n2023-11-27 13:00:00+00:00             0.364721          4.766510e-09   \n2023-11-27 14:00:00+00:00             0.364721          4.766510e-09   \n2023-11-27 15:00:00+00:00             0.364721          4.766510e-09   \n2023-11-27 16:00:00+00:00             0.364721          4.766510e-09   \n2023-11-27 17:00:00+00:00             0.364721          4.766510e-09   \n\n                           log_close/mean_900-mean_close/mean_900  \\\ntimestamp                                                           \n2021-01-15 09:00:00+00:00                               10.197277   \n2021-01-15 10:00:00+00:00                               10.191367   \n2021-01-15 11:00:00+00:00                               10.182357   \n2021-01-15 12:00:00+00:00                               10.174258   \n2021-01-15 13:00:00+00:00                               10.170988   \n...                                                           ...   \n2023-11-27 13:00:00+00:00                               -0.322713   \n2023-11-27 14:00:00+00:00                               -0.326952   \n2023-11-27 15:00:00+00:00                               -0.328285   \n2023-11-27 16:00:00+00:00                               -0.320248   \n2023-11-27 17:00:00+00:00                               -0.324373   \n\n                           log_return_900-mean_log_returns_900         lag_1  \\\ntimestamp                                                                      \n2021-01-15 09:00:00+00:00                             0.040444  38261.460938   \n2021-01-15 10:00:00+00:00                             0.036478  38638.250000   \n2021-01-15 11:00:00+00:00                             0.028930  38410.539062   \n2021-01-15 12:00:00+00:00                             0.018940  38066.011719   \n2021-01-15 13:00:00+00:00                             0.016423  37758.960938   \n...                                                        ...           ...   \n2023-11-27 13:00:00+00:00                             0.224267  36992.609375   \n2023-11-27 14:00:00+00:00                             0.221354  36955.343750   \n2023-11-27 15:00:00+00:00                             0.216375  36807.449219   \n2023-11-27 16:00:00+00:00                             0.226338  36766.648438   \n2023-11-27 17:00:00+00:00                             0.221391  37072.050781   \n\n                                  lag_2         lag_3         lag_4  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  38436.921875  38000.000000  37800.019531   \n2021-01-15 10:00:00+00:00  38261.460938  38436.921875  38000.000000   \n2021-01-15 11:00:00+00:00  38638.250000  38261.460938  38436.921875   \n2021-01-15 12:00:00+00:00  38410.539062  38638.250000  38261.460938   \n2021-01-15 13:00:00+00:00  38066.011719  38410.539062  38638.250000   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37046.425781  37110.019531  37386.000000   \n2023-11-27 14:00:00+00:00  36992.609375  37046.425781  37110.019531   \n2023-11-27 15:00:00+00:00  36955.343750  36992.609375  37046.425781   \n2023-11-27 16:00:00+00:00  36807.449219  36955.343750  36992.609375   \n2023-11-27 17:00:00+00:00  36766.648438  36807.449219  36955.343750   \n\n                                  lag_5         lag_6         lag_7  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  38419.769531  38669.949219  39219.519531   \n2021-01-15 10:00:00+00:00  37800.019531  38419.769531  38669.949219   \n2021-01-15 11:00:00+00:00  38000.000000  37800.019531  38419.769531   \n2021-01-15 12:00:00+00:00  38436.921875  38000.000000  37800.019531   \n2021-01-15 13:00:00+00:00  38261.460938  38436.921875  38000.000000   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37351.371094  37293.785156  37295.492188   \n2023-11-27 14:00:00+00:00  37386.000000  37351.371094  37293.785156   \n2023-11-27 15:00:00+00:00  37110.019531  37386.000000  37351.371094   \n2023-11-27 16:00:00+00:00  37046.425781  37110.019531  37386.000000   \n2023-11-27 17:00:00+00:00  36992.609375  37046.425781  37110.019531   \n\n                                  lag_8         lag_9        lag_10  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  39118.320312  39425.910156  39127.101562   \n2021-01-15 10:00:00+00:00  39219.519531  39118.320312  39425.910156   \n2021-01-15 11:00:00+00:00  38669.949219  39219.519531  39118.320312   \n2021-01-15 12:00:00+00:00  38419.769531  38669.949219  39219.519531   \n2021-01-15 13:00:00+00:00  37800.019531  38419.769531  38669.949219   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37315.109375  37382.367188  37319.820312   \n2023-11-27 14:00:00+00:00  37295.492188  37315.109375  37382.367188   \n2023-11-27 15:00:00+00:00  37293.785156  37295.492188  37315.109375   \n2023-11-27 16:00:00+00:00  37351.371094  37293.785156  37295.492188   \n2023-11-27 17:00:00+00:00  37386.000000  37351.371094  37293.785156   \n\n                                 lag_11        lag_12        lag_13  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  38932.648438  38767.269531  39319.371094   \n2021-01-15 10:00:00+00:00  39127.101562  38932.648438  38767.269531   \n2021-01-15 11:00:00+00:00  39425.910156  39127.101562  38932.648438   \n2021-01-15 12:00:00+00:00  39118.320312  39425.910156  39127.101562   \n2021-01-15 13:00:00+00:00  39219.519531  39118.320312  39425.910156   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37318.031250  37511.992188  37500.851562   \n2023-11-27 14:00:00+00:00  37319.820312  37318.031250  37511.992188   \n2023-11-27 15:00:00+00:00  37382.367188  37319.820312  37318.031250   \n2023-11-27 16:00:00+00:00  37315.109375  37382.367188  37319.820312   \n2023-11-27 17:00:00+00:00  37295.492188  37315.109375  37382.367188   \n\n                                 lag_14        lag_15        lag_16  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  39837.339844  39311.238281  39403.218750   \n2021-01-15 10:00:00+00:00  39319.371094  39837.339844  39311.238281   \n2021-01-15 11:00:00+00:00  38767.269531  39319.371094  39837.339844   \n2021-01-15 12:00:00+00:00  38932.648438  38767.269531  39319.371094   \n2021-01-15 13:00:00+00:00  39127.101562  38932.648438  38767.269531   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37460.746094  37674.539062  37622.535156   \n2023-11-27 14:00:00+00:00  37500.851562  37460.746094  37674.539062   \n2023-11-27 15:00:00+00:00  37511.992188  37500.851562  37460.746094   \n2023-11-27 16:00:00+00:00  37318.031250  37511.992188  37500.851562   \n2023-11-27 17:00:00+00:00  37319.820312  37318.031250  37511.992188   \n\n                                 lag_17        lag_18        lag_19  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  39240.449219  39506.679688  39574.878906   \n2021-01-15 10:00:00+00:00  39403.218750  39240.449219  39506.679688   \n2021-01-15 11:00:00+00:00  39311.238281  39403.218750  39240.449219   \n2021-01-15 12:00:00+00:00  39837.339844  39311.238281  39403.218750   \n2021-01-15 13:00:00+00:00  39319.371094  39837.339844  39311.238281   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37600.101562  37400.328125  37261.785156   \n2023-11-27 14:00:00+00:00  37622.535156  37600.101562  37400.328125   \n2023-11-27 15:00:00+00:00  37674.539062  37622.535156  37600.101562   \n2023-11-27 16:00:00+00:00  37460.746094  37674.539062  37622.535156   \n2023-11-27 17:00:00+00:00  37500.851562  37460.746094  37674.539062   \n\n                                 lag_20        lag_21        lag_22  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  38994.078125  38275.000000  38361.769531   \n2021-01-15 10:00:00+00:00  39574.878906  38994.078125  38275.000000   \n2021-01-15 11:00:00+00:00  39506.679688  39574.878906  38994.078125   \n2021-01-15 12:00:00+00:00  39240.449219  39506.679688  39574.878906   \n2021-01-15 13:00:00+00:00  39403.218750  39240.449219  39506.679688   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37287.253906  37245.613281  37453.195312   \n2023-11-27 14:00:00+00:00  37261.785156  37287.253906  37245.613281   \n2023-11-27 15:00:00+00:00  37400.328125  37261.785156  37287.253906   \n2023-11-27 16:00:00+00:00  37600.101562  37400.328125  37261.785156   \n2023-11-27 17:00:00+00:00  37622.535156  37600.101562  37400.328125   \n\n                                 lag_23        lag_24        lag_25  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  38494.500000  37972.750000  38406.128906   \n2021-01-15 10:00:00+00:00  38361.769531  38494.500000  37972.750000   \n2021-01-15 11:00:00+00:00  38275.000000  38361.769531  38494.500000   \n2021-01-15 12:00:00+00:00  38994.078125  38275.000000  38361.769531   \n2021-01-15 13:00:00+00:00  39574.878906  38994.078125  38275.000000   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37369.542969  37548.851562  37648.285156   \n2023-11-27 14:00:00+00:00  37453.195312  37369.542969  37548.851562   \n2023-11-27 15:00:00+00:00  37245.613281  37453.195312  37369.542969   \n2023-11-27 16:00:00+00:00  37287.253906  37245.613281  37453.195312   \n2023-11-27 17:00:00+00:00  37261.785156  37287.253906  37245.613281   \n\n                                 lag_26        lag_27        lag_28  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  38224.589844  37861.808594  37546.570312   \n2021-01-15 10:00:00+00:00  38406.128906  38224.589844  37861.808594   \n2021-01-15 11:00:00+00:00  37972.750000  38406.128906  38224.589844   \n2021-01-15 12:00:00+00:00  38494.500000  37972.750000  38406.128906   \n2021-01-15 13:00:00+00:00  38361.769531  38494.500000  37972.750000   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37712.632812  37721.714844  37784.953125   \n2023-11-27 14:00:00+00:00  37648.285156  37712.632812  37721.714844   \n2023-11-27 15:00:00+00:00  37548.851562  37648.285156  37712.632812   \n2023-11-27 16:00:00+00:00  37369.542969  37548.851562  37648.285156   \n2023-11-27 17:00:00+00:00  37453.195312  37369.542969  37548.851562   \n\n                                 lag_29        lag_30        lag_31  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  37501.000000  37037.511719  37428.949219   \n2021-01-15 10:00:00+00:00  37546.570312  37501.000000  37037.511719   \n2021-01-15 11:00:00+00:00  37861.808594  37546.570312  37501.000000   \n2021-01-15 12:00:00+00:00  38224.589844  37861.808594  37546.570312   \n2021-01-15 13:00:00+00:00  38406.128906  38224.589844  37861.808594   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37782.777344  37823.695312  37808.441406   \n2023-11-27 14:00:00+00:00  37784.953125  37782.777344  37823.695312   \n2023-11-27 15:00:00+00:00  37721.714844  37784.953125  37782.777344   \n2023-11-27 16:00:00+00:00  37712.632812  37721.714844  37784.953125   \n2023-11-27 17:00:00+00:00  37648.285156  37712.632812  37721.714844   \n\n                                 lag_32        lag_33        lag_34  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  37438.890625  37792.710938  37400.000000   \n2021-01-15 10:00:00+00:00  37428.949219  37438.890625  37792.710938   \n2021-01-15 11:00:00+00:00  37037.511719  37428.949219  37438.890625   \n2021-01-15 12:00:00+00:00  37501.000000  37037.511719  37428.949219   \n2021-01-15 13:00:00+00:00  37546.570312  37501.000000  37037.511719   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37803.851562  37790.101562  37763.789062   \n2023-11-27 14:00:00+00:00  37808.441406  37803.851562  37790.101562   \n2023-11-27 15:00:00+00:00  37823.695312  37808.441406  37803.851562   \n2023-11-27 16:00:00+00:00  37782.777344  37823.695312  37808.441406   \n2023-11-27 17:00:00+00:00  37784.953125  37782.777344  37823.695312   \n\n                                 lag_35        lag_36        lag_37  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  37177.968750  37294.968750  36210.621094   \n2021-01-15 10:00:00+00:00  37400.000000  37177.968750  37294.968750   \n2021-01-15 11:00:00+00:00  37792.710938  37400.000000  37177.968750   \n2021-01-15 12:00:00+00:00  37438.890625  37792.710938  37400.000000   \n2021-01-15 13:00:00+00:00  37428.949219  37438.890625  37792.710938   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37778.171875  37737.050781  37774.500000   \n2023-11-27 14:00:00+00:00  37763.789062  37778.171875  37737.050781   \n2023-11-27 15:00:00+00:00  37790.101562  37763.789062  37778.171875   \n2023-11-27 16:00:00+00:00  37803.851562  37790.101562  37763.789062   \n2023-11-27 17:00:00+00:00  37808.441406  37803.851562  37790.101562   \n\n                                 lag_38        lag_39        lag_40  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  35992.359375  35706.871094  34777.578125   \n2021-01-15 10:00:00+00:00  36210.621094  35992.359375  35706.871094   \n2021-01-15 11:00:00+00:00  37294.968750  36210.621094  35992.359375   \n2021-01-15 12:00:00+00:00  37177.968750  37294.968750  36210.621094   \n2021-01-15 13:00:00+00:00  37400.000000  37177.968750  37294.968750   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37806.699219  37752.734375  37828.062500   \n2023-11-27 14:00:00+00:00  37774.500000  37806.699219  37752.734375   \n2023-11-27 15:00:00+00:00  37737.050781  37774.500000  37806.699219   \n2023-11-27 16:00:00+00:00  37778.171875  37737.050781  37774.500000   \n2023-11-27 17:00:00+00:00  37763.789062  37778.171875  37737.050781   \n\n                                 lag_41        lag_42        lag_43  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  34750.000000  34646.531250  34250.078125   \n2021-01-15 10:00:00+00:00  34777.578125  34750.000000  34646.531250   \n2021-01-15 11:00:00+00:00  35706.871094  34777.578125  34750.000000   \n2021-01-15 12:00:00+00:00  35992.359375  35706.871094  34777.578125   \n2021-01-15 13:00:00+00:00  36210.621094  35992.359375  35706.871094   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37826.925781  37846.503906  37845.960938   \n2023-11-27 14:00:00+00:00  37828.062500  37826.925781  37846.503906   \n2023-11-27 15:00:00+00:00  37752.734375  37828.062500  37826.925781   \n2023-11-27 16:00:00+00:00  37806.699219  37752.734375  37828.062500   \n2023-11-27 17:00:00+00:00  37774.500000  37806.699219  37752.734375   \n\n                                 lag_44        lag_45        lag_46  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  34553.570312  34247.710938  34350.328125   \n2021-01-15 10:00:00+00:00  34250.078125  34553.570312  34247.710938   \n2021-01-15 11:00:00+00:00  34646.531250  34250.078125  34553.570312   \n2021-01-15 12:00:00+00:00  34750.000000  34646.531250  34250.078125   \n2021-01-15 13:00:00+00:00  34777.578125  34750.000000  34646.531250   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37793.910156  37769.109375  37725.953125   \n2023-11-27 14:00:00+00:00  37845.960938  37793.910156  37769.109375   \n2023-11-27 15:00:00+00:00  37846.503906  37845.960938  37793.910156   \n2023-11-27 16:00:00+00:00  37826.925781  37846.503906  37845.960938   \n2023-11-27 17:00:00+00:00  37828.062500  37826.925781  37846.503906   \n\n                                 lag_47        lag_48        lag_49  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  34906.191406  34325.000000  34521.519531   \n2021-01-15 10:00:00+00:00  34350.328125  34906.191406  34325.000000   \n2021-01-15 11:00:00+00:00  34247.710938  34350.328125  34906.191406   \n2021-01-15 12:00:00+00:00  34553.570312  34247.710938  34350.328125   \n2021-01-15 13:00:00+00:00  34250.078125  34553.570312  34247.710938   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37710.105469  37742.480469  37749.714844   \n2023-11-27 14:00:00+00:00  37725.953125  37710.105469  37742.480469   \n2023-11-27 15:00:00+00:00  37769.109375  37725.953125  37710.105469   \n2023-11-27 16:00:00+00:00  37793.910156  37769.109375  37725.953125   \n2023-11-27 17:00:00+00:00  37845.960938  37793.910156  37769.109375   \n\n                                 lag_50        lag_51        lag_52  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  34967.351562  33729.250000  33284.019531   \n2021-01-15 10:00:00+00:00  34521.519531  34967.351562  33729.250000   \n2021-01-15 11:00:00+00:00  34325.000000  34521.519531  34967.351562   \n2021-01-15 12:00:00+00:00  34906.191406  34325.000000  34521.519531   \n2021-01-15 13:00:00+00:00  34350.328125  34906.191406  34325.000000   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37676.199219  37708.832031  37767.398438   \n2023-11-27 14:00:00+00:00  37749.714844  37676.199219  37708.832031   \n2023-11-27 15:00:00+00:00  37742.480469  37749.714844  37676.199219   \n2023-11-27 16:00:00+00:00  37710.105469  37742.480469  37749.714844   \n2023-11-27 17:00:00+00:00  37725.953125  37710.105469  37742.480469   \n\n                                 lag_53        lag_54        lag_55  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  33131.640625  33770.179688  32995.460938   \n2021-01-15 10:00:00+00:00  33284.019531  33131.640625  33770.179688   \n2021-01-15 11:00:00+00:00  33729.250000  33284.019531  33131.640625   \n2021-01-15 12:00:00+00:00  34967.351562  33729.250000  33284.019531   \n2021-01-15 13:00:00+00:00  34521.519531  34967.351562  33729.250000   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37785.714844  37816.875000  37815.179688   \n2023-11-27 14:00:00+00:00  37767.398438  37785.714844  37816.875000   \n2023-11-27 15:00:00+00:00  37708.832031  37767.398438  37785.714844   \n2023-11-27 16:00:00+00:00  37676.199219  37708.832031  37767.398438   \n2023-11-27 17:00:00+00:00  37749.714844  37676.199219  37708.832031   \n\n                                 lag_56        lag_57        lag_58  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  33109.378906  32469.419922  34031.160156   \n2021-01-15 10:00:00+00:00  32995.460938  33109.378906  32469.419922   \n2021-01-15 11:00:00+00:00  33770.179688  32995.460938  33109.378906   \n2021-01-15 12:00:00+00:00  33131.640625  33770.179688  32995.460938   \n2021-01-15 13:00:00+00:00  33284.019531  33131.640625  33770.179688   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37829.914062  37812.269531  37818.269531   \n2023-11-27 14:00:00+00:00  37815.179688  37829.914062  37812.269531   \n2023-11-27 15:00:00+00:00  37816.875000  37815.179688  37829.914062   \n2023-11-27 16:00:00+00:00  37785.714844  37816.875000  37815.179688   \n2023-11-27 17:00:00+00:00  37767.398438  37785.714844  37816.875000   \n\n                                 lag_59        lag_60        lag_61  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  33720.648438  34675.550781  34282.460938   \n2021-01-15 10:00:00+00:00  34031.160156  33720.648438  34675.550781   \n2021-01-15 11:00:00+00:00  32469.419922  34031.160156  33720.648438   \n2021-01-15 12:00:00+00:00  33109.378906  32469.419922  34031.160156   \n2021-01-15 13:00:00+00:00  32995.460938  33109.378906  32469.419922   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37850.578125  37815.070312  37741.773438   \n2023-11-27 14:00:00+00:00  37818.269531  37850.578125  37815.070312   \n2023-11-27 15:00:00+00:00  37812.269531  37818.269531  37850.578125   \n2023-11-27 16:00:00+00:00  37829.914062  37812.269531  37818.269531   \n2023-11-27 17:00:00+00:00  37815.179688  37829.914062  37812.269531   \n\n                                 lag_62        lag_63        lag_64  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  34642.320312  35013.011719  34991.878906   \n2021-01-15 10:00:00+00:00  34282.460938  34642.320312  35013.011719   \n2021-01-15 11:00:00+00:00  34675.550781  34282.460938  34642.320312   \n2021-01-15 12:00:00+00:00  33720.648438  34675.550781  34282.460938   \n2021-01-15 13:00:00+00:00  34031.160156  33720.648438  34675.550781   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37740.777344  37720.859375  37857.000000   \n2023-11-27 14:00:00+00:00  37741.773438  37740.777344  37720.859375   \n2023-11-27 15:00:00+00:00  37815.070312  37741.773438  37740.777344   \n2023-11-27 16:00:00+00:00  37850.578125  37815.070312  37741.773438   \n2023-11-27 17:00:00+00:00  37818.269531  37850.578125  37815.070312   \n\n                                 lag_65        lag_66        lag_67  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  34984.000000  33831.558594  33394.210938   \n2021-01-15 10:00:00+00:00  34991.878906  34984.000000  33831.558594   \n2021-01-15 11:00:00+00:00  35013.011719  34991.878906  34984.000000   \n2021-01-15 12:00:00+00:00  34642.320312  35013.011719  34991.878906   \n2021-01-15 13:00:00+00:00  34282.460938  34642.320312  35013.011719   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37849.980469  37797.285156  37722.636719   \n2023-11-27 14:00:00+00:00  37857.000000  37849.980469  37797.285156   \n2023-11-27 15:00:00+00:00  37720.859375  37857.000000  37849.980469   \n2023-11-27 16:00:00+00:00  37740.777344  37720.859375  37857.000000   \n2023-11-27 17:00:00+00:00  37741.773438  37740.777344  37720.859375   \n\n                                 lag_68        lag_69        lag_70  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  33263.000000  35030.539062  35382.781250   \n2021-01-15 10:00:00+00:00  33394.210938  33263.000000  35030.539062   \n2021-01-15 11:00:00+00:00  33831.558594  33394.210938  33263.000000   \n2021-01-15 12:00:00+00:00  34984.000000  33831.558594  33394.210938   \n2021-01-15 13:00:00+00:00  34991.878906  34984.000000  33831.558594   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37866.453125  37746.105469  38265.125000   \n2023-11-27 14:00:00+00:00  37722.636719  37866.453125  37746.105469   \n2023-11-27 15:00:00+00:00  37797.285156  37722.636719  37866.453125   \n2023-11-27 16:00:00+00:00  37849.980469  37797.285156  37722.636719   \n2023-11-27 17:00:00+00:00  37857.000000  37849.980469  37797.285156   \n\n                                 lag_71        lag_72        lag_73  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  35032.558594  35928.031250  35772.339844   \n2021-01-15 10:00:00+00:00  35382.781250  35032.558594  35928.031250   \n2021-01-15 11:00:00+00:00  35030.539062  35382.781250  35032.558594   \n2021-01-15 12:00:00+00:00  33263.000000  35030.539062  35382.781250   \n2021-01-15 13:00:00+00:00  33394.210938  33263.000000  35030.539062   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37815.886719  37797.679688  37744.050781   \n2023-11-27 14:00:00+00:00  38265.125000  37815.886719  37797.679688   \n2023-11-27 15:00:00+00:00  37746.105469  38265.125000  37815.886719   \n2023-11-27 16:00:00+00:00  37866.453125  37746.105469  38265.125000   \n2023-11-27 17:00:00+00:00  37722.636719  37866.453125  37746.105469   \n\n                                 lag_74        lag_75        lag_76  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  36448.281250  36002.031250  35892.578125   \n2021-01-15 10:00:00+00:00  35772.339844  36448.281250  36002.031250   \n2021-01-15 11:00:00+00:00  35928.031250  35772.339844  36448.281250   \n2021-01-15 12:00:00+00:00  35032.558594  35928.031250  35772.339844   \n2021-01-15 13:00:00+00:00  35382.781250  35032.558594  35928.031250   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37756.984375  37606.589844  37627.941406   \n2023-11-27 14:00:00+00:00  37744.050781  37756.984375  37606.589844   \n2023-11-27 15:00:00+00:00  37797.679688  37744.050781  37756.984375   \n2023-11-27 16:00:00+00:00  37815.886719  37797.679688  37744.050781   \n2023-11-27 17:00:00+00:00  38265.125000  37815.886719  37797.679688   \n\n                                 lag_77        lag_78        lag_79  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  35299.988281  34964.468750  34371.000000   \n2021-01-15 10:00:00+00:00  35892.578125  35299.988281  34964.468750   \n2021-01-15 11:00:00+00:00  36002.031250  35892.578125  35299.988281   \n2021-01-15 12:00:00+00:00  36448.281250  36002.031250  35892.578125   \n2021-01-15 13:00:00+00:00  35772.339844  36448.281250  36002.031250   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37445.910156  37551.734375  37394.496094   \n2023-11-27 14:00:00+00:00  37627.941406  37445.910156  37551.734375   \n2023-11-27 15:00:00+00:00  37606.589844  37627.941406  37445.910156   \n2023-11-27 16:00:00+00:00  37756.984375  37606.589844  37627.941406   \n2023-11-27 17:00:00+00:00  37744.050781  37756.984375  37606.589844   \n\n                                 lag_80        lag_81        lag_82  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  34272.179688  34942.488281  35494.000000   \n2021-01-15 10:00:00+00:00  34371.000000  34272.179688  34942.488281   \n2021-01-15 11:00:00+00:00  34964.468750  34371.000000  34272.179688   \n2021-01-15 12:00:00+00:00  35299.988281  34964.468750  34371.000000   \n2021-01-15 13:00:00+00:00  35892.578125  35299.988281  34964.468750   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37440.757812  37361.238281  37338.574219   \n2023-11-27 14:00:00+00:00  37394.496094  37440.757812  37361.238281   \n2023-11-27 15:00:00+00:00  37551.734375  37394.496094  37440.757812   \n2023-11-27 16:00:00+00:00  37445.910156  37551.734375  37394.496094   \n2023-11-27 17:00:00+00:00  37627.941406  37445.910156  37551.734375   \n\n                                 lag_83        lag_84        lag_85  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  34409.890625  34006.250000  33373.480469   \n2021-01-15 10:00:00+00:00  35494.000000  34409.890625  34006.250000   \n2021-01-15 11:00:00+00:00  34942.488281  35494.000000  34409.890625   \n2021-01-15 12:00:00+00:00  34272.179688  34942.488281  35494.000000   \n2021-01-15 13:00:00+00:00  34371.000000  34272.179688  34942.488281   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37391.601562  37413.457031  37304.656250   \n2023-11-27 14:00:00+00:00  37338.574219  37391.601562  37413.457031   \n2023-11-27 15:00:00+00:00  37361.238281  37338.574219  37391.601562   \n2023-11-27 16:00:00+00:00  37440.757812  37361.238281  37338.574219   \n2023-11-27 17:00:00+00:00  37394.496094  37440.757812  37361.238281   \n\n                                 lag_86        lag_87        lag_88  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  32238.699219  32573.380859  32800.000000   \n2021-01-15 10:00:00+00:00  33373.480469  32238.699219  32573.380859   \n2021-01-15 11:00:00+00:00  34006.250000  33373.480469  32238.699219   \n2021-01-15 12:00:00+00:00  34409.890625  34006.250000  33373.480469   \n2021-01-15 13:00:00+00:00  35494.000000  34409.890625  34006.250000   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37301.031250  37314.050781  37261.746094   \n2023-11-27 14:00:00+00:00  37304.656250  37301.031250  37314.050781   \n2023-11-27 15:00:00+00:00  37413.457031  37304.656250  37301.031250   \n2023-11-27 16:00:00+00:00  37391.601562  37413.457031  37304.656250   \n2023-11-27 17:00:00+00:00  37338.574219  37391.601562  37413.457031   \n\n                                 lag_89        lag_90        lag_91  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  31665.929688  31511.849609  33219.339844   \n2021-01-15 10:00:00+00:00  32800.000000  31665.929688  31511.849609   \n2021-01-15 11:00:00+00:00  32573.380859  32800.000000  31665.929688   \n2021-01-15 12:00:00+00:00  32238.699219  32573.380859  32800.000000   \n2021-01-15 13:00:00+00:00  33373.480469  32238.699219  32573.380859   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37290.398438  37289.148438  37260.070312   \n2023-11-27 14:00:00+00:00  37261.746094  37290.398438  37289.148438   \n2023-11-27 15:00:00+00:00  37314.050781  37261.746094  37290.398438   \n2023-11-27 16:00:00+00:00  37301.031250  37314.050781  37261.746094   \n2023-11-27 17:00:00+00:00  37304.656250  37301.031250  37314.050781   \n\n                                 lag_92        lag_93        lag_94  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  32750.000000  34232.859375  34119.179688   \n2021-01-15 10:00:00+00:00  33219.339844  32750.000000  34232.859375   \n2021-01-15 11:00:00+00:00  31511.849609  33219.339844  32750.000000   \n2021-01-15 12:00:00+00:00  31665.929688  31511.849609  33219.339844   \n2021-01-15 13:00:00+00:00  32800.000000  31665.929688  31511.849609   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37180.660156  37099.250000  36956.265625   \n2023-11-27 14:00:00+00:00  37260.070312  37180.660156  37099.250000   \n2023-11-27 15:00:00+00:00  37289.148438  37260.070312  37180.660156   \n2023-11-27 16:00:00+00:00  37290.398438  37289.148438  37260.070312   \n2023-11-27 17:00:00+00:00  37261.746094  37290.398438  37289.148438   \n\n                                 lag_95        lag_96        lag_97  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  35359.730469  35700.000000  34860.750000   \n2021-01-15 10:00:00+00:00  34119.179688  35359.730469  35700.000000   \n2021-01-15 11:00:00+00:00  34232.859375  34119.179688  35359.730469   \n2021-01-15 12:00:00+00:00  32750.000000  34232.859375  34119.179688   \n2021-01-15 13:00:00+00:00  33219.339844  32750.000000  34232.859375   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37217.929688  37243.300781  37308.710938   \n2023-11-27 14:00:00+00:00  36956.265625  37217.929688  37243.300781   \n2023-11-27 15:00:00+00:00  37099.250000  36956.265625  37217.929688   \n2023-11-27 16:00:00+00:00  37180.660156  37099.250000  36956.265625   \n2023-11-27 17:00:00+00:00  37260.070312  37180.660156  37099.250000   \n\n                                 lag_98        lag_99       lag_100  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  35502.550781  35367.960938  36339.550781   \n2021-01-15 10:00:00+00:00  34860.750000  35502.550781  35367.960938   \n2021-01-15 11:00:00+00:00  35700.000000  34860.750000  35502.550781   \n2021-01-15 12:00:00+00:00  35359.730469  35700.000000  34860.750000   \n2021-01-15 13:00:00+00:00  34119.179688  35359.730469  35700.000000   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37331.148438  37473.097656  37511.054688   \n2023-11-27 14:00:00+00:00  37308.710938  37331.148438  37473.097656   \n2023-11-27 15:00:00+00:00  37243.300781  37308.710938  37331.148438   \n2023-11-27 16:00:00+00:00  37217.929688  37243.300781  37308.710938   \n2023-11-27 17:00:00+00:00  36956.265625  37217.929688  37243.300781   \n\n                                lag_101       lag_102       lag_103  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  37235.558594  37198.511719  38181.171875   \n2021-01-15 10:00:00+00:00  36339.550781  37235.558594  37198.511719   \n2021-01-15 11:00:00+00:00  35367.960938  36339.550781  37235.558594   \n2021-01-15 12:00:00+00:00  35502.550781  35367.960938  36339.550781   \n2021-01-15 13:00:00+00:00  34860.750000  35502.550781  35367.960938   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37500.316406  37385.480469  37295.429688   \n2023-11-27 14:00:00+00:00  37511.054688  37500.316406  37385.480469   \n2023-11-27 15:00:00+00:00  37473.097656  37511.054688  37500.316406   \n2023-11-27 16:00:00+00:00  37331.148438  37473.097656  37511.054688   \n2023-11-27 17:00:00+00:00  37308.710938  37331.148438  37473.097656   \n\n                                lag_104       lag_105       lag_106  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  38456.601562  38050.148438  37693.480469   \n2021-01-15 10:00:00+00:00  38181.171875  38456.601562  38050.148438   \n2021-01-15 11:00:00+00:00  37198.511719  38181.171875  38456.601562   \n2021-01-15 12:00:00+00:00  37235.558594  37198.511719  38181.171875   \n2021-01-15 13:00:00+00:00  36339.550781  37235.558594  37198.511719   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37346.011719  37358.363281  37322.140625   \n2023-11-27 14:00:00+00:00  37295.429688  37346.011719  37358.363281   \n2023-11-27 15:00:00+00:00  37385.480469  37295.429688  37346.011719   \n2023-11-27 16:00:00+00:00  37500.316406  37385.480469  37295.429688   \n2023-11-27 17:00:00+00:00  37511.054688  37500.316406  37385.480469   \n\n                                lag_107       lag_108       lag_109  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  37422.988281  38112.921875  38663.769531   \n2021-01-15 10:00:00+00:00  37693.480469  37422.988281  38112.921875   \n2021-01-15 11:00:00+00:00  38050.148438  37693.480469  37422.988281   \n2021-01-15 12:00:00+00:00  38456.601562  38050.148438  37693.480469   \n2021-01-15 13:00:00+00:00  38181.171875  38456.601562  38050.148438   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37356.648438  37434.425781  37345.921875   \n2023-11-27 14:00:00+00:00  37322.140625  37356.648438  37434.425781   \n2023-11-27 15:00:00+00:00  37358.363281  37322.140625  37356.648438   \n2023-11-27 16:00:00+00:00  37346.011719  37358.363281  37322.140625   \n2023-11-27 17:00:00+00:00  37295.429688  37346.011719  37358.363281   \n\n                                lag_110       lag_111       lag_112  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  39299.441406  39726.238281  39481.898438   \n2021-01-15 10:00:00+00:00  38663.769531  39299.441406  39726.238281   \n2021-01-15 11:00:00+00:00  38112.921875  38663.769531  39299.441406   \n2021-01-15 12:00:00+00:00  37422.988281  38112.921875  38663.769531   \n2021-01-15 13:00:00+00:00  37693.480469  37422.988281  38112.921875   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37429.453125  37475.507812  37618.648438   \n2023-11-27 14:00:00+00:00  37345.921875  37429.453125  37475.507812   \n2023-11-27 15:00:00+00:00  37434.425781  37345.921875  37429.453125   \n2023-11-27 16:00:00+00:00  37356.648438  37434.425781  37345.921875   \n2023-11-27 17:00:00+00:00  37322.140625  37356.648438  37434.425781   \n\n                                lag_113       lag_114       lag_115  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  39848.191406  39600.550781  39218.171875   \n2021-01-15 10:00:00+00:00  39481.898438  39848.191406  39600.550781   \n2021-01-15 11:00:00+00:00  39726.238281  39481.898438  39848.191406   \n2021-01-15 12:00:00+00:00  39299.441406  39726.238281  39481.898438   \n2021-01-15 13:00:00+00:00  38663.769531  39299.441406  39726.238281   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37638.851562  37291.343750  37203.867188   \n2023-11-27 14:00:00+00:00  37618.648438  37638.851562  37291.343750   \n2023-11-27 15:00:00+00:00  37475.507812  37618.648438  37638.851562   \n2023-11-27 16:00:00+00:00  37429.453125  37475.507812  37618.648438   \n2023-11-27 17:00:00+00:00  37345.921875  37429.453125  37475.507812   \n\n                                lag_116       lag_117       lag_118  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  40482.351562  40838.699219  40905.710938   \n2021-01-15 10:00:00+00:00  39218.171875  40482.351562  40838.699219   \n2021-01-15 11:00:00+00:00  39600.550781  39218.171875  40482.351562   \n2021-01-15 12:00:00+00:00  39848.191406  39600.550781  39218.171875   \n2021-01-15 13:00:00+00:00  39481.898438  39848.191406  39600.550781   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36589.949219  36406.671875  36492.765625   \n2023-11-27 14:00:00+00:00  37203.867188  36589.949219  36406.671875   \n2023-11-27 15:00:00+00:00  37291.343750  37203.867188  36589.949219   \n2023-11-27 16:00:00+00:00  37638.851562  37291.343750  37203.867188   \n2023-11-27 17:00:00+00:00  37618.648438  37638.851562  37291.343750   \n\n                                lag_119       lag_120       lag_121  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  41023.000000  41030.160156  40547.421875   \n2021-01-15 10:00:00+00:00  40905.710938  41023.000000  41030.160156   \n2021-01-15 11:00:00+00:00  40838.699219  40905.710938  41023.000000   \n2021-01-15 12:00:00+00:00  40482.351562  40838.699219  40905.710938   \n2021-01-15 13:00:00+00:00  39218.171875  40482.351562  40838.699219   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36404.410156  36613.843750  36570.613281   \n2023-11-27 14:00:00+00:00  36492.765625  36404.410156  36613.843750   \n2023-11-27 15:00:00+00:00  36406.671875  36492.765625  36404.410156   \n2023-11-27 16:00:00+00:00  36589.949219  36406.671875  36492.765625   \n2023-11-27 17:00:00+00:00  37203.867188  36589.949219  36406.671875   \n\n                                lag_122       lag_123       lag_124  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  40466.718750  40467.558594  41064.800781   \n2021-01-15 10:00:00+00:00  40547.421875  40466.718750  40467.558594   \n2021-01-15 11:00:00+00:00  41030.160156  40547.421875  40466.718750   \n2021-01-15 12:00:00+00:00  41023.000000  41030.160156  40547.421875   \n2021-01-15 13:00:00+00:00  40905.710938  41023.000000  41030.160156   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36447.683594  36681.859375  36652.515625   \n2023-11-27 14:00:00+00:00  36570.613281  36447.683594  36681.859375   \n2023-11-27 15:00:00+00:00  36613.843750  36570.613281  36447.683594   \n2023-11-27 16:00:00+00:00  36404.410156  36613.843750  36570.613281   \n2023-11-27 17:00:00+00:00  36492.765625  36404.410156  36613.843750   \n\n                                lag_125       lag_126       lag_127  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  41231.820312  40684.031250  40256.019531   \n2021-01-15 10:00:00+00:00  41064.800781  41231.820312  40684.031250   \n2021-01-15 11:00:00+00:00  40467.558594  41064.800781  41231.820312   \n2021-01-15 12:00:00+00:00  40466.718750  40467.558594  41064.800781   \n2021-01-15 13:00:00+00:00  40547.421875  40466.718750  40467.558594   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36548.500000  36461.550781  36525.898438   \n2023-11-27 14:00:00+00:00  36652.515625  36548.500000  36461.550781   \n2023-11-27 15:00:00+00:00  36681.859375  36652.515625  36548.500000   \n2023-11-27 16:00:00+00:00  36447.683594  36681.859375  36652.515625   \n2023-11-27 17:00:00+00:00  36570.613281  36447.683594  36681.859375   \n\n                                lag_128       lag_129       lag_130  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  40428.589844  40848.281250  40731.859375   \n2021-01-15 10:00:00+00:00  40256.019531  40428.589844  40848.281250   \n2021-01-15 11:00:00+00:00  40684.031250  40256.019531  40428.589844   \n2021-01-15 12:00:00+00:00  41231.820312  40684.031250  40256.019531   \n2021-01-15 13:00:00+00:00  41064.800781  41231.820312  40684.031250   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36372.601562  36436.886719  36374.535156   \n2023-11-27 14:00:00+00:00  36525.898438  36372.601562  36436.886719   \n2023-11-27 15:00:00+00:00  36461.550781  36525.898438  36372.601562   \n2023-11-27 16:00:00+00:00  36548.500000  36461.550781  36525.898438   \n2023-11-27 17:00:00+00:00  36652.515625  36548.500000  36461.550781   \n\n                                lag_131       lag_132       lag_133  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  40767.289062  40725.308594  40515.339844   \n2021-01-15 10:00:00+00:00  40731.859375  40767.289062  40725.308594   \n2021-01-15 11:00:00+00:00  40848.281250  40731.859375  40767.289062   \n2021-01-15 12:00:00+00:00  40428.589844  40848.281250  40731.859375   \n2021-01-15 13:00:00+00:00  40256.019531  40428.589844  40848.281250   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36302.617188  36086.960938  36115.984375   \n2023-11-27 14:00:00+00:00  36374.535156  36302.617188  36086.960938   \n2023-11-27 15:00:00+00:00  36436.886719  36374.535156  36302.617188   \n2023-11-27 16:00:00+00:00  36372.601562  36436.886719  36374.535156   \n2023-11-27 17:00:00+00:00  36525.898438  36372.601562  36436.886719   \n\n                                lag_134       lag_135       lag_136  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  40099.300781  40622.171875  40895.519531   \n2021-01-15 10:00:00+00:00  40515.339844  40099.300781  40622.171875   \n2021-01-15 11:00:00+00:00  40725.308594  40515.339844  40099.300781   \n2021-01-15 12:00:00+00:00  40767.289062  40725.308594  40515.339844   \n2021-01-15 13:00:00+00:00  40731.859375  40767.289062  40725.308594   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  35814.101562  36406.289062  36838.941406   \n2023-11-27 14:00:00+00:00  36115.984375  35814.101562  36406.289062   \n2023-11-27 15:00:00+00:00  36086.960938  36115.984375  35814.101562   \n2023-11-27 16:00:00+00:00  36302.617188  36086.960938  36115.984375   \n2023-11-27 17:00:00+00:00  36374.535156  36302.617188  36086.960938   \n\n                                lag_137       lag_138       lag_139  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  41128.300781  40912.750000  40624.960938   \n2021-01-15 10:00:00+00:00  40895.519531  41128.300781  40912.750000   \n2021-01-15 11:00:00+00:00  40622.171875  40895.519531  41128.300781   \n2021-01-15 12:00:00+00:00  40099.300781  40622.171875  40895.519531   \n2021-01-15 13:00:00+00:00  40515.339844  40099.300781  40622.171875   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36940.218750  36991.875000  36893.941406   \n2023-11-27 14:00:00+00:00  36838.941406  36940.218750  36991.875000   \n2023-11-27 15:00:00+00:00  36406.289062  36838.941406  36940.218750   \n2023-11-27 16:00:00+00:00  35814.101562  36406.289062  36838.941406   \n2023-11-27 17:00:00+00:00  36115.984375  35814.101562  36406.289062   \n\n                                lag_140       lag_141       lag_142  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  40415.031250  40343.210938  40204.171875   \n2021-01-15 10:00:00+00:00  40624.960938  40415.031250  40343.210938   \n2021-01-15 11:00:00+00:00  40912.750000  40624.960938  40415.031250   \n2021-01-15 12:00:00+00:00  41128.300781  40912.750000  40624.960938   \n2021-01-15 13:00:00+00:00  40895.519531  41128.300781  40912.750000   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36930.085938  37209.886719  37008.648438   \n2023-11-27 14:00:00+00:00  36893.941406  36930.085938  37209.886719   \n2023-11-27 15:00:00+00:00  36991.875000  36893.941406  36930.085938   \n2023-11-27 16:00:00+00:00  36940.218750  36991.875000  36893.941406   \n2023-11-27 17:00:00+00:00  36838.941406  36940.218750  36991.875000   \n\n                                lag_143       lag_144       lag_145  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  39654.949219  38989.871094  39379.238281   \n2021-01-15 10:00:00+00:00  40204.171875  39654.949219  38989.871094   \n2021-01-15 11:00:00+00:00  40343.210938  40204.171875  39654.949219   \n2021-01-15 12:00:00+00:00  40415.031250  40343.210938  40204.171875   \n2021-01-15 13:00:00+00:00  40624.960938  40415.031250  40343.210938   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36648.074219  37034.703125  37155.750000   \n2023-11-27 14:00:00+00:00  37008.648438  36648.074219  37034.703125   \n2023-11-27 15:00:00+00:00  37209.886719  37008.648438  36648.074219   \n2023-11-27 16:00:00+00:00  36930.085938  37209.886719  37008.648438   \n2023-11-27 17:00:00+00:00  36893.941406  36930.085938  37209.886719   \n\n                                lag_146       lag_147       lag_148  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  40254.210938  40261.289062  39987.441406   \n2021-01-15 10:00:00+00:00  39379.238281  40254.210938  40261.289062   \n2021-01-15 11:00:00+00:00  38989.871094  39379.238281  40254.210938   \n2021-01-15 12:00:00+00:00  39654.949219  38989.871094  39379.238281   \n2021-01-15 13:00:00+00:00  40204.171875  39654.949219  38989.871094   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37191.179688  37380.101562  37324.824219   \n2023-11-27 14:00:00+00:00  37155.750000  37191.179688  37380.101562   \n2023-11-27 15:00:00+00:00  37034.703125  37155.750000  37191.179688   \n2023-11-27 16:00:00+00:00  36648.074219  37034.703125  37155.750000   \n2023-11-27 17:00:00+00:00  37008.648438  36648.074219  37034.703125   \n\n                                lag_149       lag_150       lag_151  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  40207.601562  40305.570312  40622.871094   \n2021-01-15 10:00:00+00:00  39987.441406  40207.601562  40305.570312   \n2021-01-15 11:00:00+00:00  40261.289062  39987.441406  40207.601562   \n2021-01-15 12:00:00+00:00  40254.210938  40261.289062  39987.441406   \n2021-01-15 13:00:00+00:00  39379.238281  40254.210938  40261.289062   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37417.746094  37296.796875  37393.140625   \n2023-11-27 14:00:00+00:00  37324.824219  37417.746094  37296.796875   \n2023-11-27 15:00:00+00:00  37380.101562  37324.824219  37417.746094   \n2023-11-27 16:00:00+00:00  37191.179688  37380.101562  37324.824219   \n2023-11-27 17:00:00+00:00  37155.750000  37191.179688  37380.101562   \n\n                                lag_152       lag_153       lag_154  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  40719.000000  40050.000000  39107.738281   \n2021-01-15 10:00:00+00:00  40622.871094  40719.000000  40050.000000   \n2021-01-15 11:00:00+00:00  40305.570312  40622.871094  40719.000000   \n2021-01-15 12:00:00+00:00  40207.601562  40305.570312  40622.871094   \n2021-01-15 13:00:00+00:00  39987.441406  40207.601562  40305.570312   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37375.539062  37417.441406  37488.855469   \n2023-11-27 14:00:00+00:00  37393.140625  37375.539062  37417.441406   \n2023-11-27 15:00:00+00:00  37296.796875  37393.140625  37375.539062   \n2023-11-27 16:00:00+00:00  37417.746094  37296.796875  37393.140625   \n2023-11-27 17:00:00+00:00  37324.824219  37417.746094  37296.796875   \n\n                                lag_155       lag_156  ...       lag_163  \\\ntimestamp                                              ...                 \n2021-01-15 09:00:00+00:00  40363.320312  39914.738281  ...  41340.250000   \n2021-01-15 10:00:00+00:00  39107.738281  40363.320312  ...  41372.308594   \n2021-01-15 11:00:00+00:00  40050.000000  39107.738281  ...  41353.328125   \n2021-01-15 12:00:00+00:00  40719.000000  40050.000000  ...  41512.871094   \n2021-01-15 13:00:00+00:00  40622.871094  40719.000000  ...  40885.691406   \n...                                 ...           ...  ...           ...   \n2023-11-27 13:00:00+00:00  37456.285156  37645.496094  ...  37547.015625   \n2023-11-27 14:00:00+00:00  37488.855469  37456.285156  ...  37602.968750   \n2023-11-27 15:00:00+00:00  37417.441406  37488.855469  ...  37586.710938   \n2023-11-27 16:00:00+00:00  37375.539062  37417.441406  ...  37408.617188   \n2023-11-27 17:00:00+00:00  37393.140625  37375.539062  ...  37462.246094   \n\n                                lag_164       lag_165       lag_166  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  40749.351562  39379.000000  38655.660156   \n2021-01-15 10:00:00+00:00  41340.250000  40749.351562  39379.000000   \n2021-01-15 11:00:00+00:00  41372.308594  41340.250000  40749.351562   \n2021-01-15 12:00:00+00:00  41353.328125  41372.308594  41340.250000   \n2021-01-15 13:00:00+00:00  41512.871094  41353.328125  41372.308594   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37600.449219  37258.398438  37468.058594   \n2023-11-27 14:00:00+00:00  37547.015625  37600.449219  37258.398438   \n2023-11-27 15:00:00+00:00  37602.968750  37547.015625  37600.449219   \n2023-11-27 16:00:00+00:00  37586.710938  37602.968750  37547.015625   \n2023-11-27 17:00:00+00:00  37408.617188  37586.710938  37602.968750   \n\n                                lag_167       lag_168       lag_169  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  38953.320312  38560.898438  38009.789062   \n2021-01-15 10:00:00+00:00  38655.660156  38953.320312  38560.898438   \n2021-01-15 11:00:00+00:00  39379.000000  38655.660156  38953.320312   \n2021-01-15 12:00:00+00:00  40749.351562  39379.000000  38655.660156   \n2021-01-15 13:00:00+00:00  41340.250000  40749.351562  39379.000000   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37440.609375  37112.296875  37171.574219   \n2023-11-27 14:00:00+00:00  37468.058594  37440.609375  37112.296875   \n2023-11-27 15:00:00+00:00  37258.398438  37468.058594  37440.609375   \n2023-11-27 16:00:00+00:00  37600.449219  37258.398438  37468.058594   \n2023-11-27 17:00:00+00:00  37547.015625  37600.449219  37258.398438   \n\n                                lag_170       lag_171       lag_172  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  38723.480469  38404.460938  37226.308594   \n2021-01-15 10:00:00+00:00  38009.789062  38723.480469  38404.460938   \n2021-01-15 11:00:00+00:00  38560.898438  38009.789062  38723.480469   \n2021-01-15 12:00:00+00:00  38953.320312  38560.898438  38009.789062   \n2021-01-15 13:00:00+00:00  38655.660156  38953.320312  38560.898438   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37190.757812  37302.925781  37217.878906   \n2023-11-27 14:00:00+00:00  37171.574219  37190.757812  37302.925781   \n2023-11-27 15:00:00+00:00  37112.296875  37171.574219  37190.757812   \n2023-11-27 16:00:00+00:00  37440.609375  37112.296875  37171.574219   \n2023-11-27 17:00:00+00:00  37468.058594  37440.609375  37112.296875   \n\n                                lag_173       lag_174       lag_175  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  38701.941406  38840.738281  39479.781250   \n2021-01-15 10:00:00+00:00  37226.308594  38701.941406  38840.738281   \n2021-01-15 11:00:00+00:00  38404.460938  37226.308594  38701.941406   \n2021-01-15 12:00:00+00:00  38723.480469  38404.460938  37226.308594   \n2021-01-15 13:00:00+00:00  38009.789062  38723.480469  38404.460938   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37148.933594  37253.976562  37204.277344   \n2023-11-27 14:00:00+00:00  37217.878906  37148.933594  37253.976562   \n2023-11-27 15:00:00+00:00  37302.925781  37217.878906  37148.933594   \n2023-11-27 16:00:00+00:00  37190.757812  37302.925781  37217.878906   \n2023-11-27 17:00:00+00:00  37171.574219  37190.757812  37302.925781   \n\n                                lag_176       lag_177       lag_178  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  39698.871094  39732.289062  39141.980469   \n2021-01-15 10:00:00+00:00  39479.781250  39698.871094  39732.289062   \n2021-01-15 11:00:00+00:00  38840.738281  39479.781250  39698.871094   \n2021-01-15 12:00:00+00:00  38701.941406  38840.738281  39479.781250   \n2021-01-15 13:00:00+00:00  37226.308594  38701.941406  38840.738281   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37157.253906  37220.949219  37131.574219   \n2023-11-27 14:00:00+00:00  37204.277344  37157.253906  37220.949219   \n2023-11-27 15:00:00+00:00  37253.976562  37204.277344  37157.253906   \n2023-11-27 16:00:00+00:00  37148.933594  37253.976562  37204.277344   \n2023-11-27 17:00:00+00:00  37217.878906  37148.933594  37253.976562   \n\n                                lag_179       lag_180       lag_181  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  39078.390625  38934.351562  39771.328125   \n2021-01-15 10:00:00+00:00  39141.980469  39078.390625  38934.351562   \n2021-01-15 11:00:00+00:00  39732.289062  39141.980469  39078.390625   \n2021-01-15 12:00:00+00:00  39698.871094  39732.289062  39141.980469   \n2021-01-15 13:00:00+00:00  39479.781250  39698.871094  39732.289062   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37334.664062  37317.066406  37306.523438   \n2023-11-27 14:00:00+00:00  37131.574219  37334.664062  37317.066406   \n2023-11-27 15:00:00+00:00  37220.949219  37131.574219  37334.664062   \n2023-11-27 16:00:00+00:00  37157.253906  37220.949219  37131.574219   \n2023-11-27 17:00:00+00:00  37204.277344  37157.253906  37220.949219   \n\n                                lag_182       lag_183       lag_184  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  39182.171875  39024.210938  38503.449219   \n2021-01-15 10:00:00+00:00  39771.328125  39182.171875  39024.210938   \n2021-01-15 11:00:00+00:00  38934.351562  39771.328125  39182.171875   \n2021-01-15 12:00:00+00:00  39078.390625  38934.351562  39771.328125   \n2021-01-15 13:00:00+00:00  39141.980469  39078.390625  38934.351562   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37387.921875  36992.503906  37009.546875   \n2023-11-27 14:00:00+00:00  37306.523438  37387.921875  36992.503906   \n2023-11-27 15:00:00+00:00  37317.066406  37306.523438  37387.921875   \n2023-11-27 16:00:00+00:00  37334.664062  37317.066406  37306.523438   \n2023-11-27 17:00:00+00:00  37131.574219  37334.664062  37317.066406   \n\n                                lag_185       lag_186       lag_187  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  38227.988281  38015.328125  37888.011719   \n2021-01-15 10:00:00+00:00  38503.449219  38227.988281  38015.328125   \n2021-01-15 11:00:00+00:00  39024.210938  38503.449219  38227.988281   \n2021-01-15 12:00:00+00:00  39182.171875  39024.210938  38503.449219   \n2021-01-15 13:00:00+00:00  39771.328125  39182.171875  39024.210938   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36948.828125  36965.484375  36855.968750   \n2023-11-27 14:00:00+00:00  37009.546875  36948.828125  36965.484375   \n2023-11-27 15:00:00+00:00  36992.503906  37009.546875  36948.828125   \n2023-11-27 16:00:00+00:00  37387.921875  36992.503906  37009.546875   \n2023-11-27 17:00:00+00:00  37306.523438  37387.921875  36992.503906   \n\n                                lag_188       lag_189       lag_190  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  37089.039062  37529.988281  37243.328125   \n2021-01-15 10:00:00+00:00  37888.011719  37089.039062  37529.988281   \n2021-01-15 11:00:00+00:00  38015.328125  37888.011719  37089.039062   \n2021-01-15 12:00:00+00:00  38227.988281  38015.328125  37888.011719   \n2021-01-15 13:00:00+00:00  38503.449219  38227.988281  38015.328125   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36609.250000  36581.687500  36577.316406   \n2023-11-27 14:00:00+00:00  36855.968750  36609.250000  36581.687500   \n2023-11-27 15:00:00+00:00  36965.484375  36855.968750  36609.250000   \n2023-11-27 16:00:00+00:00  36948.828125  36965.484375  36855.968750   \n2023-11-27 17:00:00+00:00  37009.546875  36948.828125  36965.484375   \n\n                                lag_191       lag_192       lag_193  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  36922.960938  37228.820312  37485.269531   \n2021-01-15 10:00:00+00:00  37243.328125  36922.960938  37228.820312   \n2021-01-15 11:00:00+00:00  37529.988281  37243.328125  36922.960938   \n2021-01-15 12:00:00+00:00  37089.039062  37529.988281  37243.328125   \n2021-01-15 13:00:00+00:00  37888.011719  37089.039062  37529.988281   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36460.593750  36507.871094  36514.593750   \n2023-11-27 14:00:00+00:00  36577.316406  36460.593750  36507.871094   \n2023-11-27 15:00:00+00:00  36581.687500  36577.316406  36460.593750   \n2023-11-27 16:00:00+00:00  36609.250000  36581.687500  36577.316406   \n2023-11-27 17:00:00+00:00  36855.968750  36609.250000  36581.687500   \n\n                                lag_194       lag_195       lag_196  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  37559.421875  37560.300781  37082.941406   \n2021-01-15 10:00:00+00:00  37485.269531  37559.421875  37560.300781   \n2021-01-15 11:00:00+00:00  37228.820312  37485.269531  37559.421875   \n2021-01-15 12:00:00+00:00  36922.960938  37228.820312  37485.269531   \n2021-01-15 13:00:00+00:00  37243.328125  36922.960938  37228.820312   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36482.050781  36567.250000  36656.121094   \n2023-11-27 14:00:00+00:00  36514.593750  36482.050781  36567.250000   \n2023-11-27 15:00:00+00:00  36507.871094  36514.593750  36482.050781   \n2023-11-27 16:00:00+00:00  36460.593750  36507.871094  36514.593750   \n2023-11-27 17:00:00+00:00  36577.316406  36460.593750  36507.871094   \n\n                                lag_197       lag_198       lag_199  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  36971.781250  37223.078125  36856.980469   \n2021-01-15 10:00:00+00:00  37082.941406  36971.781250  37223.078125   \n2021-01-15 11:00:00+00:00  37560.300781  37082.941406  36971.781250   \n2021-01-15 12:00:00+00:00  37559.421875  37560.300781  37082.941406   \n2021-01-15 13:00:00+00:00  37485.269531  37559.421875  37560.300781   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36644.832031  36628.085938  36637.300781   \n2023-11-27 14:00:00+00:00  36656.121094  36644.832031  36628.085938   \n2023-11-27 15:00:00+00:00  36567.250000  36656.121094  36644.832031   \n2023-11-27 16:00:00+00:00  36482.050781  36567.250000  36656.121094   \n2023-11-27 17:00:00+00:00  36514.593750  36482.050781  36567.250000   \n\n                                lag_200       lag_201       lag_202  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  36084.148438  35960.839844  36133.750000   \n2021-01-15 10:00:00+00:00  36856.980469  36084.148438  35960.839844   \n2021-01-15 11:00:00+00:00  37223.078125  36856.980469  36084.148438   \n2021-01-15 12:00:00+00:00  36971.781250  37223.078125  36856.980469   \n2021-01-15 13:00:00+00:00  37082.941406  36971.781250  37223.078125   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36534.128906  36567.628906  36554.750000   \n2023-11-27 14:00:00+00:00  36637.300781  36534.128906  36567.628906   \n2023-11-27 15:00:00+00:00  36628.085938  36637.300781  36534.128906   \n2023-11-27 16:00:00+00:00  36644.832031  36628.085938  36637.300781   \n2023-11-27 17:00:00+00:00  36656.121094  36644.832031  36628.085938   \n\n                                lag_203       lag_204       lag_205  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  34920.789062  35329.281250  35000.000000   \n2021-01-15 10:00:00+00:00  36133.750000  34920.789062  35329.281250   \n2021-01-15 11:00:00+00:00  35960.839844  36133.750000  34920.789062   \n2021-01-15 12:00:00+00:00  36084.148438  35960.839844  36133.750000   \n2021-01-15 13:00:00+00:00  36856.980469  36084.148438  35960.839844   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36487.917969  36515.101562  36492.691406   \n2023-11-27 14:00:00+00:00  36554.750000  36487.917969  36515.101562   \n2023-11-27 15:00:00+00:00  36567.628906  36554.750000  36487.917969   \n2023-11-27 16:00:00+00:00  36534.128906  36567.628906  36554.750000   \n2023-11-27 17:00:00+00:00  36637.300781  36534.128906  36567.628906   \n\n                                lag_206       lag_207       lag_208  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  34700.441406  34670.128906  34285.750000   \n2021-01-15 10:00:00+00:00  35000.000000  34700.441406  34670.128906   \n2021-01-15 11:00:00+00:00  35329.281250  35000.000000  34700.441406   \n2021-01-15 12:00:00+00:00  34920.789062  35329.281250  35000.000000   \n2021-01-15 13:00:00+00:00  36133.750000  34920.789062  35329.281250   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36592.660156  36522.640625  36569.359375   \n2023-11-27 14:00:00+00:00  36492.691406  36592.660156  36522.640625   \n2023-11-27 15:00:00+00:00  36515.101562  36492.691406  36592.660156   \n2023-11-27 16:00:00+00:00  36487.917969  36515.101562  36492.691406   \n2023-11-27 17:00:00+00:00  36554.750000  36487.917969  36515.101562   \n\n                                lag_209       lag_210       lag_211  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  34620.218750  35027.871094  35013.550781   \n2021-01-15 10:00:00+00:00  34285.750000  34620.218750  35027.871094   \n2021-01-15 11:00:00+00:00  34670.128906  34285.750000  34620.218750   \n2021-01-15 12:00:00+00:00  34700.441406  34670.128906  34285.750000   \n2021-01-15 13:00:00+00:00  35000.000000  34700.441406  34670.128906   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36625.449219  36703.609375  36749.742188   \n2023-11-27 14:00:00+00:00  36569.359375  36625.449219  36703.609375   \n2023-11-27 15:00:00+00:00  36522.640625  36569.359375  36625.449219   \n2023-11-27 16:00:00+00:00  36592.660156  36522.640625  36569.359375   \n2023-11-27 17:00:00+00:00  36492.691406  36592.660156  36522.640625   \n\n                                lag_212       lag_213       lag_214  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  34526.511719  33995.511719  34641.738281   \n2021-01-15 10:00:00+00:00  35013.550781  34526.511719  33995.511719   \n2021-01-15 11:00:00+00:00  35027.871094  35013.550781  34526.511719   \n2021-01-15 12:00:00+00:00  34620.218750  35027.871094  35013.550781   \n2021-01-15 13:00:00+00:00  34285.750000  34620.218750  35027.871094   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36648.488281  36695.476562  36674.894531   \n2023-11-27 14:00:00+00:00  36749.742188  36648.488281  36695.476562   \n2023-11-27 15:00:00+00:00  36703.609375  36749.742188  36648.488281   \n2023-11-27 16:00:00+00:00  36625.449219  36703.609375  36749.742188   \n2023-11-27 17:00:00+00:00  36569.359375  36625.449219  36703.609375   \n\n                                lag_215       lag_216       lag_217  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  34768.910156  35257.191406  35063.000000   \n2021-01-15 10:00:00+00:00  34641.738281  34768.910156  35257.191406   \n2021-01-15 11:00:00+00:00  33995.511719  34641.738281  34768.910156   \n2021-01-15 12:00:00+00:00  34526.511719  33995.511719  34641.738281   \n2021-01-15 13:00:00+00:00  35013.550781  34526.511719  33995.511719   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36521.394531  36454.929688  36478.910156   \n2023-11-27 14:00:00+00:00  36674.894531  36521.394531  36454.929688   \n2023-11-27 15:00:00+00:00  36695.476562  36674.894531  36521.394531   \n2023-11-27 16:00:00+00:00  36648.488281  36695.476562  36674.894531   \n2023-11-27 17:00:00+00:00  36749.742188  36648.488281  36695.476562   \n\n                                lag_218       lag_219       lag_220  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  35455.339844  34192.351562  33978.000000   \n2021-01-15 10:00:00+00:00  35063.000000  35455.339844  34192.351562   \n2021-01-15 11:00:00+00:00  35257.191406  35063.000000  35455.339844   \n2021-01-15 12:00:00+00:00  34768.910156  35257.191406  35063.000000   \n2021-01-15 13:00:00+00:00  34641.738281  34768.910156  35257.191406   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36422.441406  36480.339844  36413.179688   \n2023-11-27 14:00:00+00:00  36478.910156  36422.441406  36480.339844   \n2023-11-27 15:00:00+00:00  36454.929688  36478.910156  36422.441406   \n2023-11-27 16:00:00+00:00  36521.394531  36454.929688  36478.910156   \n2023-11-27 17:00:00+00:00  36674.894531  36521.394531  36454.929688   \n\n                                lag_221       lag_222       lag_223  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  33888.429688  33976.468750  34036.078125   \n2021-01-15 10:00:00+00:00  33978.000000  33888.429688  33976.468750   \n2021-01-15 11:00:00+00:00  34192.351562  33978.000000  33888.429688   \n2021-01-15 12:00:00+00:00  35455.339844  34192.351562  33978.000000   \n2021-01-15 13:00:00+00:00  35063.000000  35455.339844  34192.351562   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36468.468750  36406.851562  36278.894531   \n2023-11-27 14:00:00+00:00  36413.179688  36468.468750  36406.851562   \n2023-11-27 15:00:00+00:00  36480.339844  36413.179688  36468.468750   \n2023-11-27 16:00:00+00:00  36422.441406  36480.339844  36413.179688   \n2023-11-27 17:00:00+00:00  36478.910156  36422.441406  36480.339844   \n\n                                lag_224       lag_225       lag_226  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  34313.199219  33810.078125  34064.121094   \n2021-01-15 10:00:00+00:00  34036.078125  34313.199219  33810.078125   \n2021-01-15 11:00:00+00:00  33976.468750  34036.078125  34313.199219   \n2021-01-15 12:00:00+00:00  33888.429688  33976.468750  34036.078125   \n2021-01-15 13:00:00+00:00  33978.000000  33888.429688  33976.468750   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36360.625000  36409.949219  36390.769531   \n2023-11-27 14:00:00+00:00  36278.894531  36360.625000  36409.949219   \n2023-11-27 15:00:00+00:00  36406.851562  36278.894531  36360.625000   \n2023-11-27 16:00:00+00:00  36468.468750  36406.851562  36278.894531   \n2023-11-27 17:00:00+00:00  36413.179688  36468.468750  36406.851562   \n\n                                lag_227       lag_228       lag_229  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  33810.898438  33459.019531  32795.199219   \n2021-01-15 10:00:00+00:00  34064.121094  33810.898438  33459.019531   \n2021-01-15 11:00:00+00:00  33810.078125  34064.121094  33810.898438   \n2021-01-15 12:00:00+00:00  34313.199219  33810.078125  34064.121094   \n2021-01-15 13:00:00+00:00  34036.078125  34313.199219  33810.078125   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36428.011719  36423.769531  36418.550781   \n2023-11-27 14:00:00+00:00  36390.769531  36428.011719  36423.769531   \n2023-11-27 15:00:00+00:00  36409.949219  36390.769531  36428.011719   \n2023-11-27 16:00:00+00:00  36360.625000  36409.949219  36390.769531   \n2023-11-27 17:00:00+00:00  36278.894531  36360.625000  36409.949219   \n\n                                lag_230       lag_231       lag_232  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  32501.630859  32158.669922  32375.900391   \n2021-01-15 10:00:00+00:00  32795.199219  32501.630859  32158.669922   \n2021-01-15 11:00:00+00:00  33459.019531  32795.199219  32501.630859   \n2021-01-15 12:00:00+00:00  33810.898438  33459.019531  32795.199219   \n2021-01-15 13:00:00+00:00  34064.121094  33810.898438  33459.019531   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36629.398438  36552.156250  36417.292969   \n2023-11-27 14:00:00+00:00  36418.550781  36629.398438  36552.156250   \n2023-11-27 15:00:00+00:00  36423.769531  36418.550781  36629.398438   \n2023-11-27 16:00:00+00:00  36428.011719  36423.769531  36418.550781   \n2023-11-27 17:00:00+00:00  36390.769531  36428.011719  36423.769531   \n\n                                lag_233       lag_234       lag_235  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  31510.330078  31634.000000  31815.189453   \n2021-01-15 10:00:00+00:00  32375.900391  31510.330078  31634.000000   \n2021-01-15 11:00:00+00:00  32158.669922  32375.900391  31510.330078   \n2021-01-15 12:00:00+00:00  32501.630859  32158.669922  32375.900391   \n2021-01-15 13:00:00+00:00  32795.199219  32501.630859  32158.669922   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36492.996094  36323.144531  36485.074219   \n2023-11-27 14:00:00+00:00  36417.292969  36492.996094  36323.144531   \n2023-11-27 15:00:00+00:00  36552.156250  36417.292969  36492.996094   \n2023-11-27 16:00:00+00:00  36629.398438  36552.156250  36417.292969   \n2023-11-27 17:00:00+00:00  36418.550781  36629.398438  36552.156250   \n\n                                lag_236       lag_237       lag_238  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  31360.509766  31592.599609  31217.900391   \n2021-01-15 10:00:00+00:00  31815.189453  31360.509766  31592.599609   \n2021-01-15 11:00:00+00:00  31634.000000  31815.189453  31360.509766   \n2021-01-15 12:00:00+00:00  31510.330078  31634.000000  31815.189453   \n2021-01-15 13:00:00+00:00  32375.900391  31510.330078  31634.000000   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36498.621094  35990.039062  36042.480469   \n2023-11-27 14:00:00+00:00  36485.074219  36498.621094  35990.039062   \n2023-11-27 15:00:00+00:00  36323.144531  36485.074219  36498.621094   \n2023-11-27 16:00:00+00:00  36492.996094  36323.144531  36485.074219   \n2023-11-27 17:00:00+00:00  36417.292969  36492.996094  36323.144531   \n\n                                lag_239       lag_240       lag_241  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  30816.070312  31154.589844  30441.570312   \n2021-01-15 10:00:00+00:00  31217.900391  30816.070312  31154.589844   \n2021-01-15 11:00:00+00:00  31592.599609  31217.900391  30816.070312   \n2021-01-15 12:00:00+00:00  31360.509766  31592.599609  31217.900391   \n2021-01-15 13:00:00+00:00  31815.189453  31360.509766  31592.599609   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36015.324219  36352.300781  36528.421875   \n2023-11-27 14:00:00+00:00  36042.480469  36015.324219  36352.300781   \n2023-11-27 15:00:00+00:00  35990.039062  36042.480469  36015.324219   \n2023-11-27 16:00:00+00:00  36498.621094  35990.039062  36042.480469   \n2023-11-27 17:00:00+00:00  36485.074219  36498.621094  35990.039062   \n\n                                lag_242       lag_243       lag_244  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  30879.560547  31201.310547  32354.060547   \n2021-01-15 10:00:00+00:00  30441.570312  30879.560547  31201.310547   \n2021-01-15 11:00:00+00:00  31154.589844  30441.570312  30879.560547   \n2021-01-15 12:00:00+00:00  30816.070312  31154.589844  30441.570312   \n2021-01-15 13:00:00+00:00  31217.900391  30816.070312  31154.589844   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36398.250000  36374.000000  36289.562500   \n2023-11-27 14:00:00+00:00  36528.421875  36398.250000  36374.000000   \n2023-11-27 15:00:00+00:00  36352.300781  36528.421875  36398.250000   \n2023-11-27 16:00:00+00:00  36015.324219  36352.300781  36528.421875   \n2023-11-27 17:00:00+00:00  36042.480469  36015.324219  36352.300781   \n\n                                lag_245       lag_246       lag_247  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  32823.960938  32450.119141  32025.230469   \n2021-01-15 10:00:00+00:00  32354.060547  32823.960938  32450.119141   \n2021-01-15 11:00:00+00:00  31201.310547  32354.060547  32823.960938   \n2021-01-15 12:00:00+00:00  30879.560547  31201.310547  32354.060547   \n2021-01-15 13:00:00+00:00  30441.570312  30879.560547  31201.310547   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36401.597656  36252.414062  36376.671875   \n2023-11-27 14:00:00+00:00  36289.562500  36401.597656  36252.414062   \n2023-11-27 15:00:00+00:00  36374.000000  36289.562500  36401.597656   \n2023-11-27 16:00:00+00:00  36398.250000  36374.000000  36289.562500   \n2023-11-27 17:00:00+00:00  36528.421875  36398.250000  36374.000000   \n\n                                lag_248       lag_249       lag_250  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  31364.589844  31029.650391  31287.410156   \n2021-01-15 10:00:00+00:00  32025.230469  31364.589844  31029.650391   \n2021-01-15 11:00:00+00:00  32450.119141  32025.230469  31364.589844   \n2021-01-15 12:00:00+00:00  32823.960938  32450.119141  32025.230469   \n2021-01-15 13:00:00+00:00  32354.060547  32823.960938  32450.119141   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36345.585938  36441.394531  36496.632812   \n2023-11-27 14:00:00+00:00  36376.671875  36345.585938  36441.394531   \n2023-11-27 15:00:00+00:00  36252.414062  36376.671875  36345.585938   \n2023-11-27 16:00:00+00:00  36401.597656  36252.414062  36376.671875   \n2023-11-27 17:00:00+00:00  36289.562500  36401.597656  36252.414062   \n\n                                lag_251       lag_252       lag_253  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  31667.720703  31184.980469  31015.689453   \n2021-01-15 10:00:00+00:00  31287.410156  31667.720703  31184.980469   \n2021-01-15 11:00:00+00:00  31029.650391  31287.410156  31667.720703   \n2021-01-15 12:00:00+00:00  31364.589844  31029.650391  31287.410156   \n2021-01-15 13:00:00+00:00  32025.230469  31364.589844  31029.650391   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36453.898438  36556.718750  36367.058594   \n2023-11-27 14:00:00+00:00  36496.632812  36453.898438  36556.718750   \n2023-11-27 15:00:00+00:00  36441.394531  36496.632812  36453.898438   \n2023-11-27 16:00:00+00:00  36345.585938  36441.394531  36496.632812   \n2023-11-27 17:00:00+00:00  36376.671875  36345.585938  36441.394531   \n\n                                lag_254       lag_255       lag_256  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  31321.000000  31770.460938  32137.560547   \n2021-01-15 10:00:00+00:00  31015.689453  31321.000000  31770.460938   \n2021-01-15 11:00:00+00:00  31184.980469  31015.689453  31321.000000   \n2021-01-15 12:00:00+00:00  31667.720703  31184.980469  31015.689453   \n2021-01-15 13:00:00+00:00  31287.410156  31667.720703  31184.980469   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36168.449219  36226.828125  35963.859375   \n2023-11-27 14:00:00+00:00  36367.058594  36168.449219  36226.828125   \n2023-11-27 15:00:00+00:00  36556.718750  36367.058594  36168.449219   \n2023-11-27 16:00:00+00:00  36453.898438  36556.718750  36367.058594   \n2023-11-27 17:00:00+00:00  36496.632812  36453.898438  36556.718750   \n\n                                lag_257       lag_258       lag_259  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  31418.369141  31239.310547  30755.500000   \n2021-01-15 10:00:00+00:00  32137.560547  31418.369141  31239.310547   \n2021-01-15 11:00:00+00:00  31770.460938  32137.560547  31418.369141   \n2021-01-15 12:00:00+00:00  31321.000000  31770.460938  32137.560547   \n2021-01-15 13:00:00+00:00  31015.689453  31321.000000  31770.460938   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  35960.750000  35921.035156  36250.046875   \n2023-11-27 14:00:00+00:00  35963.859375  35960.750000  35921.035156   \n2023-11-27 15:00:00+00:00  36226.828125  35963.859375  35960.750000   \n2023-11-27 16:00:00+00:00  36168.449219  36226.828125  35963.859375   \n2023-11-27 17:00:00+00:00  36367.058594  36168.449219  36226.828125   \n\n                                lag_260       lag_261       lag_262  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  30290.429688  31013.269531  32041.269531   \n2021-01-15 10:00:00+00:00  30755.500000  30290.429688  31013.269531   \n2021-01-15 11:00:00+00:00  31239.310547  30755.500000  30290.429688   \n2021-01-15 12:00:00+00:00  31418.369141  31239.310547  30755.500000   \n2021-01-15 13:00:00+00:00  32137.560547  31418.369141  31239.310547   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36317.250000  36594.953125  36429.351562   \n2023-11-27 14:00:00+00:00  36250.046875  36317.250000  36594.953125   \n2023-11-27 15:00:00+00:00  35921.035156  36250.046875  36317.250000   \n2023-11-27 16:00:00+00:00  35960.750000  35921.035156  36250.046875   \n2023-11-27 17:00:00+00:00  35963.859375  35960.750000  35921.035156   \n\n                                lag_263       lag_264       lag_265  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  32105.070312  33260.808594  32908.019531   \n2021-01-15 10:00:00+00:00  32041.269531  32105.070312  33260.808594   \n2021-01-15 11:00:00+00:00  31013.269531  32041.269531  32105.070312   \n2021-01-15 12:00:00+00:00  30290.429688  31013.269531  32041.269531   \n2021-01-15 13:00:00+00:00  30755.500000  30290.429688  31013.269531   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36761.156250  36789.429688  37136.937500   \n2023-11-27 14:00:00+00:00  36429.351562  36761.156250  36789.429688   \n2023-11-27 15:00:00+00:00  36594.953125  36429.351562  36761.156250   \n2023-11-27 16:00:00+00:00  36317.250000  36594.953125  36429.351562   \n2023-11-27 17:00:00+00:00  36250.046875  36317.250000  36594.953125   \n\n                                lag_266       lag_267       lag_268  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  33317.148438  33588.101562  33493.031250   \n2021-01-15 10:00:00+00:00  32908.019531  33317.148438  33588.101562   \n2021-01-15 11:00:00+00:00  33260.808594  32908.019531  33317.148438   \n2021-01-15 12:00:00+00:00  32105.070312  33260.808594  32908.019531   \n2021-01-15 13:00:00+00:00  32041.269531  32105.070312  33260.808594   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37204.390625  37303.949219  37367.195312   \n2023-11-27 14:00:00+00:00  37136.937500  37204.390625  37303.949219   \n2023-11-27 15:00:00+00:00  36789.429688  37136.937500  37204.390625   \n2023-11-27 16:00:00+00:00  36761.156250  36789.429688  37136.937500   \n2023-11-27 17:00:00+00:00  36429.351562  36761.156250  36789.429688   \n\n                                lag_269       lag_270       lag_271  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  33229.269531  32882.949219  33111.988281   \n2021-01-15 10:00:00+00:00  33493.031250  33229.269531  32882.949219   \n2021-01-15 11:00:00+00:00  33588.101562  33493.031250  33229.269531   \n2021-01-15 12:00:00+00:00  33317.148438  33588.101562  33493.031250   \n2021-01-15 13:00:00+00:00  32908.019531  33317.148438  33588.101562   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37328.851562  37458.226562  37568.496094   \n2023-11-27 14:00:00+00:00  37367.195312  37328.851562  37458.226562   \n2023-11-27 15:00:00+00:00  37303.949219  37367.195312  37328.851562   \n2023-11-27 16:00:00+00:00  37204.390625  37303.949219  37367.195312   \n2023-11-27 17:00:00+00:00  37136.937500  37204.390625  37303.949219   \n\n                                lag_272       lag_273       lag_274  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  33148.109375  33584.960938  33747.281250   \n2021-01-15 10:00:00+00:00  33111.988281  33148.109375  33584.960938   \n2021-01-15 11:00:00+00:00  32882.949219  33111.988281  33148.109375   \n2021-01-15 12:00:00+00:00  33229.269531  32882.949219  33111.988281   \n2021-01-15 13:00:00+00:00  33493.031250  33229.269531  32882.949219   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37466.882812  37455.558594  37473.175781   \n2023-11-27 14:00:00+00:00  37568.496094  37466.882812  37455.558594   \n2023-11-27 15:00:00+00:00  37458.226562  37568.496094  37466.882812   \n2023-11-27 16:00:00+00:00  37328.851562  37458.226562  37568.496094   \n2023-11-27 17:00:00+00:00  37367.195312  37328.851562  37458.226562   \n\n                                lag_275       lag_276       lag_277  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  32777.250000  32792.761719  32902.011719   \n2021-01-15 10:00:00+00:00  33747.281250  32777.250000  32792.761719   \n2021-01-15 11:00:00+00:00  33584.960938  33747.281250  32777.250000   \n2021-01-15 12:00:00+00:00  33148.109375  33584.960938  33747.281250   \n2021-01-15 13:00:00+00:00  33111.988281  33148.109375  33584.960938   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37524.074219  37448.699219  37785.699219   \n2023-11-27 14:00:00+00:00  37473.175781  37524.074219  37448.699219   \n2023-11-27 15:00:00+00:00  37455.558594  37473.175781  37524.074219   \n2023-11-27 16:00:00+00:00  37466.882812  37455.558594  37473.175781   \n2023-11-27 17:00:00+00:00  37568.496094  37466.882812  37455.558594   \n\n                                lag_278       lag_279       lag_280  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  32711.720703  33561.890625  33893.031250   \n2021-01-15 10:00:00+00:00  32902.011719  32711.720703  33561.890625   \n2021-01-15 11:00:00+00:00  32792.761719  32902.011719  32711.720703   \n2021-01-15 12:00:00+00:00  32777.250000  32792.761719  32902.011719   \n2021-01-15 13:00:00+00:00  33747.281250  32777.250000  32792.761719   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37900.609375  37722.441406  37647.261719   \n2023-11-27 14:00:00+00:00  37785.699219  37900.609375  37722.441406   \n2023-11-27 15:00:00+00:00  37448.699219  37785.699219  37900.609375   \n2023-11-27 16:00:00+00:00  37524.074219  37448.699219  37785.699219   \n2023-11-27 17:00:00+00:00  37473.175781  37524.074219  37448.699219   \n\n                                lag_281       lag_282       lag_283  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  33900.000000  34128.359375  34393.289062   \n2021-01-15 10:00:00+00:00  33893.031250  33900.000000  34128.359375   \n2021-01-15 11:00:00+00:00  33561.890625  33893.031250  33900.000000   \n2021-01-15 12:00:00+00:00  32711.720703  33561.890625  33893.031250   \n2021-01-15 13:00:00+00:00  32902.011719  32711.720703  33561.890625   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  37569.449219  37513.953125  37388.851562   \n2023-11-27 14:00:00+00:00  37647.261719  37569.449219  37513.953125   \n2023-11-27 15:00:00+00:00  37722.441406  37647.261719  37569.449219   \n2023-11-27 16:00:00+00:00  37900.609375  37722.441406  37647.261719   \n2023-11-27 17:00:00+00:00  37785.699219  37900.609375  37722.441406   \n\n                                lag_284       lag_285       lag_286  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  33909.988281  34207.218750  34398.449219   \n2021-01-15 10:00:00+00:00  34393.289062  33909.988281  34207.218750   \n2021-01-15 11:00:00+00:00  34128.359375  34393.289062  33909.988281   \n2021-01-15 12:00:00+00:00  33900.000000  34128.359375  34393.289062   \n2021-01-15 13:00:00+00:00  33893.031250  33900.000000  34128.359375   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36500.656250  36385.816406  36398.691406   \n2023-11-27 14:00:00+00:00  37388.851562  36500.656250  36385.816406   \n2023-11-27 15:00:00+00:00  37513.953125  37388.851562  36500.656250   \n2023-11-27 16:00:00+00:00  37569.449219  37513.953125  37388.851562   \n2023-11-27 17:00:00+00:00  37647.261719  37569.449219  37513.953125   \n\n                                lag_287       lag_288       lag_289  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  34459.031250  33980.738281  33769.519531   \n2021-01-15 10:00:00+00:00  34398.449219  34459.031250  33980.738281   \n2021-01-15 11:00:00+00:00  34207.218750  34398.449219  34459.031250   \n2021-01-15 12:00:00+00:00  33909.988281  34207.218750  34398.449219   \n2021-01-15 13:00:00+00:00  34393.289062  33909.988281  34207.218750   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36132.300781  36099.589844  36225.386719   \n2023-11-27 14:00:00+00:00  36398.691406  36132.300781  36099.589844   \n2023-11-27 15:00:00+00:00  36385.816406  36398.691406  36132.300781   \n2023-11-27 16:00:00+00:00  36500.656250  36385.816406  36398.691406   \n2023-11-27 17:00:00+00:00  37388.851562  36500.656250  36385.816406   \n\n                                lag_290       lag_291       lag_292  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  33223.078125  32611.640625  32781.210938   \n2021-01-15 10:00:00+00:00  33769.519531  33223.078125  32611.640625   \n2021-01-15 11:00:00+00:00  33980.738281  33769.519531  33223.078125   \n2021-01-15 12:00:00+00:00  34459.031250  33980.738281  33769.519531   \n2021-01-15 13:00:00+00:00  34398.449219  34459.031250  33980.738281   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36246.609375  35884.214844  35794.199219   \n2023-11-27 14:00:00+00:00  36225.386719  36246.609375  35884.214844   \n2023-11-27 15:00:00+00:00  36099.589844  36225.386719  36246.609375   \n2023-11-27 16:00:00+00:00  36132.300781  36099.589844  36225.386719   \n2023-11-27 17:00:00+00:00  36398.691406  36132.300781  36099.589844   \n\n                                lag_293       lag_294       lag_295  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  32621.369141  32480.000000  32218.380859   \n2021-01-15 10:00:00+00:00  32781.210938  32621.369141  32480.000000   \n2021-01-15 11:00:00+00:00  32611.640625  32781.210938  32621.369141   \n2021-01-15 12:00:00+00:00  33223.078125  32611.640625  32781.210938   \n2021-01-15 13:00:00+00:00  33769.519531  33223.078125  32611.640625   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  35801.464844  35595.703125  35594.414062   \n2023-11-27 14:00:00+00:00  35794.199219  35801.464844  35595.703125   \n2023-11-27 15:00:00+00:00  35884.214844  35794.199219  35801.464844   \n2023-11-27 16:00:00+00:00  36246.609375  35884.214844  35794.199219   \n2023-11-27 17:00:00+00:00  36225.386719  36246.609375  35884.214844   \n\n                                lag_296       lag_297       lag_298  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  32170.570312  31701.539062  30750.000000   \n2021-01-15 10:00:00+00:00  32218.380859  32170.570312  31701.539062   \n2021-01-15 11:00:00+00:00  32480.000000  32218.380859  32170.570312   \n2021-01-15 12:00:00+00:00  32621.369141  32480.000000  32218.380859   \n2021-01-15 13:00:00+00:00  32781.210938  32621.369141  32480.000000   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  35620.476562  35580.953125  35400.820312   \n2023-11-27 14:00:00+00:00  35594.414062  35620.476562  35580.953125   \n2023-11-27 15:00:00+00:00  35595.703125  35594.414062  35620.476562   \n2023-11-27 16:00:00+00:00  35801.464844  35595.703125  35594.414062   \n2023-11-27 17:00:00+00:00  35794.199219  35801.464844  35595.703125   \n\n                                lag_299       lag_300       lag_301  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  33087.839844  33007.468750  32940.011719   \n2021-01-15 10:00:00+00:00  30750.000000  33087.839844  33007.468750   \n2021-01-15 11:00:00+00:00  31701.539062  30750.000000  33087.839844   \n2021-01-15 12:00:00+00:00  32170.570312  31701.539062  30750.000000   \n2021-01-15 13:00:00+00:00  32218.380859  32170.570312  31701.539062   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  35487.273438  35491.156250  35502.875000   \n2023-11-27 14:00:00+00:00  35400.820312  35487.273438  35491.156250   \n2023-11-27 15:00:00+00:00  35580.953125  35400.820312  35487.273438   \n2023-11-27 16:00:00+00:00  35620.476562  35580.953125  35400.820312   \n2023-11-27 17:00:00+00:00  35594.414062  35620.476562  35580.953125   \n\n                                lag_302       lag_303       lag_304  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  32551.220703  31763.000000  31352.519531   \n2021-01-15 10:00:00+00:00  32940.011719  32551.220703  31763.000000   \n2021-01-15 11:00:00+00:00  33007.468750  32940.011719  32551.220703   \n2021-01-15 12:00:00+00:00  33087.839844  33007.468750  32940.011719   \n2021-01-15 13:00:00+00:00  30750.000000  33087.839844  33007.468750   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  35551.351562  35612.113281  35575.347656   \n2023-11-27 14:00:00+00:00  35502.875000  35551.351562  35612.113281   \n2023-11-27 15:00:00+00:00  35491.156250  35502.875000  35551.351562   \n2023-11-27 16:00:00+00:00  35487.273438  35491.156250  35502.875000   \n2023-11-27 17:00:00+00:00  35400.820312  35487.273438  35491.156250   \n\n                                lag_305       lag_306       lag_307  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  31599.269531  30649.710938  29832.000000   \n2021-01-15 10:00:00+00:00  31352.519531  31599.269531  30649.710938   \n2021-01-15 11:00:00+00:00  31763.000000  31352.519531  31599.269531   \n2021-01-15 12:00:00+00:00  32551.220703  31763.000000  31352.519531   \n2021-01-15 13:00:00+00:00  32940.011719  32551.220703  31763.000000   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  35291.933594  35294.433594  35240.332031   \n2023-11-27 14:00:00+00:00  35575.347656  35291.933594  35294.433594   \n2023-11-27 15:00:00+00:00  35612.113281  35575.347656  35291.933594   \n2023-11-27 16:00:00+00:00  35551.351562  35612.113281  35575.347656   \n2023-11-27 17:00:00+00:00  35502.875000  35551.351562  35612.113281   \n\n                                lag_308       lag_309       lag_310  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  29745.000000  29696.320312  29912.390625   \n2021-01-15 10:00:00+00:00  29832.000000  29745.000000  29696.320312   \n2021-01-15 11:00:00+00:00  30649.710938  29832.000000  29745.000000   \n2021-01-15 12:00:00+00:00  31599.269531  30649.710938  29832.000000   \n2021-01-15 13:00:00+00:00  31352.519531  31599.269531  30649.710938   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36083.226562  36274.691406  36142.460938   \n2023-11-27 14:00:00+00:00  35240.332031  36083.226562  36274.691406   \n2023-11-27 15:00:00+00:00  35294.433594  35240.332031  36083.226562   \n2023-11-27 16:00:00+00:00  35291.933594  35294.433594  35240.332031   \n2023-11-27 17:00:00+00:00  35575.347656  35291.933594  35294.433594   \n\n                                lag_311       lag_312       lag_313  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  29833.599609  29799.960938  29676.789062   \n2021-01-15 10:00:00+00:00  29912.390625  29833.599609  29799.960938   \n2021-01-15 11:00:00+00:00  29696.320312  29912.390625  29833.599609   \n2021-01-15 12:00:00+00:00  29745.000000  29696.320312  29912.390625   \n2021-01-15 13:00:00+00:00  29832.000000  29745.000000  29696.320312   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36617.410156  36528.296875  36365.453125   \n2023-11-27 14:00:00+00:00  36142.460938  36617.410156  36528.296875   \n2023-11-27 15:00:00+00:00  36274.691406  36142.460938  36617.410156   \n2023-11-27 16:00:00+00:00  36083.226562  36274.691406  36142.460938   \n2023-11-27 17:00:00+00:00  35240.332031  36083.226562  36274.691406   \n\n                                lag_314       lag_315       lag_316  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  29442.300781  29430.830078  29400.000000   \n2021-01-15 10:00:00+00:00  29676.789062  29442.300781  29430.830078   \n2021-01-15 11:00:00+00:00  29799.960938  29676.789062  29442.300781   \n2021-01-15 12:00:00+00:00  29833.599609  29799.960938  29676.789062   \n2021-01-15 13:00:00+00:00  29912.390625  29833.599609  29799.960938   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36279.300781  36515.265625  36490.078125   \n2023-11-27 14:00:00+00:00  36365.453125  36279.300781  36515.265625   \n2023-11-27 15:00:00+00:00  36528.296875  36365.453125  36279.300781   \n2023-11-27 16:00:00+00:00  36617.410156  36528.296875  36365.453125   \n2023-11-27 17:00:00+00:00  36142.460938  36617.410156  36528.296875   \n\n                                lag_317       lag_318       lag_319  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  29454.089844  29262.789062  29418.720703   \n2021-01-15 10:00:00+00:00  29400.000000  29454.089844  29262.789062   \n2021-01-15 11:00:00+00:00  29430.830078  29400.000000  29454.089844   \n2021-01-15 12:00:00+00:00  29442.300781  29430.830078  29400.000000   \n2021-01-15 13:00:00+00:00  29676.789062  29442.300781  29430.830078   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36742.324219  36698.449219  36666.105469   \n2023-11-27 14:00:00+00:00  36490.078125  36742.324219  36698.449219   \n2023-11-27 15:00:00+00:00  36515.265625  36490.078125  36742.324219   \n2023-11-27 16:00:00+00:00  36279.300781  36515.265625  36490.078125   \n2023-11-27 17:00:00+00:00  36365.453125  36279.300781  36515.265625   \n\n                                lag_320       lag_321       lag_322  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  29360.880859  29258.820312  29301.929688   \n2021-01-15 10:00:00+00:00  29418.720703  29360.880859  29258.820312   \n2021-01-15 11:00:00+00:00  29262.789062  29418.720703  29360.880859   \n2021-01-15 12:00:00+00:00  29454.089844  29262.789062  29418.720703   \n2021-01-15 13:00:00+00:00  29400.000000  29454.089844  29262.789062   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36641.476562  36498.023438  36463.804688   \n2023-11-27 14:00:00+00:00  36666.105469  36641.476562  36498.023438   \n2023-11-27 15:00:00+00:00  36698.449219  36666.105469  36641.476562   \n2023-11-27 16:00:00+00:00  36742.324219  36698.449219  36666.105469   \n2023-11-27 17:00:00+00:00  36490.078125  36742.324219  36698.449219   \n\n                                lag_323       lag_324       lag_325  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  29104.880859  29141.189453  29169.029297   \n2021-01-15 10:00:00+00:00  29301.929688  29104.880859  29141.189453   \n2021-01-15 11:00:00+00:00  29258.820312  29301.929688  29104.880859   \n2021-01-15 12:00:00+00:00  29360.880859  29258.820312  29301.929688   \n2021-01-15 13:00:00+00:00  29418.720703  29360.880859  29258.820312   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36442.343750  36340.074219  36256.136719   \n2023-11-27 14:00:00+00:00  36463.804688  36442.343750  36340.074219   \n2023-11-27 15:00:00+00:00  36498.023438  36463.804688  36442.343750   \n2023-11-27 16:00:00+00:00  36641.476562  36498.023438  36463.804688   \n2023-11-27 17:00:00+00:00  36666.105469  36641.476562  36498.023438   \n\n                                lag_326       lag_327       lag_328  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  29372.880859  29264.220703  29398.099609   \n2021-01-15 10:00:00+00:00  29169.029297  29372.880859  29264.220703   \n2021-01-15 11:00:00+00:00  29141.189453  29169.029297  29372.880859   \n2021-01-15 12:00:00+00:00  29104.880859  29141.189453  29169.029297   \n2021-01-15 13:00:00+00:00  29301.929688  29104.880859  29141.189453   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36541.910156  36477.402344  36495.328125   \n2023-11-27 14:00:00+00:00  36256.136719  36541.910156  36477.402344   \n2023-11-27 15:00:00+00:00  36340.074219  36256.136719  36541.910156   \n2023-11-27 16:00:00+00:00  36442.343750  36340.074219  36256.136719   \n2023-11-27 17:00:00+00:00  36463.804688  36442.343750  36340.074219   \n\n                                lag_329       lag_330       lag_331  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  29535.000000  29311.470703  29396.279297   \n2021-01-15 10:00:00+00:00  29398.099609  29535.000000  29311.470703   \n2021-01-15 11:00:00+00:00  29264.220703  29398.099609  29535.000000   \n2021-01-15 12:00:00+00:00  29372.880859  29264.220703  29398.099609   \n2021-01-15 13:00:00+00:00  29169.029297  29372.880859  29264.220703   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36800.109375  36862.921875  36760.453125   \n2023-11-27 14:00:00+00:00  36495.328125  36800.109375  36862.921875   \n2023-11-27 15:00:00+00:00  36477.402344  36495.328125  36800.109375   \n2023-11-27 16:00:00+00:00  36541.910156  36477.402344  36495.328125   \n2023-11-27 17:00:00+00:00  36256.136719  36541.910156  36477.402344   \n\n                                lag_332       lag_333       lag_334  \\\ntimestamp                                                             \n2021-01-15 09:00:00+00:00  29286.630859  29284.919922  29076.769531   \n2021-01-15 10:00:00+00:00  29396.279297  29286.630859  29284.919922   \n2021-01-15 11:00:00+00:00  29311.470703  29396.279297  29286.630859   \n2021-01-15 12:00:00+00:00  29535.000000  29311.470703  29396.279297   \n2021-01-15 13:00:00+00:00  29398.099609  29535.000000  29311.470703   \n...                                 ...           ...           ...   \n2023-11-27 13:00:00+00:00  36657.753906  36861.910156  36862.207031   \n2023-11-27 14:00:00+00:00  36760.453125  36657.753906  36861.910156   \n2023-11-27 15:00:00+00:00  36862.921875  36760.453125  36657.753906   \n2023-11-27 16:00:00+00:00  36800.109375  36862.921875  36760.453125   \n2023-11-27 17:00:00+00:00  36495.328125  36800.109375  36862.921875   \n\n                                lag_335       lag_336        target  \ntimestamp                                                            \n2021-01-15 09:00:00+00:00  29162.500000  29234.150391  31422.880859  \n2021-01-15 10:00:00+00:00  29076.769531  29162.500000  31383.779297  \n2021-01-15 11:00:00+00:00  29284.919922  29076.769531  31756.880859  \n2021-01-15 12:00:00+00:00  29286.630859  29284.919922  31563.599609  \n2021-01-15 13:00:00+00:00  29396.279297  29286.630859  32195.539062  \n...                                 ...           ...           ...  \n2023-11-27 13:00:00+00:00  36806.250000  36837.101562  41753.480469  \n2023-11-27 14:00:00+00:00  36862.207031  36806.250000  41644.941406  \n2023-11-27 15:00:00+00:00  36861.910156  36862.207031  41270.328125  \n2023-11-27 16:00:00+00:00  36657.753906  36861.910156  41610.027344  \n2023-11-27 17:00:00+00:00  36760.453125  36657.753906  41589.976562  \n\n[25106 rows x 356 columns]",
      "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>close</th>\n      <th>log_close/mean_60</th>\n      <th>log_return_60</th>\n      <th>mean_close/mean_60</th>\n      <th>mean_log_returns_60</th>\n      <th>log_close/mean_60-mean_close/mean_60</th>\n      <th>log_return_60-mean_log_returns_60</th>\n      <th>log_close/mean_300</th>\n      <th>log_return_300</th>\n      <th>mean_close/mean_300</th>\n      <th>mean_log_returns_300</th>\n      <th>log_close/mean_300-mean_close/mean_300</th>\n      <th>log_return_300-mean_log_returns_300</th>\n      <th>log_close/mean_900</th>\n      <th>log_return_900</th>\n      <th>mean_close/mean_900</th>\n      <th>mean_log_returns_900</th>\n      <th>log_close/mean_900-mean_close/mean_900</th>\n      <th>log_return_900-mean_log_returns_900</th>\n      <th>lag_1</th>\n      <th>lag_2</th>\n      <th>lag_3</th>\n      <th>lag_4</th>\n      <th>lag_5</th>\n      <th>lag_6</th>\n      <th>lag_7</th>\n      <th>lag_8</th>\n      <th>lag_9</th>\n      <th>lag_10</th>\n      <th>lag_11</th>\n      <th>lag_12</th>\n      <th>lag_13</th>\n      <th>lag_14</th>\n      <th>lag_15</th>\n      <th>lag_16</th>\n      <th>lag_17</th>\n      <th>lag_18</th>\n      <th>lag_19</th>\n      <th>lag_20</th>\n      <th>lag_21</th>\n      <th>lag_22</th>\n      <th>lag_23</th>\n      <th>lag_24</th>\n      <th>lag_25</th>\n      <th>lag_26</th>\n      <th>lag_27</th>\n      <th>lag_28</th>\n      <th>lag_29</th>\n      <th>lag_30</th>\n      <th>lag_31</th>\n      <th>lag_32</th>\n      <th>lag_33</th>\n      <th>lag_34</th>\n      <th>lag_35</th>\n      <th>lag_36</th>\n      <th>lag_37</th>\n      <th>lag_38</th>\n      <th>lag_39</th>\n      <th>lag_40</th>\n      <th>lag_41</th>\n      <th>lag_42</th>\n      <th>lag_43</th>\n      <th>lag_44</th>\n      <th>lag_45</th>\n      <th>lag_46</th>\n      <th>lag_47</th>\n      <th>lag_48</th>\n      <th>lag_49</th>\n      <th>lag_50</th>\n      <th>lag_51</th>\n      <th>lag_52</th>\n      <th>lag_53</th>\n      <th>lag_54</th>\n      <th>lag_55</th>\n      <th>lag_56</th>\n      <th>lag_57</th>\n      <th>lag_58</th>\n      <th>lag_59</th>\n      <th>lag_60</th>\n      <th>lag_61</th>\n      <th>lag_62</th>\n      <th>lag_63</th>\n      <th>lag_64</th>\n      <th>lag_65</th>\n      <th>lag_66</th>\n      <th>lag_67</th>\n      <th>lag_68</th>\n      <th>lag_69</th>\n      <th>lag_70</th>\n      <th>lag_71</th>\n      <th>lag_72</th>\n      <th>lag_73</th>\n      <th>lag_74</th>\n      <th>lag_75</th>\n      <th>lag_76</th>\n      <th>lag_77</th>\n      <th>lag_78</th>\n      <th>lag_79</th>\n      <th>lag_80</th>\n      <th>lag_81</th>\n      <th>lag_82</th>\n      <th>lag_83</th>\n      <th>lag_84</th>\n      <th>lag_85</th>\n      <th>lag_86</th>\n      <th>lag_87</th>\n      <th>lag_88</th>\n      <th>lag_89</th>\n      <th>lag_90</th>\n      <th>lag_91</th>\n      <th>lag_92</th>\n      <th>lag_93</th>\n      <th>lag_94</th>\n      <th>lag_95</th>\n      <th>lag_96</th>\n      <th>lag_97</th>\n      <th>lag_98</th>\n      <th>lag_99</th>\n      <th>lag_100</th>\n      <th>lag_101</th>\n      <th>lag_102</th>\n      <th>lag_103</th>\n      <th>lag_104</th>\n      <th>lag_105</th>\n      <th>lag_106</th>\n      <th>lag_107</th>\n      <th>lag_108</th>\n      <th>lag_109</th>\n      <th>lag_110</th>\n      <th>lag_111</th>\n      <th>lag_112</th>\n      <th>lag_113</th>\n      <th>lag_114</th>\n      <th>lag_115</th>\n      <th>lag_116</th>\n      <th>lag_117</th>\n      <th>lag_118</th>\n      <th>lag_119</th>\n      <th>lag_120</th>\n      <th>lag_121</th>\n      <th>lag_122</th>\n      <th>lag_123</th>\n      <th>lag_124</th>\n      <th>lag_125</th>\n      <th>lag_126</th>\n      <th>lag_127</th>\n      <th>lag_128</th>\n      <th>lag_129</th>\n      <th>lag_130</th>\n      <th>lag_131</th>\n      <th>lag_132</th>\n      <th>lag_133</th>\n      <th>lag_134</th>\n      <th>lag_135</th>\n      <th>lag_136</th>\n      <th>lag_137</th>\n      <th>lag_138</th>\n      <th>lag_139</th>\n      <th>lag_140</th>\n      <th>lag_141</th>\n      <th>lag_142</th>\n      <th>lag_143</th>\n      <th>lag_144</th>\n      <th>lag_145</th>\n      <th>lag_146</th>\n      <th>lag_147</th>\n      <th>lag_148</th>\n      <th>lag_149</th>\n      <th>lag_150</th>\n      <th>lag_151</th>\n      <th>lag_152</th>\n      <th>lag_153</th>\n      <th>lag_154</th>\n      <th>lag_155</th>\n      <th>lag_156</th>\n      <th>...</th>\n      <th>lag_163</th>\n      <th>lag_164</th>\n      <th>lag_165</th>\n      <th>lag_166</th>\n      <th>lag_167</th>\n      <th>lag_168</th>\n      <th>lag_169</th>\n      <th>lag_170</th>\n      <th>lag_171</th>\n      <th>lag_172</th>\n      <th>lag_173</th>\n      <th>lag_174</th>\n      <th>lag_175</th>\n      <th>lag_176</th>\n      <th>lag_177</th>\n      <th>lag_178</th>\n      <th>lag_179</th>\n      <th>lag_180</th>\n      <th>lag_181</th>\n      <th>lag_182</th>\n      <th>lag_183</th>\n      <th>lag_184</th>\n      <th>lag_185</th>\n      <th>lag_186</th>\n      <th>lag_187</th>\n      <th>lag_188</th>\n      <th>lag_189</th>\n      <th>lag_190</th>\n      <th>lag_191</th>\n      <th>lag_192</th>\n      <th>lag_193</th>\n      <th>lag_194</th>\n      <th>lag_195</th>\n      <th>lag_196</th>\n      <th>lag_197</th>\n      <th>lag_198</th>\n      <th>lag_199</th>\n      <th>lag_200</th>\n      <th>lag_201</th>\n      <th>lag_202</th>\n      <th>lag_203</th>\n      <th>lag_204</th>\n      <th>lag_205</th>\n      <th>lag_206</th>\n      <th>lag_207</th>\n      <th>lag_208</th>\n      <th>lag_209</th>\n      <th>lag_210</th>\n      <th>lag_211</th>\n      <th>lag_212</th>\n      <th>lag_213</th>\n      <th>lag_214</th>\n      <th>lag_215</th>\n      <th>lag_216</th>\n      <th>lag_217</th>\n      <th>lag_218</th>\n      <th>lag_219</th>\n      <th>lag_220</th>\n      <th>lag_221</th>\n      <th>lag_222</th>\n      <th>lag_223</th>\n      <th>lag_224</th>\n      <th>lag_225</th>\n      <th>lag_226</th>\n      <th>lag_227</th>\n      <th>lag_228</th>\n      <th>lag_229</th>\n      <th>lag_230</th>\n      <th>lag_231</th>\n      <th>lag_232</th>\n      <th>lag_233</th>\n      <th>lag_234</th>\n      <th>lag_235</th>\n      <th>lag_236</th>\n      <th>lag_237</th>\n      <th>lag_238</th>\n      <th>lag_239</th>\n      <th>lag_240</th>\n      <th>lag_241</th>\n      <th>lag_242</th>\n      <th>lag_243</th>\n      <th>lag_244</th>\n      <th>lag_245</th>\n      <th>lag_246</th>\n      <th>lag_247</th>\n      <th>lag_248</th>\n      <th>lag_249</th>\n      <th>lag_250</th>\n      <th>lag_251</th>\n      <th>lag_252</th>\n      <th>lag_253</th>\n      <th>lag_254</th>\n      <th>lag_255</th>\n      <th>lag_256</th>\n      <th>lag_257</th>\n      <th>lag_258</th>\n      <th>lag_259</th>\n      <th>lag_260</th>\n      <th>lag_261</th>\n      <th>lag_262</th>\n      <th>lag_263</th>\n      <th>lag_264</th>\n      <th>lag_265</th>\n      <th>lag_266</th>\n      <th>lag_267</th>\n      <th>lag_268</th>\n      <th>lag_269</th>\n      <th>lag_270</th>\n      <th>lag_271</th>\n      <th>lag_272</th>\n      <th>lag_273</th>\n      <th>lag_274</th>\n      <th>lag_275</th>\n      <th>lag_276</th>\n      <th>lag_277</th>\n      <th>lag_278</th>\n      <th>lag_279</th>\n      <th>lag_280</th>\n      <th>lag_281</th>\n      <th>lag_282</th>\n      <th>lag_283</th>\n      <th>lag_284</th>\n      <th>lag_285</th>\n      <th>lag_286</th>\n      <th>lag_287</th>\n      <th>lag_288</th>\n      <th>lag_289</th>\n      <th>lag_290</th>\n      <th>lag_291</th>\n      <th>lag_292</th>\n      <th>lag_293</th>\n      <th>lag_294</th>\n      <th>lag_295</th>\n      <th>lag_296</th>\n      <th>lag_297</th>\n      <th>lag_298</th>\n      <th>lag_299</th>\n      <th>lag_300</th>\n      <th>lag_301</th>\n      <th>lag_302</th>\n      <th>lag_303</th>\n      <th>lag_304</th>\n      <th>lag_305</th>\n      <th>lag_306</th>\n      <th>lag_307</th>\n      <th>lag_308</th>\n      <th>lag_309</th>\n      <th>lag_310</th>\n      <th>lag_311</th>\n      <th>lag_312</th>\n      <th>lag_313</th>\n      <th>lag_314</th>\n      <th>lag_315</th>\n      <th>lag_316</th>\n      <th>lag_317</th>\n      <th>lag_318</th>\n      <th>lag_319</th>\n      <th>lag_320</th>\n      <th>lag_321</th>\n      <th>lag_322</th>\n      <th>lag_323</th>\n      <th>lag_324</th>\n      <th>lag_325</th>\n      <th>lag_326</th>\n      <th>lag_327</th>\n      <th>lag_328</th>\n      <th>lag_329</th>\n      <th>lag_330</th>\n      <th>lag_331</th>\n      <th>lag_332</th>\n      <th>lag_333</th>\n      <th>lag_334</th>\n      <th>lag_335</th>\n      <th>lag_336</th>\n      <th>target</th>\n    </tr>\n    <tr>\n      <th>timestamp</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-01-15 09:00:00+00:00</th>\n      <td>38638.250000</td>\n      <td>0.047538</td>\n      <td>0.108208</td>\n      <td>0.023853</td>\n      <td>1.489534e-10</td>\n      <td>0.023685</td>\n      <td>0.108208</td>\n      <td>0.067505</td>\n      <td>0.157509</td>\n      <td>0.121317</td>\n      <td>2.979069e-10</td>\n      <td>-0.053812</td>\n      <td>0.157509</td>\n      <td>10.561998</td>\n      <td>0.040444</td>\n      <td>0.364721</td>\n      <td>4.766510e-09</td>\n      <td>10.197277</td>\n      <td>0.040444</td>\n      <td>38261.460938</td>\n      <td>38436.921875</td>\n      <td>38000.000000</td>\n      <td>37800.019531</td>\n      <td>38419.769531</td>\n      <td>38669.949219</td>\n      <td>39219.519531</td>\n      <td>39118.320312</td>\n      <td>39425.910156</td>\n      <td>39127.101562</td>\n      <td>38932.648438</td>\n      <td>38767.269531</td>\n      <td>39319.371094</td>\n      <td>39837.339844</td>\n      <td>39311.238281</td>\n      <td>39403.218750</td>\n      <td>39240.449219</td>\n      <td>39506.679688</td>\n      <td>39574.878906</td>\n      <td>38994.078125</td>\n      <td>38275.000000</td>\n      <td>38361.769531</td>\n      <td>38494.500000</td>\n      <td>37972.750000</td>\n      <td>38406.128906</td>\n      <td>38224.589844</td>\n      <td>37861.808594</td>\n      <td>37546.570312</td>\n      <td>37501.000000</td>\n      <td>37037.511719</td>\n      <td>37428.949219</td>\n      <td>37438.890625</td>\n      <td>37792.710938</td>\n      <td>37400.000000</td>\n      <td>37177.968750</td>\n      <td>37294.968750</td>\n      <td>36210.621094</td>\n      <td>35992.359375</td>\n      <td>35706.871094</td>\n      <td>34777.578125</td>\n      <td>34750.000000</td>\n      <td>34646.531250</td>\n      <td>34250.078125</td>\n      <td>34553.570312</td>\n      <td>34247.710938</td>\n      <td>34350.328125</td>\n      <td>34906.191406</td>\n      <td>34325.000000</td>\n      <td>34521.519531</td>\n      <td>34967.351562</td>\n      <td>33729.250000</td>\n      <td>33284.019531</td>\n      <td>33131.640625</td>\n      <td>33770.179688</td>\n      <td>32995.460938</td>\n      <td>33109.378906</td>\n      <td>32469.419922</td>\n      <td>34031.160156</td>\n      <td>33720.648438</td>\n      <td>34675.550781</td>\n      <td>34282.460938</td>\n      <td>34642.320312</td>\n      <td>35013.011719</td>\n      <td>34991.878906</td>\n      <td>34984.000000</td>\n      <td>33831.558594</td>\n      <td>33394.210938</td>\n      <td>33263.000000</td>\n      <td>35030.539062</td>\n      <td>35382.781250</td>\n      <td>35032.558594</td>\n      <td>35928.031250</td>\n      <td>35772.339844</td>\n      <td>36448.281250</td>\n      <td>36002.031250</td>\n      <td>35892.578125</td>\n      <td>35299.988281</td>\n      <td>34964.468750</td>\n      <td>34371.000000</td>\n      <td>34272.179688</td>\n      <td>34942.488281</td>\n      <td>35494.000000</td>\n      <td>34409.890625</td>\n      <td>34006.250000</td>\n      <td>33373.480469</td>\n      <td>32238.699219</td>\n      <td>32573.380859</td>\n      <td>32800.000000</td>\n      <td>31665.929688</td>\n      <td>31511.849609</td>\n      <td>33219.339844</td>\n      <td>32750.000000</td>\n      <td>34232.859375</td>\n      <td>34119.179688</td>\n      <td>35359.730469</td>\n      <td>35700.000000</td>\n      <td>34860.750000</td>\n      <td>35502.550781</td>\n      <td>35367.960938</td>\n      <td>36339.550781</td>\n      <td>37235.558594</td>\n      <td>37198.511719</td>\n      <td>38181.171875</td>\n      <td>38456.601562</td>\n      <td>38050.148438</td>\n      <td>37693.480469</td>\n      <td>37422.988281</td>\n      <td>38112.921875</td>\n      <td>38663.769531</td>\n      <td>39299.441406</td>\n      <td>39726.238281</td>\n      <td>39481.898438</td>\n      <td>39848.191406</td>\n      <td>39600.550781</td>\n      <td>39218.171875</td>\n      <td>40482.351562</td>\n      <td>40838.699219</td>\n      <td>40905.710938</td>\n      <td>41023.000000</td>\n      <td>41030.160156</td>\n      <td>40547.421875</td>\n      <td>40466.718750</td>\n      <td>40467.558594</td>\n      <td>41064.800781</td>\n      <td>41231.820312</td>\n      <td>40684.031250</td>\n      <td>40256.019531</td>\n      <td>40428.589844</td>\n      <td>40848.281250</td>\n      <td>40731.859375</td>\n      <td>40767.289062</td>\n      <td>40725.308594</td>\n      <td>40515.339844</td>\n      <td>40099.300781</td>\n      <td>40622.171875</td>\n      <td>40895.519531</td>\n      <td>41128.300781</td>\n      <td>40912.750000</td>\n      <td>40624.960938</td>\n      <td>40415.031250</td>\n      <td>40343.210938</td>\n      <td>40204.171875</td>\n      <td>39654.949219</td>\n      <td>38989.871094</td>\n      <td>39379.238281</td>\n      <td>40254.210938</td>\n      <td>40261.289062</td>\n      <td>39987.441406</td>\n      <td>40207.601562</td>\n      <td>40305.570312</td>\n      <td>40622.871094</td>\n      <td>40719.000000</td>\n      <td>40050.000000</td>\n      <td>39107.738281</td>\n      <td>40363.320312</td>\n      <td>39914.738281</td>\n      <td>...</td>\n      <td>41340.250000</td>\n      <td>40749.351562</td>\n      <td>39379.000000</td>\n      <td>38655.660156</td>\n      <td>38953.320312</td>\n      <td>38560.898438</td>\n      <td>38009.789062</td>\n      <td>38723.480469</td>\n      <td>38404.460938</td>\n      <td>37226.308594</td>\n      <td>38701.941406</td>\n      <td>38840.738281</td>\n      <td>39479.781250</td>\n      <td>39698.871094</td>\n      <td>39732.289062</td>\n      <td>39141.980469</td>\n      <td>39078.390625</td>\n      <td>38934.351562</td>\n      <td>39771.328125</td>\n      <td>39182.171875</td>\n      <td>39024.210938</td>\n      <td>38503.449219</td>\n      <td>38227.988281</td>\n      <td>38015.328125</td>\n      <td>37888.011719</td>\n      <td>37089.039062</td>\n      <td>37529.988281</td>\n      <td>37243.328125</td>\n      <td>36922.960938</td>\n      <td>37228.820312</td>\n      <td>37485.269531</td>\n      <td>37559.421875</td>\n      <td>37560.300781</td>\n      <td>37082.941406</td>\n      <td>36971.781250</td>\n      <td>37223.078125</td>\n      <td>36856.980469</td>\n      <td>36084.148438</td>\n      <td>35960.839844</td>\n      <td>36133.750000</td>\n      <td>34920.789062</td>\n      <td>35329.281250</td>\n      <td>35000.000000</td>\n      <td>34700.441406</td>\n      <td>34670.128906</td>\n      <td>34285.750000</td>\n      <td>34620.218750</td>\n      <td>35027.871094</td>\n      <td>35013.550781</td>\n      <td>34526.511719</td>\n      <td>33995.511719</td>\n      <td>34641.738281</td>\n      <td>34768.910156</td>\n      <td>35257.191406</td>\n      <td>35063.000000</td>\n      <td>35455.339844</td>\n      <td>34192.351562</td>\n      <td>33978.000000</td>\n      <td>33888.429688</td>\n      <td>33976.468750</td>\n      <td>34036.078125</td>\n      <td>34313.199219</td>\n      <td>33810.078125</td>\n      <td>34064.121094</td>\n      <td>33810.898438</td>\n      <td>33459.019531</td>\n      <td>32795.199219</td>\n      <td>32501.630859</td>\n      <td>32158.669922</td>\n      <td>32375.900391</td>\n      <td>31510.330078</td>\n      <td>31634.000000</td>\n      <td>31815.189453</td>\n      <td>31360.509766</td>\n      <td>31592.599609</td>\n      <td>31217.900391</td>\n      <td>30816.070312</td>\n      <td>31154.589844</td>\n      <td>30441.570312</td>\n      <td>30879.560547</td>\n      <td>31201.310547</td>\n      <td>32354.060547</td>\n      <td>32823.960938</td>\n      <td>32450.119141</td>\n      <td>32025.230469</td>\n      <td>31364.589844</td>\n      <td>31029.650391</td>\n      <td>31287.410156</td>\n      <td>31667.720703</td>\n      <td>31184.980469</td>\n      <td>31015.689453</td>\n      <td>31321.000000</td>\n      <td>31770.460938</td>\n      <td>32137.560547</td>\n      <td>31418.369141</td>\n      <td>31239.310547</td>\n      <td>30755.500000</td>\n      <td>30290.429688</td>\n      <td>31013.269531</td>\n      <td>32041.269531</td>\n      <td>32105.070312</td>\n      <td>33260.808594</td>\n      <td>32908.019531</td>\n      <td>33317.148438</td>\n      <td>33588.101562</td>\n      <td>33493.031250</td>\n      <td>33229.269531</td>\n      <td>32882.949219</td>\n      <td>33111.988281</td>\n      <td>33148.109375</td>\n      <td>33584.960938</td>\n      <td>33747.281250</td>\n      <td>32777.250000</td>\n      <td>32792.761719</td>\n      <td>32902.011719</td>\n      <td>32711.720703</td>\n      <td>33561.890625</td>\n      <td>33893.031250</td>\n      <td>33900.000000</td>\n      <td>34128.359375</td>\n      <td>34393.289062</td>\n      <td>33909.988281</td>\n      <td>34207.218750</td>\n      <td>34398.449219</td>\n      <td>34459.031250</td>\n      <td>33980.738281</td>\n      <td>33769.519531</td>\n      <td>33223.078125</td>\n      <td>32611.640625</td>\n      <td>32781.210938</td>\n      <td>32621.369141</td>\n      <td>32480.000000</td>\n      <td>32218.380859</td>\n      <td>32170.570312</td>\n      <td>31701.539062</td>\n      <td>30750.000000</td>\n      <td>33087.839844</td>\n      <td>33007.468750</td>\n      <td>32940.011719</td>\n      <td>32551.220703</td>\n      <td>31763.000000</td>\n      <td>31352.519531</td>\n      <td>31599.269531</td>\n      <td>30649.710938</td>\n      <td>29832.000000</td>\n      <td>29745.000000</td>\n      <td>29696.320312</td>\n      <td>29912.390625</td>\n      <td>29833.599609</td>\n      <td>29799.960938</td>\n      <td>29676.789062</td>\n      <td>29442.300781</td>\n      <td>29430.830078</td>\n      <td>29400.000000</td>\n      <td>29454.089844</td>\n      <td>29262.789062</td>\n      <td>29418.720703</td>\n      <td>29360.880859</td>\n      <td>29258.820312</td>\n      <td>29301.929688</td>\n      <td>29104.880859</td>\n      <td>29141.189453</td>\n      <td>29169.029297</td>\n      <td>29372.880859</td>\n      <td>29264.220703</td>\n      <td>29398.099609</td>\n      <td>29535.000000</td>\n      <td>29311.470703</td>\n      <td>29396.279297</td>\n      <td>29286.630859</td>\n      <td>29284.919922</td>\n      <td>29076.769531</td>\n      <td>29162.500000</td>\n      <td>29234.150391</td>\n      <td>31422.880859</td>\n    </tr>\n    <tr>\n      <th>2021-01-15 10:00:00+00:00</th>\n      <td>38410.539062</td>\n      <td>0.039508</td>\n      <td>0.130221</td>\n      <td>0.023853</td>\n      <td>1.489534e-10</td>\n      <td>0.015655</td>\n      <td>0.130221</td>\n      <td>0.061103</td>\n      <td>0.149166</td>\n      <td>0.121317</td>\n      <td>2.979069e-10</td>\n      <td>-0.060214</td>\n      <td>0.149166</td>\n      <td>10.556087</td>\n      <td>0.036478</td>\n      <td>0.364721</td>\n      <td>4.766510e-09</td>\n      <td>10.191367</td>\n      <td>0.036478</td>\n      <td>38638.250000</td>\n      <td>38261.460938</td>\n      <td>38436.921875</td>\n      <td>38000.000000</td>\n      <td>37800.019531</td>\n      <td>38419.769531</td>\n      <td>38669.949219</td>\n      <td>39219.519531</td>\n      <td>39118.320312</td>\n      <td>39425.910156</td>\n      <td>39127.101562</td>\n      <td>38932.648438</td>\n      <td>38767.269531</td>\n      <td>39319.371094</td>\n      <td>39837.339844</td>\n      <td>39311.238281</td>\n      <td>39403.218750</td>\n      <td>39240.449219</td>\n      <td>39506.679688</td>\n      <td>39574.878906</td>\n      <td>38994.078125</td>\n      <td>38275.000000</td>\n      <td>38361.769531</td>\n      <td>38494.500000</td>\n      <td>37972.750000</td>\n      <td>38406.128906</td>\n      <td>38224.589844</td>\n      <td>37861.808594</td>\n      <td>37546.570312</td>\n      <td>37501.000000</td>\n      <td>37037.511719</td>\n      <td>37428.949219</td>\n      <td>37438.890625</td>\n      <td>37792.710938</td>\n      <td>37400.000000</td>\n      <td>37177.968750</td>\n      <td>37294.968750</td>\n      <td>36210.621094</td>\n      <td>35992.359375</td>\n      <td>35706.871094</td>\n      <td>34777.578125</td>\n      <td>34750.000000</td>\n      <td>34646.531250</td>\n      <td>34250.078125</td>\n      <td>34553.570312</td>\n      <td>34247.710938</td>\n      <td>34350.328125</td>\n      <td>34906.191406</td>\n      <td>34325.000000</td>\n      <td>34521.519531</td>\n      <td>34967.351562</td>\n      <td>33729.250000</td>\n      <td>33284.019531</td>\n      <td>33131.640625</td>\n      <td>33770.179688</td>\n      <td>32995.460938</td>\n      <td>33109.378906</td>\n      <td>32469.419922</td>\n      <td>34031.160156</td>\n      <td>33720.648438</td>\n      <td>34675.550781</td>\n      <td>34282.460938</td>\n      <td>34642.320312</td>\n      <td>35013.011719</td>\n      <td>34991.878906</td>\n      <td>34984.000000</td>\n      <td>33831.558594</td>\n      <td>33394.210938</td>\n      <td>33263.000000</td>\n      <td>35030.539062</td>\n      <td>35382.781250</td>\n      <td>35032.558594</td>\n      <td>35928.031250</td>\n      <td>35772.339844</td>\n      <td>36448.281250</td>\n      <td>36002.031250</td>\n      <td>35892.578125</td>\n      <td>35299.988281</td>\n      <td>34964.468750</td>\n      <td>34371.000000</td>\n      <td>34272.179688</td>\n      <td>34942.488281</td>\n      <td>35494.000000</td>\n      <td>34409.890625</td>\n      <td>34006.250000</td>\n      <td>33373.480469</td>\n      <td>32238.699219</td>\n      <td>32573.380859</td>\n      <td>32800.000000</td>\n      <td>31665.929688</td>\n      <td>31511.849609</td>\n      <td>33219.339844</td>\n      <td>32750.000000</td>\n      <td>34232.859375</td>\n      <td>34119.179688</td>\n      <td>35359.730469</td>\n      <td>35700.000000</td>\n      <td>34860.750000</td>\n      <td>35502.550781</td>\n      <td>35367.960938</td>\n      <td>36339.550781</td>\n      <td>37235.558594</td>\n      <td>37198.511719</td>\n      <td>38181.171875</td>\n      <td>38456.601562</td>\n      <td>38050.148438</td>\n      <td>37693.480469</td>\n      <td>37422.988281</td>\n      <td>38112.921875</td>\n      <td>38663.769531</td>\n      <td>39299.441406</td>\n      <td>39726.238281</td>\n      <td>39481.898438</td>\n      <td>39848.191406</td>\n      <td>39600.550781</td>\n      <td>39218.171875</td>\n      <td>40482.351562</td>\n      <td>40838.699219</td>\n      <td>40905.710938</td>\n      <td>41023.000000</td>\n      <td>41030.160156</td>\n      <td>40547.421875</td>\n      <td>40466.718750</td>\n      <td>40467.558594</td>\n      <td>41064.800781</td>\n      <td>41231.820312</td>\n      <td>40684.031250</td>\n      <td>40256.019531</td>\n      <td>40428.589844</td>\n      <td>40848.281250</td>\n      <td>40731.859375</td>\n      <td>40767.289062</td>\n      <td>40725.308594</td>\n      <td>40515.339844</td>\n      <td>40099.300781</td>\n      <td>40622.171875</td>\n      <td>40895.519531</td>\n      <td>41128.300781</td>\n      <td>40912.750000</td>\n      <td>40624.960938</td>\n      <td>40415.031250</td>\n      <td>40343.210938</td>\n      <td>40204.171875</td>\n      <td>39654.949219</td>\n      <td>38989.871094</td>\n      <td>39379.238281</td>\n      <td>40254.210938</td>\n      <td>40261.289062</td>\n      <td>39987.441406</td>\n      <td>40207.601562</td>\n      <td>40305.570312</td>\n      <td>40622.871094</td>\n      <td>40719.000000</td>\n      <td>40050.000000</td>\n      <td>39107.738281</td>\n      <td>40363.320312</td>\n      <td>...</td>\n      <td>41372.308594</td>\n      <td>41340.250000</td>\n      <td>40749.351562</td>\n      <td>39379.000000</td>\n      <td>38655.660156</td>\n      <td>38953.320312</td>\n      <td>38560.898438</td>\n      <td>38009.789062</td>\n      <td>38723.480469</td>\n      <td>38404.460938</td>\n      <td>37226.308594</td>\n      <td>38701.941406</td>\n      <td>38840.738281</td>\n      <td>39479.781250</td>\n      <td>39698.871094</td>\n      <td>39732.289062</td>\n      <td>39141.980469</td>\n      <td>39078.390625</td>\n      <td>38934.351562</td>\n      <td>39771.328125</td>\n      <td>39182.171875</td>\n      <td>39024.210938</td>\n      <td>38503.449219</td>\n      <td>38227.988281</td>\n      <td>38015.328125</td>\n      <td>37888.011719</td>\n      <td>37089.039062</td>\n      <td>37529.988281</td>\n      <td>37243.328125</td>\n      <td>36922.960938</td>\n      <td>37228.820312</td>\n      <td>37485.269531</td>\n      <td>37559.421875</td>\n      <td>37560.300781</td>\n      <td>37082.941406</td>\n      <td>36971.781250</td>\n      <td>37223.078125</td>\n      <td>36856.980469</td>\n      <td>36084.148438</td>\n      <td>35960.839844</td>\n      <td>36133.750000</td>\n      <td>34920.789062</td>\n      <td>35329.281250</td>\n      <td>35000.000000</td>\n      <td>34700.441406</td>\n      <td>34670.128906</td>\n      <td>34285.750000</td>\n      <td>34620.218750</td>\n      <td>35027.871094</td>\n      <td>35013.550781</td>\n      <td>34526.511719</td>\n      <td>33995.511719</td>\n      <td>34641.738281</td>\n      <td>34768.910156</td>\n      <td>35257.191406</td>\n      <td>35063.000000</td>\n      <td>35455.339844</td>\n      <td>34192.351562</td>\n      <td>33978.000000</td>\n      <td>33888.429688</td>\n      <td>33976.468750</td>\n      <td>34036.078125</td>\n      <td>34313.199219</td>\n      <td>33810.078125</td>\n      <td>34064.121094</td>\n      <td>33810.898438</td>\n      <td>33459.019531</td>\n      <td>32795.199219</td>\n      <td>32501.630859</td>\n      <td>32158.669922</td>\n      <td>32375.900391</td>\n      <td>31510.330078</td>\n      <td>31634.000000</td>\n      <td>31815.189453</td>\n      <td>31360.509766</td>\n      <td>31592.599609</td>\n      <td>31217.900391</td>\n      <td>30816.070312</td>\n      <td>31154.589844</td>\n      <td>30441.570312</td>\n      <td>30879.560547</td>\n      <td>31201.310547</td>\n      <td>32354.060547</td>\n      <td>32823.960938</td>\n      <td>32450.119141</td>\n      <td>32025.230469</td>\n      <td>31364.589844</td>\n      <td>31029.650391</td>\n      <td>31287.410156</td>\n      <td>31667.720703</td>\n      <td>31184.980469</td>\n      <td>31015.689453</td>\n      <td>31321.000000</td>\n      <td>31770.460938</td>\n      <td>32137.560547</td>\n      <td>31418.369141</td>\n      <td>31239.310547</td>\n      <td>30755.500000</td>\n      <td>30290.429688</td>\n      <td>31013.269531</td>\n      <td>32041.269531</td>\n      <td>32105.070312</td>\n      <td>33260.808594</td>\n      <td>32908.019531</td>\n      <td>33317.148438</td>\n      <td>33588.101562</td>\n      <td>33493.031250</td>\n      <td>33229.269531</td>\n      <td>32882.949219</td>\n      <td>33111.988281</td>\n      <td>33148.109375</td>\n      <td>33584.960938</td>\n      <td>33747.281250</td>\n      <td>32777.250000</td>\n      <td>32792.761719</td>\n      <td>32902.011719</td>\n      <td>32711.720703</td>\n      <td>33561.890625</td>\n      <td>33893.031250</td>\n      <td>33900.000000</td>\n      <td>34128.359375</td>\n      <td>34393.289062</td>\n      <td>33909.988281</td>\n      <td>34207.218750</td>\n      <td>34398.449219</td>\n      <td>34459.031250</td>\n      <td>33980.738281</td>\n      <td>33769.519531</td>\n      <td>33223.078125</td>\n      <td>32611.640625</td>\n      <td>32781.210938</td>\n      <td>32621.369141</td>\n      <td>32480.000000</td>\n      <td>32218.380859</td>\n      <td>32170.570312</td>\n      <td>31701.539062</td>\n      <td>30750.000000</td>\n      <td>33087.839844</td>\n      <td>33007.468750</td>\n      <td>32940.011719</td>\n      <td>32551.220703</td>\n      <td>31763.000000</td>\n      <td>31352.519531</td>\n      <td>31599.269531</td>\n      <td>30649.710938</td>\n      <td>29832.000000</td>\n      <td>29745.000000</td>\n      <td>29696.320312</td>\n      <td>29912.390625</td>\n      <td>29833.599609</td>\n      <td>29799.960938</td>\n      <td>29676.789062</td>\n      <td>29442.300781</td>\n      <td>29430.830078</td>\n      <td>29400.000000</td>\n      <td>29454.089844</td>\n      <td>29262.789062</td>\n      <td>29418.720703</td>\n      <td>29360.880859</td>\n      <td>29258.820312</td>\n      <td>29301.929688</td>\n      <td>29104.880859</td>\n      <td>29141.189453</td>\n      <td>29169.029297</td>\n      <td>29372.880859</td>\n      <td>29264.220703</td>\n      <td>29398.099609</td>\n      <td>29535.000000</td>\n      <td>29311.470703</td>\n      <td>29396.279297</td>\n      <td>29286.630859</td>\n      <td>29284.919922</td>\n      <td>29076.769531</td>\n      <td>29162.500000</td>\n      <td>31383.779297</td>\n    </tr>\n    <tr>\n      <th>2021-01-15 11:00:00+00:00</th>\n      <td>38066.011719</td>\n      <td>0.028678</td>\n      <td>0.112045</td>\n      <td>0.023853</td>\n      <td>1.489534e-10</td>\n      <td>0.004825</td>\n      <td>0.112045</td>\n      <td>0.051418</td>\n      <td>0.213432</td>\n      <td>0.121317</td>\n      <td>2.979069e-10</td>\n      <td>-0.069899</td>\n      <td>0.213432</td>\n      <td>10.547077</td>\n      <td>0.028930</td>\n      <td>0.364721</td>\n      <td>4.766510e-09</td>\n      <td>10.182357</td>\n      <td>0.028930</td>\n      <td>38410.539062</td>\n      <td>38638.250000</td>\n      <td>38261.460938</td>\n      <td>38436.921875</td>\n      <td>38000.000000</td>\n      <td>37800.019531</td>\n      <td>38419.769531</td>\n      <td>38669.949219</td>\n      <td>39219.519531</td>\n      <td>39118.320312</td>\n      <td>39425.910156</td>\n      <td>39127.101562</td>\n      <td>38932.648438</td>\n      <td>38767.269531</td>\n      <td>39319.371094</td>\n      <td>39837.339844</td>\n      <td>39311.238281</td>\n      <td>39403.218750</td>\n      <td>39240.449219</td>\n      <td>39506.679688</td>\n      <td>39574.878906</td>\n      <td>38994.078125</td>\n      <td>38275.000000</td>\n      <td>38361.769531</td>\n      <td>38494.500000</td>\n      <td>37972.750000</td>\n      <td>38406.128906</td>\n      <td>38224.589844</td>\n      <td>37861.808594</td>\n      <td>37546.570312</td>\n      <td>37501.000000</td>\n      <td>37037.511719</td>\n      <td>37428.949219</td>\n      <td>37438.890625</td>\n      <td>37792.710938</td>\n      <td>37400.000000</td>\n      <td>37177.968750</td>\n      <td>37294.968750</td>\n      <td>36210.621094</td>\n      <td>35992.359375</td>\n      <td>35706.871094</td>\n      <td>34777.578125</td>\n      <td>34750.000000</td>\n      <td>34646.531250</td>\n      <td>34250.078125</td>\n      <td>34553.570312</td>\n      <td>34247.710938</td>\n      <td>34350.328125</td>\n      <td>34906.191406</td>\n      <td>34325.000000</td>\n      <td>34521.519531</td>\n      <td>34967.351562</td>\n      <td>33729.250000</td>\n      <td>33284.019531</td>\n      <td>33131.640625</td>\n      <td>33770.179688</td>\n      <td>32995.460938</td>\n      <td>33109.378906</td>\n      <td>32469.419922</td>\n      <td>34031.160156</td>\n      <td>33720.648438</td>\n      <td>34675.550781</td>\n      <td>34282.460938</td>\n      <td>34642.320312</td>\n      <td>35013.011719</td>\n      <td>34991.878906</td>\n      <td>34984.000000</td>\n      <td>33831.558594</td>\n      <td>33394.210938</td>\n      <td>33263.000000</td>\n      <td>35030.539062</td>\n      <td>35382.781250</td>\n      <td>35032.558594</td>\n      <td>35928.031250</td>\n      <td>35772.339844</td>\n      <td>36448.281250</td>\n      <td>36002.031250</td>\n      <td>35892.578125</td>\n      <td>35299.988281</td>\n      <td>34964.468750</td>\n      <td>34371.000000</td>\n      <td>34272.179688</td>\n      <td>34942.488281</td>\n      <td>35494.000000</td>\n      <td>34409.890625</td>\n      <td>34006.250000</td>\n      <td>33373.480469</td>\n      <td>32238.699219</td>\n      <td>32573.380859</td>\n      <td>32800.000000</td>\n      <td>31665.929688</td>\n      <td>31511.849609</td>\n      <td>33219.339844</td>\n      <td>32750.000000</td>\n      <td>34232.859375</td>\n      <td>34119.179688</td>\n      <td>35359.730469</td>\n      <td>35700.000000</td>\n      <td>34860.750000</td>\n      <td>35502.550781</td>\n      <td>35367.960938</td>\n      <td>36339.550781</td>\n      <td>37235.558594</td>\n      <td>37198.511719</td>\n      <td>38181.171875</td>\n      <td>38456.601562</td>\n      <td>38050.148438</td>\n      <td>37693.480469</td>\n      <td>37422.988281</td>\n      <td>38112.921875</td>\n      <td>38663.769531</td>\n      <td>39299.441406</td>\n      <td>39726.238281</td>\n      <td>39481.898438</td>\n      <td>39848.191406</td>\n      <td>39600.550781</td>\n      <td>39218.171875</td>\n      <td>40482.351562</td>\n      <td>40838.699219</td>\n      <td>40905.710938</td>\n      <td>41023.000000</td>\n      <td>41030.160156</td>\n      <td>40547.421875</td>\n      <td>40466.718750</td>\n      <td>40467.558594</td>\n      <td>41064.800781</td>\n      <td>41231.820312</td>\n      <td>40684.031250</td>\n      <td>40256.019531</td>\n      <td>40428.589844</td>\n      <td>40848.281250</td>\n      <td>40731.859375</td>\n      <td>40767.289062</td>\n      <td>40725.308594</td>\n      <td>40515.339844</td>\n      <td>40099.300781</td>\n      <td>40622.171875</td>\n      <td>40895.519531</td>\n      <td>41128.300781</td>\n      <td>40912.750000</td>\n      <td>40624.960938</td>\n      <td>40415.031250</td>\n      <td>40343.210938</td>\n      <td>40204.171875</td>\n      <td>39654.949219</td>\n      <td>38989.871094</td>\n      <td>39379.238281</td>\n      <td>40254.210938</td>\n      <td>40261.289062</td>\n      <td>39987.441406</td>\n      <td>40207.601562</td>\n      <td>40305.570312</td>\n      <td>40622.871094</td>\n      <td>40719.000000</td>\n      <td>40050.000000</td>\n      <td>39107.738281</td>\n      <td>...</td>\n      <td>41353.328125</td>\n      <td>41372.308594</td>\n      <td>41340.250000</td>\n      <td>40749.351562</td>\n      <td>39379.000000</td>\n      <td>38655.660156</td>\n      <td>38953.320312</td>\n      <td>38560.898438</td>\n      <td>38009.789062</td>\n      <td>38723.480469</td>\n      <td>38404.460938</td>\n      <td>37226.308594</td>\n      <td>38701.941406</td>\n      <td>38840.738281</td>\n      <td>39479.781250</td>\n      <td>39698.871094</td>\n      <td>39732.289062</td>\n      <td>39141.980469</td>\n      <td>39078.390625</td>\n      <td>38934.351562</td>\n      <td>39771.328125</td>\n      <td>39182.171875</td>\n      <td>39024.210938</td>\n      <td>38503.449219</td>\n      <td>38227.988281</td>\n      <td>38015.328125</td>\n      <td>37888.011719</td>\n      <td>37089.039062</td>\n      <td>37529.988281</td>\n      <td>37243.328125</td>\n      <td>36922.960938</td>\n      <td>37228.820312</td>\n      <td>37485.269531</td>\n      <td>37559.421875</td>\n      <td>37560.300781</td>\n      <td>37082.941406</td>\n      <td>36971.781250</td>\n      <td>37223.078125</td>\n      <td>36856.980469</td>\n      <td>36084.148438</td>\n      <td>35960.839844</td>\n      <td>36133.750000</td>\n      <td>34920.789062</td>\n      <td>35329.281250</td>\n      <td>35000.000000</td>\n      <td>34700.441406</td>\n      <td>34670.128906</td>\n      <td>34285.750000</td>\n      <td>34620.218750</td>\n      <td>35027.871094</td>\n      <td>35013.550781</td>\n      <td>34526.511719</td>\n      <td>33995.511719</td>\n      <td>34641.738281</td>\n      <td>34768.910156</td>\n      <td>35257.191406</td>\n      <td>35063.000000</td>\n      <td>35455.339844</td>\n      <td>34192.351562</td>\n      <td>33978.000000</td>\n      <td>33888.429688</td>\n      <td>33976.468750</td>\n      <td>34036.078125</td>\n      <td>34313.199219</td>\n      <td>33810.078125</td>\n      <td>34064.121094</td>\n      <td>33810.898438</td>\n      <td>33459.019531</td>\n      <td>32795.199219</td>\n      <td>32501.630859</td>\n      <td>32158.669922</td>\n      <td>32375.900391</td>\n      <td>31510.330078</td>\n      <td>31634.000000</td>\n      <td>31815.189453</td>\n      <td>31360.509766</td>\n      <td>31592.599609</td>\n      <td>31217.900391</td>\n      <td>30816.070312</td>\n      <td>31154.589844</td>\n      <td>30441.570312</td>\n      <td>30879.560547</td>\n      <td>31201.310547</td>\n      <td>32354.060547</td>\n      <td>32823.960938</td>\n      <td>32450.119141</td>\n      <td>32025.230469</td>\n      <td>31364.589844</td>\n      <td>31029.650391</td>\n      <td>31287.410156</td>\n      <td>31667.720703</td>\n      <td>31184.980469</td>\n      <td>31015.689453</td>\n      <td>31321.000000</td>\n      <td>31770.460938</td>\n      <td>32137.560547</td>\n      <td>31418.369141</td>\n      <td>31239.310547</td>\n      <td>30755.500000</td>\n      <td>30290.429688</td>\n      <td>31013.269531</td>\n      <td>32041.269531</td>\n      <td>32105.070312</td>\n      <td>33260.808594</td>\n      <td>32908.019531</td>\n      <td>33317.148438</td>\n      <td>33588.101562</td>\n      <td>33493.031250</td>\n      <td>33229.269531</td>\n      <td>32882.949219</td>\n      <td>33111.988281</td>\n      <td>33148.109375</td>\n      <td>33584.960938</td>\n      <td>33747.281250</td>\n      <td>32777.250000</td>\n      <td>32792.761719</td>\n      <td>32902.011719</td>\n      <td>32711.720703</td>\n      <td>33561.890625</td>\n      <td>33893.031250</td>\n      <td>33900.000000</td>\n      <td>34128.359375</td>\n      <td>34393.289062</td>\n      <td>33909.988281</td>\n      <td>34207.218750</td>\n      <td>34398.449219</td>\n      <td>34459.031250</td>\n      <td>33980.738281</td>\n      <td>33769.519531</td>\n      <td>33223.078125</td>\n      <td>32611.640625</td>\n      <td>32781.210938</td>\n      <td>32621.369141</td>\n      <td>32480.000000</td>\n      <td>32218.380859</td>\n      <td>32170.570312</td>\n      <td>31701.539062</td>\n      <td>30750.000000</td>\n      <td>33087.839844</td>\n      <td>33007.468750</td>\n      <td>32940.011719</td>\n      <td>32551.220703</td>\n      <td>31763.000000</td>\n      <td>31352.519531</td>\n      <td>31599.269531</td>\n      <td>30649.710938</td>\n      <td>29832.000000</td>\n      <td>29745.000000</td>\n      <td>29696.320312</td>\n      <td>29912.390625</td>\n      <td>29833.599609</td>\n      <td>29799.960938</td>\n      <td>29676.789062</td>\n      <td>29442.300781</td>\n      <td>29430.830078</td>\n      <td>29400.000000</td>\n      <td>29454.089844</td>\n      <td>29262.789062</td>\n      <td>29418.720703</td>\n      <td>29360.880859</td>\n      <td>29258.820312</td>\n      <td>29301.929688</td>\n      <td>29104.880859</td>\n      <td>29141.189453</td>\n      <td>29169.029297</td>\n      <td>29372.880859</td>\n      <td>29264.220703</td>\n      <td>29398.099609</td>\n      <td>29535.000000</td>\n      <td>29311.470703</td>\n      <td>29396.279297</td>\n      <td>29286.630859</td>\n      <td>29284.919922</td>\n      <td>29076.769531</td>\n      <td>31756.880859</td>\n    </tr>\n    <tr>\n      <th>2021-01-15 12:00:00+00:00</th>\n      <td>37758.960938</td>\n      <td>0.018199</td>\n      <td>0.150924</td>\n      <td>0.023853</td>\n      <td>1.489534e-10</td>\n      <td>-0.005654</td>\n      <td>0.150924</td>\n      <td>0.042761</td>\n      <td>0.174858</td>\n      <td>0.121317</td>\n      <td>2.979069e-10</td>\n      <td>-0.078556</td>\n      <td>0.174858</td>\n      <td>10.538978</td>\n      <td>0.018940</td>\n      <td>0.364721</td>\n      <td>4.766510e-09</td>\n      <td>10.174258</td>\n      <td>0.018940</td>\n      <td>38066.011719</td>\n      <td>38410.539062</td>\n      <td>38638.250000</td>\n      <td>38261.460938</td>\n      <td>38436.921875</td>\n      <td>38000.000000</td>\n      <td>37800.019531</td>\n      <td>38419.769531</td>\n      <td>38669.949219</td>\n      <td>39219.519531</td>\n      <td>39118.320312</td>\n      <td>39425.910156</td>\n      <td>39127.101562</td>\n      <td>38932.648438</td>\n      <td>38767.269531</td>\n      <td>39319.371094</td>\n      <td>39837.339844</td>\n      <td>39311.238281</td>\n      <td>39403.218750</td>\n      <td>39240.449219</td>\n      <td>39506.679688</td>\n      <td>39574.878906</td>\n      <td>38994.078125</td>\n      <td>38275.000000</td>\n      <td>38361.769531</td>\n      <td>38494.500000</td>\n      <td>37972.750000</td>\n      <td>38406.128906</td>\n      <td>38224.589844</td>\n      <td>37861.808594</td>\n      <td>37546.570312</td>\n      <td>37501.000000</td>\n      <td>37037.511719</td>\n      <td>37428.949219</td>\n      <td>37438.890625</td>\n      <td>37792.710938</td>\n      <td>37400.000000</td>\n      <td>37177.968750</td>\n      <td>37294.968750</td>\n      <td>36210.621094</td>\n      <td>35992.359375</td>\n      <td>35706.871094</td>\n      <td>34777.578125</td>\n      <td>34750.000000</td>\n      <td>34646.531250</td>\n      <td>34250.078125</td>\n      <td>34553.570312</td>\n      <td>34247.710938</td>\n      <td>34350.328125</td>\n      <td>34906.191406</td>\n      <td>34325.000000</td>\n      <td>34521.519531</td>\n      <td>34967.351562</td>\n      <td>33729.250000</td>\n      <td>33284.019531</td>\n      <td>33131.640625</td>\n      <td>33770.179688</td>\n      <td>32995.460938</td>\n      <td>33109.378906</td>\n      <td>32469.419922</td>\n      <td>34031.160156</td>\n      <td>33720.648438</td>\n      <td>34675.550781</td>\n      <td>34282.460938</td>\n      <td>34642.320312</td>\n      <td>35013.011719</td>\n      <td>34991.878906</td>\n      <td>34984.000000</td>\n      <td>33831.558594</td>\n      <td>33394.210938</td>\n      <td>33263.000000</td>\n      <td>35030.539062</td>\n      <td>35382.781250</td>\n      <td>35032.558594</td>\n      <td>35928.031250</td>\n      <td>35772.339844</td>\n      <td>36448.281250</td>\n      <td>36002.031250</td>\n      <td>35892.578125</td>\n      <td>35299.988281</td>\n      <td>34964.468750</td>\n      <td>34371.000000</td>\n      <td>34272.179688</td>\n      <td>34942.488281</td>\n      <td>35494.000000</td>\n      <td>34409.890625</td>\n      <td>34006.250000</td>\n      <td>33373.480469</td>\n      <td>32238.699219</td>\n      <td>32573.380859</td>\n      <td>32800.000000</td>\n      <td>31665.929688</td>\n      <td>31511.849609</td>\n      <td>33219.339844</td>\n      <td>32750.000000</td>\n      <td>34232.859375</td>\n      <td>34119.179688</td>\n      <td>35359.730469</td>\n      <td>35700.000000</td>\n      <td>34860.750000</td>\n      <td>35502.550781</td>\n      <td>35367.960938</td>\n      <td>36339.550781</td>\n      <td>37235.558594</td>\n      <td>37198.511719</td>\n      <td>38181.171875</td>\n      <td>38456.601562</td>\n      <td>38050.148438</td>\n      <td>37693.480469</td>\n      <td>37422.988281</td>\n      <td>38112.921875</td>\n      <td>38663.769531</td>\n      <td>39299.441406</td>\n      <td>39726.238281</td>\n      <td>39481.898438</td>\n      <td>39848.191406</td>\n      <td>39600.550781</td>\n      <td>39218.171875</td>\n      <td>40482.351562</td>\n      <td>40838.699219</td>\n      <td>40905.710938</td>\n      <td>41023.000000</td>\n      <td>41030.160156</td>\n      <td>40547.421875</td>\n      <td>40466.718750</td>\n      <td>40467.558594</td>\n      <td>41064.800781</td>\n      <td>41231.820312</td>\n      <td>40684.031250</td>\n      <td>40256.019531</td>\n      <td>40428.589844</td>\n      <td>40848.281250</td>\n      <td>40731.859375</td>\n      <td>40767.289062</td>\n      <td>40725.308594</td>\n      <td>40515.339844</td>\n      <td>40099.300781</td>\n      <td>40622.171875</td>\n      <td>40895.519531</td>\n      <td>41128.300781</td>\n      <td>40912.750000</td>\n      <td>40624.960938</td>\n      <td>40415.031250</td>\n      <td>40343.210938</td>\n      <td>40204.171875</td>\n      <td>39654.949219</td>\n      <td>38989.871094</td>\n      <td>39379.238281</td>\n      <td>40254.210938</td>\n      <td>40261.289062</td>\n      <td>39987.441406</td>\n      <td>40207.601562</td>\n      <td>40305.570312</td>\n      <td>40622.871094</td>\n      <td>40719.000000</td>\n      <td>40050.000000</td>\n      <td>...</td>\n      <td>41512.871094</td>\n      <td>41353.328125</td>\n      <td>41372.308594</td>\n      <td>41340.250000</td>\n      <td>40749.351562</td>\n      <td>39379.000000</td>\n      <td>38655.660156</td>\n      <td>38953.320312</td>\n      <td>38560.898438</td>\n      <td>38009.789062</td>\n      <td>38723.480469</td>\n      <td>38404.460938</td>\n      <td>37226.308594</td>\n      <td>38701.941406</td>\n      <td>38840.738281</td>\n      <td>39479.781250</td>\n      <td>39698.871094</td>\n      <td>39732.289062</td>\n      <td>39141.980469</td>\n      <td>39078.390625</td>\n      <td>38934.351562</td>\n      <td>39771.328125</td>\n      <td>39182.171875</td>\n      <td>39024.210938</td>\n      <td>38503.449219</td>\n      <td>38227.988281</td>\n      <td>38015.328125</td>\n      <td>37888.011719</td>\n      <td>37089.039062</td>\n      <td>37529.988281</td>\n      <td>37243.328125</td>\n      <td>36922.960938</td>\n      <td>37228.820312</td>\n      <td>37485.269531</td>\n      <td>37559.421875</td>\n      <td>37560.300781</td>\n      <td>37082.941406</td>\n      <td>36971.781250</td>\n      <td>37223.078125</td>\n      <td>36856.980469</td>\n      <td>36084.148438</td>\n      <td>35960.839844</td>\n      <td>36133.750000</td>\n      <td>34920.789062</td>\n      <td>35329.281250</td>\n      <td>35000.000000</td>\n      <td>34700.441406</td>\n      <td>34670.128906</td>\n      <td>34285.750000</td>\n      <td>34620.218750</td>\n      <td>35027.871094</td>\n      <td>35013.550781</td>\n      <td>34526.511719</td>\n      <td>33995.511719</td>\n      <td>34641.738281</td>\n      <td>34768.910156</td>\n      <td>35257.191406</td>\n      <td>35063.000000</td>\n      <td>35455.339844</td>\n      <td>34192.351562</td>\n      <td>33978.000000</td>\n      <td>33888.429688</td>\n      <td>33976.468750</td>\n      <td>34036.078125</td>\n      <td>34313.199219</td>\n      <td>33810.078125</td>\n      <td>34064.121094</td>\n      <td>33810.898438</td>\n      <td>33459.019531</td>\n      <td>32795.199219</td>\n      <td>32501.630859</td>\n      <td>32158.669922</td>\n      <td>32375.900391</td>\n      <td>31510.330078</td>\n      <td>31634.000000</td>\n      <td>31815.189453</td>\n      <td>31360.509766</td>\n      <td>31592.599609</td>\n      <td>31217.900391</td>\n      <td>30816.070312</td>\n      <td>31154.589844</td>\n      <td>30441.570312</td>\n      <td>30879.560547</td>\n      <td>31201.310547</td>\n      <td>32354.060547</td>\n      <td>32823.960938</td>\n      <td>32450.119141</td>\n      <td>32025.230469</td>\n      <td>31364.589844</td>\n      <td>31029.650391</td>\n      <td>31287.410156</td>\n      <td>31667.720703</td>\n      <td>31184.980469</td>\n      <td>31015.689453</td>\n      <td>31321.000000</td>\n      <td>31770.460938</td>\n      <td>32137.560547</td>\n      <td>31418.369141</td>\n      <td>31239.310547</td>\n      <td>30755.500000</td>\n      <td>30290.429688</td>\n      <td>31013.269531</td>\n      <td>32041.269531</td>\n      <td>32105.070312</td>\n      <td>33260.808594</td>\n      <td>32908.019531</td>\n      <td>33317.148438</td>\n      <td>33588.101562</td>\n      <td>33493.031250</td>\n      <td>33229.269531</td>\n      <td>32882.949219</td>\n      <td>33111.988281</td>\n      <td>33148.109375</td>\n      <td>33584.960938</td>\n      <td>33747.281250</td>\n      <td>32777.250000</td>\n      <td>32792.761719</td>\n      <td>32902.011719</td>\n      <td>32711.720703</td>\n      <td>33561.890625</td>\n      <td>33893.031250</td>\n      <td>33900.000000</td>\n      <td>34128.359375</td>\n      <td>34393.289062</td>\n      <td>33909.988281</td>\n      <td>34207.218750</td>\n      <td>34398.449219</td>\n      <td>34459.031250</td>\n      <td>33980.738281</td>\n      <td>33769.519531</td>\n      <td>33223.078125</td>\n      <td>32611.640625</td>\n      <td>32781.210938</td>\n      <td>32621.369141</td>\n      <td>32480.000000</td>\n      <td>32218.380859</td>\n      <td>32170.570312</td>\n      <td>31701.539062</td>\n      <td>30750.000000</td>\n      <td>33087.839844</td>\n      <td>33007.468750</td>\n      <td>32940.011719</td>\n      <td>32551.220703</td>\n      <td>31763.000000</td>\n      <td>31352.519531</td>\n      <td>31599.269531</td>\n      <td>30649.710938</td>\n      <td>29832.000000</td>\n      <td>29745.000000</td>\n      <td>29696.320312</td>\n      <td>29912.390625</td>\n      <td>29833.599609</td>\n      <td>29799.960938</td>\n      <td>29676.789062</td>\n      <td>29442.300781</td>\n      <td>29430.830078</td>\n      <td>29400.000000</td>\n      <td>29454.089844</td>\n      <td>29262.789062</td>\n      <td>29418.720703</td>\n      <td>29360.880859</td>\n      <td>29258.820312</td>\n      <td>29301.929688</td>\n      <td>29104.880859</td>\n      <td>29141.189453</td>\n      <td>29169.029297</td>\n      <td>29372.880859</td>\n      <td>29264.220703</td>\n      <td>29398.099609</td>\n      <td>29535.000000</td>\n      <td>29311.470703</td>\n      <td>29396.279297</td>\n      <td>29286.630859</td>\n      <td>29284.919922</td>\n      <td>31563.599609</td>\n    </tr>\n    <tr>\n      <th>2021-01-15 13:00:00+00:00</th>\n      <td>37635.718750</td>\n      <td>0.012897</td>\n      <td>0.128137</td>\n      <td>0.023853</td>\n      <td>1.489534e-10</td>\n      <td>-0.010956</td>\n      <td>0.128137</td>\n      <td>0.038988</td>\n      <td>0.156902</td>\n      <td>0.121317</td>\n      <td>2.979069e-10</td>\n      <td>-0.082329</td>\n      <td>0.156902</td>\n      <td>10.535709</td>\n      <td>0.016423</td>\n      <td>0.364721</td>\n      <td>4.766510e-09</td>\n      <td>10.170988</td>\n      <td>0.016423</td>\n      <td>37758.960938</td>\n      <td>38066.011719</td>\n      <td>38410.539062</td>\n      <td>38638.250000</td>\n      <td>38261.460938</td>\n      <td>38436.921875</td>\n      <td>38000.000000</td>\n      <td>37800.019531</td>\n      <td>38419.769531</td>\n      <td>38669.949219</td>\n      <td>39219.519531</td>\n      <td>39118.320312</td>\n      <td>39425.910156</td>\n      <td>39127.101562</td>\n      <td>38932.648438</td>\n      <td>38767.269531</td>\n      <td>39319.371094</td>\n      <td>39837.339844</td>\n      <td>39311.238281</td>\n      <td>39403.218750</td>\n      <td>39240.449219</td>\n      <td>39506.679688</td>\n      <td>39574.878906</td>\n      <td>38994.078125</td>\n      <td>38275.000000</td>\n      <td>38361.769531</td>\n      <td>38494.500000</td>\n      <td>37972.750000</td>\n      <td>38406.128906</td>\n      <td>38224.589844</td>\n      <td>37861.808594</td>\n      <td>37546.570312</td>\n      <td>37501.000000</td>\n      <td>37037.511719</td>\n      <td>37428.949219</td>\n      <td>37438.890625</td>\n      <td>37792.710938</td>\n      <td>37400.000000</td>\n      <td>37177.968750</td>\n      <td>37294.968750</td>\n      <td>36210.621094</td>\n      <td>35992.359375</td>\n      <td>35706.871094</td>\n      <td>34777.578125</td>\n      <td>34750.000000</td>\n      <td>34646.531250</td>\n      <td>34250.078125</td>\n      <td>34553.570312</td>\n      <td>34247.710938</td>\n      <td>34350.328125</td>\n      <td>34906.191406</td>\n      <td>34325.000000</td>\n      <td>34521.519531</td>\n      <td>34967.351562</td>\n      <td>33729.250000</td>\n      <td>33284.019531</td>\n      <td>33131.640625</td>\n      <td>33770.179688</td>\n      <td>32995.460938</td>\n      <td>33109.378906</td>\n      <td>32469.419922</td>\n      <td>34031.160156</td>\n      <td>33720.648438</td>\n      <td>34675.550781</td>\n      <td>34282.460938</td>\n      <td>34642.320312</td>\n      <td>35013.011719</td>\n      <td>34991.878906</td>\n      <td>34984.000000</td>\n      <td>33831.558594</td>\n      <td>33394.210938</td>\n      <td>33263.000000</td>\n      <td>35030.539062</td>\n      <td>35382.781250</td>\n      <td>35032.558594</td>\n      <td>35928.031250</td>\n      <td>35772.339844</td>\n      <td>36448.281250</td>\n      <td>36002.031250</td>\n      <td>35892.578125</td>\n      <td>35299.988281</td>\n      <td>34964.468750</td>\n      <td>34371.000000</td>\n      <td>34272.179688</td>\n      <td>34942.488281</td>\n      <td>35494.000000</td>\n      <td>34409.890625</td>\n      <td>34006.250000</td>\n      <td>33373.480469</td>\n      <td>32238.699219</td>\n      <td>32573.380859</td>\n      <td>32800.000000</td>\n      <td>31665.929688</td>\n      <td>31511.849609</td>\n      <td>33219.339844</td>\n      <td>32750.000000</td>\n      <td>34232.859375</td>\n      <td>34119.179688</td>\n      <td>35359.730469</td>\n      <td>35700.000000</td>\n      <td>34860.750000</td>\n      <td>35502.550781</td>\n      <td>35367.960938</td>\n      <td>36339.550781</td>\n      <td>37235.558594</td>\n      <td>37198.511719</td>\n      <td>38181.171875</td>\n      <td>38456.601562</td>\n      <td>38050.148438</td>\n      <td>37693.480469</td>\n      <td>37422.988281</td>\n      <td>38112.921875</td>\n      <td>38663.769531</td>\n      <td>39299.441406</td>\n      <td>39726.238281</td>\n      <td>39481.898438</td>\n      <td>39848.191406</td>\n      <td>39600.550781</td>\n      <td>39218.171875</td>\n      <td>40482.351562</td>\n      <td>40838.699219</td>\n      <td>40905.710938</td>\n      <td>41023.000000</td>\n      <td>41030.160156</td>\n      <td>40547.421875</td>\n      <td>40466.718750</td>\n      <td>40467.558594</td>\n      <td>41064.800781</td>\n      <td>41231.820312</td>\n      <td>40684.031250</td>\n      <td>40256.019531</td>\n      <td>40428.589844</td>\n      <td>40848.281250</td>\n      <td>40731.859375</td>\n      <td>40767.289062</td>\n      <td>40725.308594</td>\n      <td>40515.339844</td>\n      <td>40099.300781</td>\n      <td>40622.171875</td>\n      <td>40895.519531</td>\n      <td>41128.300781</td>\n      <td>40912.750000</td>\n      <td>40624.960938</td>\n      <td>40415.031250</td>\n      <td>40343.210938</td>\n      <td>40204.171875</td>\n      <td>39654.949219</td>\n      <td>38989.871094</td>\n      <td>39379.238281</td>\n      <td>40254.210938</td>\n      <td>40261.289062</td>\n      <td>39987.441406</td>\n      <td>40207.601562</td>\n      <td>40305.570312</td>\n      <td>40622.871094</td>\n      <td>40719.000000</td>\n      <td>...</td>\n      <td>40885.691406</td>\n      <td>41512.871094</td>\n      <td>41353.328125</td>\n      <td>41372.308594</td>\n      <td>41340.250000</td>\n      <td>40749.351562</td>\n      <td>39379.000000</td>\n      <td>38655.660156</td>\n      <td>38953.320312</td>\n      <td>38560.898438</td>\n      <td>38009.789062</td>\n      <td>38723.480469</td>\n      <td>38404.460938</td>\n      <td>37226.308594</td>\n      <td>38701.941406</td>\n      <td>38840.738281</td>\n      <td>39479.781250</td>\n      <td>39698.871094</td>\n      <td>39732.289062</td>\n      <td>39141.980469</td>\n      <td>39078.390625</td>\n      <td>38934.351562</td>\n      <td>39771.328125</td>\n      <td>39182.171875</td>\n      <td>39024.210938</td>\n      <td>38503.449219</td>\n      <td>38227.988281</td>\n      <td>38015.328125</td>\n      <td>37888.011719</td>\n      <td>37089.039062</td>\n      <td>37529.988281</td>\n      <td>37243.328125</td>\n      <td>36922.960938</td>\n      <td>37228.820312</td>\n      <td>37485.269531</td>\n      <td>37559.421875</td>\n      <td>37560.300781</td>\n      <td>37082.941406</td>\n      <td>36971.781250</td>\n      <td>37223.078125</td>\n      <td>36856.980469</td>\n      <td>36084.148438</td>\n      <td>35960.839844</td>\n      <td>36133.750000</td>\n      <td>34920.789062</td>\n      <td>35329.281250</td>\n      <td>35000.000000</td>\n      <td>34700.441406</td>\n      <td>34670.128906</td>\n      <td>34285.750000</td>\n      <td>34620.218750</td>\n      <td>35027.871094</td>\n      <td>35013.550781</td>\n      <td>34526.511719</td>\n      <td>33995.511719</td>\n      <td>34641.738281</td>\n      <td>34768.910156</td>\n      <td>35257.191406</td>\n      <td>35063.000000</td>\n      <td>35455.339844</td>\n      <td>34192.351562</td>\n      <td>33978.000000</td>\n      <td>33888.429688</td>\n      <td>33976.468750</td>\n      <td>34036.078125</td>\n      <td>34313.199219</td>\n      <td>33810.078125</td>\n      <td>34064.121094</td>\n      <td>33810.898438</td>\n      <td>33459.019531</td>\n      <td>32795.199219</td>\n      <td>32501.630859</td>\n      <td>32158.669922</td>\n      <td>32375.900391</td>\n      <td>31510.330078</td>\n      <td>31634.000000</td>\n      <td>31815.189453</td>\n      <td>31360.509766</td>\n      <td>31592.599609</td>\n      <td>31217.900391</td>\n      <td>30816.070312</td>\n      <td>31154.589844</td>\n      <td>30441.570312</td>\n      <td>30879.560547</td>\n      <td>31201.310547</td>\n      <td>32354.060547</td>\n      <td>32823.960938</td>\n      <td>32450.119141</td>\n      <td>32025.230469</td>\n      <td>31364.589844</td>\n      <td>31029.650391</td>\n      <td>31287.410156</td>\n      <td>31667.720703</td>\n      <td>31184.980469</td>\n      <td>31015.689453</td>\n      <td>31321.000000</td>\n      <td>31770.460938</td>\n      <td>32137.560547</td>\n      <td>31418.369141</td>\n      <td>31239.310547</td>\n      <td>30755.500000</td>\n      <td>30290.429688</td>\n      <td>31013.269531</td>\n      <td>32041.269531</td>\n      <td>32105.070312</td>\n      <td>33260.808594</td>\n      <td>32908.019531</td>\n      <td>33317.148438</td>\n      <td>33588.101562</td>\n      <td>33493.031250</td>\n      <td>33229.269531</td>\n      <td>32882.949219</td>\n      <td>33111.988281</td>\n      <td>33148.109375</td>\n      <td>33584.960938</td>\n      <td>33747.281250</td>\n      <td>32777.250000</td>\n      <td>32792.761719</td>\n      <td>32902.011719</td>\n      <td>32711.720703</td>\n      <td>33561.890625</td>\n      <td>33893.031250</td>\n      <td>33900.000000</td>\n      <td>34128.359375</td>\n      <td>34393.289062</td>\n      <td>33909.988281</td>\n      <td>34207.218750</td>\n      <td>34398.449219</td>\n      <td>34459.031250</td>\n      <td>33980.738281</td>\n      <td>33769.519531</td>\n      <td>33223.078125</td>\n      <td>32611.640625</td>\n      <td>32781.210938</td>\n      <td>32621.369141</td>\n      <td>32480.000000</td>\n      <td>32218.380859</td>\n      <td>32170.570312</td>\n      <td>31701.539062</td>\n      <td>30750.000000</td>\n      <td>33087.839844</td>\n      <td>33007.468750</td>\n      <td>32940.011719</td>\n      <td>32551.220703</td>\n      <td>31763.000000</td>\n      <td>31352.519531</td>\n      <td>31599.269531</td>\n      <td>30649.710938</td>\n      <td>29832.000000</td>\n      <td>29745.000000</td>\n      <td>29696.320312</td>\n      <td>29912.390625</td>\n      <td>29833.599609</td>\n      <td>29799.960938</td>\n      <td>29676.789062</td>\n      <td>29442.300781</td>\n      <td>29430.830078</td>\n      <td>29400.000000</td>\n      <td>29454.089844</td>\n      <td>29262.789062</td>\n      <td>29418.720703</td>\n      <td>29360.880859</td>\n      <td>29258.820312</td>\n      <td>29301.929688</td>\n      <td>29104.880859</td>\n      <td>29141.189453</td>\n      <td>29169.029297</td>\n      <td>29372.880859</td>\n      <td>29264.220703</td>\n      <td>29398.099609</td>\n      <td>29535.000000</td>\n      <td>29311.470703</td>\n      <td>29396.279297</td>\n      <td>29286.630859</td>\n      <td>32195.539062</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2023-11-27 13:00:00+00:00</th>\n      <td>36955.343750</td>\n      <td>-0.017255</td>\n      <td>-0.022997</td>\n      <td>0.023853</td>\n      <td>1.489534e-10</td>\n      <td>-0.041108</td>\n      <td>-0.022997</td>\n      <td>-0.002428</td>\n      <td>0.040427</td>\n      <td>0.121317</td>\n      <td>2.979069e-10</td>\n      <td>-0.123745</td>\n      <td>0.040427</td>\n      <td>0.042008</td>\n      <td>0.224267</td>\n      <td>0.364721</td>\n      <td>4.766510e-09</td>\n      <td>-0.322713</td>\n      <td>0.224267</td>\n      <td>36992.609375</td>\n      <td>37046.425781</td>\n      <td>37110.019531</td>\n      <td>37386.000000</td>\n      <td>37351.371094</td>\n      <td>37293.785156</td>\n      <td>37295.492188</td>\n      <td>37315.109375</td>\n      <td>37382.367188</td>\n      <td>37319.820312</td>\n      <td>37318.031250</td>\n      <td>37511.992188</td>\n      <td>37500.851562</td>\n      <td>37460.746094</td>\n      <td>37674.539062</td>\n      <td>37622.535156</td>\n      <td>37600.101562</td>\n      <td>37400.328125</td>\n      <td>37261.785156</td>\n      <td>37287.253906</td>\n      <td>37245.613281</td>\n      <td>37453.195312</td>\n      <td>37369.542969</td>\n      <td>37548.851562</td>\n      <td>37648.285156</td>\n      <td>37712.632812</td>\n      <td>37721.714844</td>\n      <td>37784.953125</td>\n      <td>37782.777344</td>\n      <td>37823.695312</td>\n      <td>37808.441406</td>\n      <td>37803.851562</td>\n      <td>37790.101562</td>\n      <td>37763.789062</td>\n      <td>37778.171875</td>\n      <td>37737.050781</td>\n      <td>37774.500000</td>\n      <td>37806.699219</td>\n      <td>37752.734375</td>\n      <td>37828.062500</td>\n      <td>37826.925781</td>\n      <td>37846.503906</td>\n      <td>37845.960938</td>\n      <td>37793.910156</td>\n      <td>37769.109375</td>\n      <td>37725.953125</td>\n      <td>37710.105469</td>\n      <td>37742.480469</td>\n      <td>37749.714844</td>\n      <td>37676.199219</td>\n      <td>37708.832031</td>\n      <td>37767.398438</td>\n      <td>37785.714844</td>\n      <td>37816.875000</td>\n      <td>37815.179688</td>\n      <td>37829.914062</td>\n      <td>37812.269531</td>\n      <td>37818.269531</td>\n      <td>37850.578125</td>\n      <td>37815.070312</td>\n      <td>37741.773438</td>\n      <td>37740.777344</td>\n      <td>37720.859375</td>\n      <td>37857.000000</td>\n      <td>37849.980469</td>\n      <td>37797.285156</td>\n      <td>37722.636719</td>\n      <td>37866.453125</td>\n      <td>37746.105469</td>\n      <td>38265.125000</td>\n      <td>37815.886719</td>\n      <td>37797.679688</td>\n      <td>37744.050781</td>\n      <td>37756.984375</td>\n      <td>37606.589844</td>\n      <td>37627.941406</td>\n      <td>37445.910156</td>\n      <td>37551.734375</td>\n      <td>37394.496094</td>\n      <td>37440.757812</td>\n      <td>37361.238281</td>\n      <td>37338.574219</td>\n      <td>37391.601562</td>\n      <td>37413.457031</td>\n      <td>37304.656250</td>\n      <td>37301.031250</td>\n      <td>37314.050781</td>\n      <td>37261.746094</td>\n      <td>37290.398438</td>\n      <td>37289.148438</td>\n      <td>37260.070312</td>\n      <td>37180.660156</td>\n      <td>37099.250000</td>\n      <td>36956.265625</td>\n      <td>37217.929688</td>\n      <td>37243.300781</td>\n      <td>37308.710938</td>\n      <td>37331.148438</td>\n      <td>37473.097656</td>\n      <td>37511.054688</td>\n      <td>37500.316406</td>\n      <td>37385.480469</td>\n      <td>37295.429688</td>\n      <td>37346.011719</td>\n      <td>37358.363281</td>\n      <td>37322.140625</td>\n      <td>37356.648438</td>\n      <td>37434.425781</td>\n      <td>37345.921875</td>\n      <td>37429.453125</td>\n      <td>37475.507812</td>\n      <td>37618.648438</td>\n      <td>37638.851562</td>\n      <td>37291.343750</td>\n      <td>37203.867188</td>\n      <td>36589.949219</td>\n      <td>36406.671875</td>\n      <td>36492.765625</td>\n      <td>36404.410156</td>\n      <td>36613.843750</td>\n      <td>36570.613281</td>\n      <td>36447.683594</td>\n      <td>36681.859375</td>\n      <td>36652.515625</td>\n      <td>36548.500000</td>\n      <td>36461.550781</td>\n      <td>36525.898438</td>\n      <td>36372.601562</td>\n      <td>36436.886719</td>\n      <td>36374.535156</td>\n      <td>36302.617188</td>\n      <td>36086.960938</td>\n      <td>36115.984375</td>\n      <td>35814.101562</td>\n      <td>36406.289062</td>\n      <td>36838.941406</td>\n      <td>36940.218750</td>\n      <td>36991.875000</td>\n      <td>36893.941406</td>\n      <td>36930.085938</td>\n      <td>37209.886719</td>\n      <td>37008.648438</td>\n      <td>36648.074219</td>\n      <td>37034.703125</td>\n      <td>37155.750000</td>\n      <td>37191.179688</td>\n      <td>37380.101562</td>\n      <td>37324.824219</td>\n      <td>37417.746094</td>\n      <td>37296.796875</td>\n      <td>37393.140625</td>\n      <td>37375.539062</td>\n      <td>37417.441406</td>\n      <td>37488.855469</td>\n      <td>37456.285156</td>\n      <td>37645.496094</td>\n      <td>...</td>\n      <td>37547.015625</td>\n      <td>37600.449219</td>\n      <td>37258.398438</td>\n      <td>37468.058594</td>\n      <td>37440.609375</td>\n      <td>37112.296875</td>\n      <td>37171.574219</td>\n      <td>37190.757812</td>\n      <td>37302.925781</td>\n      <td>37217.878906</td>\n      <td>37148.933594</td>\n      <td>37253.976562</td>\n      <td>37204.277344</td>\n      <td>37157.253906</td>\n      <td>37220.949219</td>\n      <td>37131.574219</td>\n      <td>37334.664062</td>\n      <td>37317.066406</td>\n      <td>37306.523438</td>\n      <td>37387.921875</td>\n      <td>36992.503906</td>\n      <td>37009.546875</td>\n      <td>36948.828125</td>\n      <td>36965.484375</td>\n      <td>36855.968750</td>\n      <td>36609.250000</td>\n      <td>36581.687500</td>\n      <td>36577.316406</td>\n      <td>36460.593750</td>\n      <td>36507.871094</td>\n      <td>36514.593750</td>\n      <td>36482.050781</td>\n      <td>36567.250000</td>\n      <td>36656.121094</td>\n      <td>36644.832031</td>\n      <td>36628.085938</td>\n      <td>36637.300781</td>\n      <td>36534.128906</td>\n      <td>36567.628906</td>\n      <td>36554.750000</td>\n      <td>36487.917969</td>\n      <td>36515.101562</td>\n      <td>36492.691406</td>\n      <td>36592.660156</td>\n      <td>36522.640625</td>\n      <td>36569.359375</td>\n      <td>36625.449219</td>\n      <td>36703.609375</td>\n      <td>36749.742188</td>\n      <td>36648.488281</td>\n      <td>36695.476562</td>\n      <td>36674.894531</td>\n      <td>36521.394531</td>\n      <td>36454.929688</td>\n      <td>36478.910156</td>\n      <td>36422.441406</td>\n      <td>36480.339844</td>\n      <td>36413.179688</td>\n      <td>36468.468750</td>\n      <td>36406.851562</td>\n      <td>36278.894531</td>\n      <td>36360.625000</td>\n      <td>36409.949219</td>\n      <td>36390.769531</td>\n      <td>36428.011719</td>\n      <td>36423.769531</td>\n      <td>36418.550781</td>\n      <td>36629.398438</td>\n      <td>36552.156250</td>\n      <td>36417.292969</td>\n      <td>36492.996094</td>\n      <td>36323.144531</td>\n      <td>36485.074219</td>\n      <td>36498.621094</td>\n      <td>35990.039062</td>\n      <td>36042.480469</td>\n      <td>36015.324219</td>\n      <td>36352.300781</td>\n      <td>36528.421875</td>\n      <td>36398.250000</td>\n      <td>36374.000000</td>\n      <td>36289.562500</td>\n      <td>36401.597656</td>\n      <td>36252.414062</td>\n      <td>36376.671875</td>\n      <td>36345.585938</td>\n      <td>36441.394531</td>\n      <td>36496.632812</td>\n      <td>36453.898438</td>\n      <td>36556.718750</td>\n      <td>36367.058594</td>\n      <td>36168.449219</td>\n      <td>36226.828125</td>\n      <td>35963.859375</td>\n      <td>35960.750000</td>\n      <td>35921.035156</td>\n      <td>36250.046875</td>\n      <td>36317.250000</td>\n      <td>36594.953125</td>\n      <td>36429.351562</td>\n      <td>36761.156250</td>\n      <td>36789.429688</td>\n      <td>37136.937500</td>\n      <td>37204.390625</td>\n      <td>37303.949219</td>\n      <td>37367.195312</td>\n      <td>37328.851562</td>\n      <td>37458.226562</td>\n      <td>37568.496094</td>\n      <td>37466.882812</td>\n      <td>37455.558594</td>\n      <td>37473.175781</td>\n      <td>37524.074219</td>\n      <td>37448.699219</td>\n      <td>37785.699219</td>\n      <td>37900.609375</td>\n      <td>37722.441406</td>\n      <td>37647.261719</td>\n      <td>37569.449219</td>\n      <td>37513.953125</td>\n      <td>37388.851562</td>\n      <td>36500.656250</td>\n      <td>36385.816406</td>\n      <td>36398.691406</td>\n      <td>36132.300781</td>\n      <td>36099.589844</td>\n      <td>36225.386719</td>\n      <td>36246.609375</td>\n      <td>35884.214844</td>\n      <td>35794.199219</td>\n      <td>35801.464844</td>\n      <td>35595.703125</td>\n      <td>35594.414062</td>\n      <td>35620.476562</td>\n      <td>35580.953125</td>\n      <td>35400.820312</td>\n      <td>35487.273438</td>\n      <td>35491.156250</td>\n      <td>35502.875000</td>\n      <td>35551.351562</td>\n      <td>35612.113281</td>\n      <td>35575.347656</td>\n      <td>35291.933594</td>\n      <td>35294.433594</td>\n      <td>35240.332031</td>\n      <td>36083.226562</td>\n      <td>36274.691406</td>\n      <td>36142.460938</td>\n      <td>36617.410156</td>\n      <td>36528.296875</td>\n      <td>36365.453125</td>\n      <td>36279.300781</td>\n      <td>36515.265625</td>\n      <td>36490.078125</td>\n      <td>36742.324219</td>\n      <td>36698.449219</td>\n      <td>36666.105469</td>\n      <td>36641.476562</td>\n      <td>36498.023438</td>\n      <td>36463.804688</td>\n      <td>36442.343750</td>\n      <td>36340.074219</td>\n      <td>36256.136719</td>\n      <td>36541.910156</td>\n      <td>36477.402344</td>\n      <td>36495.328125</td>\n      <td>36800.109375</td>\n      <td>36862.921875</td>\n      <td>36760.453125</td>\n      <td>36657.753906</td>\n      <td>36861.910156</td>\n      <td>36862.207031</td>\n      <td>36806.250000</td>\n      <td>36837.101562</td>\n      <td>41753.480469</td>\n    </tr>\n    <tr>\n      <th>2023-11-27 14:00:00+00:00</th>\n      <td>36807.449219</td>\n      <td>-0.020803</td>\n      <td>-0.027946</td>\n      <td>0.023853</td>\n      <td>1.489534e-10</td>\n      <td>-0.044656</td>\n      <td>-0.027946</td>\n      <td>-0.006557</td>\n      <td>0.036526</td>\n      <td>0.121317</td>\n      <td>2.979069e-10</td>\n      <td>-0.127873</td>\n      <td>0.036526</td>\n      <td>0.037769</td>\n      <td>0.221354</td>\n      <td>0.364721</td>\n      <td>4.766510e-09</td>\n      <td>-0.326952</td>\n      <td>0.221354</td>\n      <td>36955.343750</td>\n      <td>36992.609375</td>\n      <td>37046.425781</td>\n      <td>37110.019531</td>\n      <td>37386.000000</td>\n      <td>37351.371094</td>\n      <td>37293.785156</td>\n      <td>37295.492188</td>\n      <td>37315.109375</td>\n      <td>37382.367188</td>\n      <td>37319.820312</td>\n      <td>37318.031250</td>\n      <td>37511.992188</td>\n      <td>37500.851562</td>\n      <td>37460.746094</td>\n      <td>37674.539062</td>\n      <td>37622.535156</td>\n      <td>37600.101562</td>\n      <td>37400.328125</td>\n      <td>37261.785156</td>\n      <td>37287.253906</td>\n      <td>37245.613281</td>\n      <td>37453.195312</td>\n      <td>37369.542969</td>\n      <td>37548.851562</td>\n      <td>37648.285156</td>\n      <td>37712.632812</td>\n      <td>37721.714844</td>\n      <td>37784.953125</td>\n      <td>37782.777344</td>\n      <td>37823.695312</td>\n      <td>37808.441406</td>\n      <td>37803.851562</td>\n      <td>37790.101562</td>\n      <td>37763.789062</td>\n      <td>37778.171875</td>\n      <td>37737.050781</td>\n      <td>37774.500000</td>\n      <td>37806.699219</td>\n      <td>37752.734375</td>\n      <td>37828.062500</td>\n      <td>37826.925781</td>\n      <td>37846.503906</td>\n      <td>37845.960938</td>\n      <td>37793.910156</td>\n      <td>37769.109375</td>\n      <td>37725.953125</td>\n      <td>37710.105469</td>\n      <td>37742.480469</td>\n      <td>37749.714844</td>\n      <td>37676.199219</td>\n      <td>37708.832031</td>\n      <td>37767.398438</td>\n      <td>37785.714844</td>\n      <td>37816.875000</td>\n      <td>37815.179688</td>\n      <td>37829.914062</td>\n      <td>37812.269531</td>\n      <td>37818.269531</td>\n      <td>37850.578125</td>\n      <td>37815.070312</td>\n      <td>37741.773438</td>\n      <td>37740.777344</td>\n      <td>37720.859375</td>\n      <td>37857.000000</td>\n      <td>37849.980469</td>\n      <td>37797.285156</td>\n      <td>37722.636719</td>\n      <td>37866.453125</td>\n      <td>37746.105469</td>\n      <td>38265.125000</td>\n      <td>37815.886719</td>\n      <td>37797.679688</td>\n      <td>37744.050781</td>\n      <td>37756.984375</td>\n      <td>37606.589844</td>\n      <td>37627.941406</td>\n      <td>37445.910156</td>\n      <td>37551.734375</td>\n      <td>37394.496094</td>\n      <td>37440.757812</td>\n      <td>37361.238281</td>\n      <td>37338.574219</td>\n      <td>37391.601562</td>\n      <td>37413.457031</td>\n      <td>37304.656250</td>\n      <td>37301.031250</td>\n      <td>37314.050781</td>\n      <td>37261.746094</td>\n      <td>37290.398438</td>\n      <td>37289.148438</td>\n      <td>37260.070312</td>\n      <td>37180.660156</td>\n      <td>37099.250000</td>\n      <td>36956.265625</td>\n      <td>37217.929688</td>\n      <td>37243.300781</td>\n      <td>37308.710938</td>\n      <td>37331.148438</td>\n      <td>37473.097656</td>\n      <td>37511.054688</td>\n      <td>37500.316406</td>\n      <td>37385.480469</td>\n      <td>37295.429688</td>\n      <td>37346.011719</td>\n      <td>37358.363281</td>\n      <td>37322.140625</td>\n      <td>37356.648438</td>\n      <td>37434.425781</td>\n      <td>37345.921875</td>\n      <td>37429.453125</td>\n      <td>37475.507812</td>\n      <td>37618.648438</td>\n      <td>37638.851562</td>\n      <td>37291.343750</td>\n      <td>37203.867188</td>\n      <td>36589.949219</td>\n      <td>36406.671875</td>\n      <td>36492.765625</td>\n      <td>36404.410156</td>\n      <td>36613.843750</td>\n      <td>36570.613281</td>\n      <td>36447.683594</td>\n      <td>36681.859375</td>\n      <td>36652.515625</td>\n      <td>36548.500000</td>\n      <td>36461.550781</td>\n      <td>36525.898438</td>\n      <td>36372.601562</td>\n      <td>36436.886719</td>\n      <td>36374.535156</td>\n      <td>36302.617188</td>\n      <td>36086.960938</td>\n      <td>36115.984375</td>\n      <td>35814.101562</td>\n      <td>36406.289062</td>\n      <td>36838.941406</td>\n      <td>36940.218750</td>\n      <td>36991.875000</td>\n      <td>36893.941406</td>\n      <td>36930.085938</td>\n      <td>37209.886719</td>\n      <td>37008.648438</td>\n      <td>36648.074219</td>\n      <td>37034.703125</td>\n      <td>37155.750000</td>\n      <td>37191.179688</td>\n      <td>37380.101562</td>\n      <td>37324.824219</td>\n      <td>37417.746094</td>\n      <td>37296.796875</td>\n      <td>37393.140625</td>\n      <td>37375.539062</td>\n      <td>37417.441406</td>\n      <td>37488.855469</td>\n      <td>37456.285156</td>\n      <td>...</td>\n      <td>37602.968750</td>\n      <td>37547.015625</td>\n      <td>37600.449219</td>\n      <td>37258.398438</td>\n      <td>37468.058594</td>\n      <td>37440.609375</td>\n      <td>37112.296875</td>\n      <td>37171.574219</td>\n      <td>37190.757812</td>\n      <td>37302.925781</td>\n      <td>37217.878906</td>\n      <td>37148.933594</td>\n      <td>37253.976562</td>\n      <td>37204.277344</td>\n      <td>37157.253906</td>\n      <td>37220.949219</td>\n      <td>37131.574219</td>\n      <td>37334.664062</td>\n      <td>37317.066406</td>\n      <td>37306.523438</td>\n      <td>37387.921875</td>\n      <td>36992.503906</td>\n      <td>37009.546875</td>\n      <td>36948.828125</td>\n      <td>36965.484375</td>\n      <td>36855.968750</td>\n      <td>36609.250000</td>\n      <td>36581.687500</td>\n      <td>36577.316406</td>\n      <td>36460.593750</td>\n      <td>36507.871094</td>\n      <td>36514.593750</td>\n      <td>36482.050781</td>\n      <td>36567.250000</td>\n      <td>36656.121094</td>\n      <td>36644.832031</td>\n      <td>36628.085938</td>\n      <td>36637.300781</td>\n      <td>36534.128906</td>\n      <td>36567.628906</td>\n      <td>36554.750000</td>\n      <td>36487.917969</td>\n      <td>36515.101562</td>\n      <td>36492.691406</td>\n      <td>36592.660156</td>\n      <td>36522.640625</td>\n      <td>36569.359375</td>\n      <td>36625.449219</td>\n      <td>36703.609375</td>\n      <td>36749.742188</td>\n      <td>36648.488281</td>\n      <td>36695.476562</td>\n      <td>36674.894531</td>\n      <td>36521.394531</td>\n      <td>36454.929688</td>\n      <td>36478.910156</td>\n      <td>36422.441406</td>\n      <td>36480.339844</td>\n      <td>36413.179688</td>\n      <td>36468.468750</td>\n      <td>36406.851562</td>\n      <td>36278.894531</td>\n      <td>36360.625000</td>\n      <td>36409.949219</td>\n      <td>36390.769531</td>\n      <td>36428.011719</td>\n      <td>36423.769531</td>\n      <td>36418.550781</td>\n      <td>36629.398438</td>\n      <td>36552.156250</td>\n      <td>36417.292969</td>\n      <td>36492.996094</td>\n      <td>36323.144531</td>\n      <td>36485.074219</td>\n      <td>36498.621094</td>\n      <td>35990.039062</td>\n      <td>36042.480469</td>\n      <td>36015.324219</td>\n      <td>36352.300781</td>\n      <td>36528.421875</td>\n      <td>36398.250000</td>\n      <td>36374.000000</td>\n      <td>36289.562500</td>\n      <td>36401.597656</td>\n      <td>36252.414062</td>\n      <td>36376.671875</td>\n      <td>36345.585938</td>\n      <td>36441.394531</td>\n      <td>36496.632812</td>\n      <td>36453.898438</td>\n      <td>36556.718750</td>\n      <td>36367.058594</td>\n      <td>36168.449219</td>\n      <td>36226.828125</td>\n      <td>35963.859375</td>\n      <td>35960.750000</td>\n      <td>35921.035156</td>\n      <td>36250.046875</td>\n      <td>36317.250000</td>\n      <td>36594.953125</td>\n      <td>36429.351562</td>\n      <td>36761.156250</td>\n      <td>36789.429688</td>\n      <td>37136.937500</td>\n      <td>37204.390625</td>\n      <td>37303.949219</td>\n      <td>37367.195312</td>\n      <td>37328.851562</td>\n      <td>37458.226562</td>\n      <td>37568.496094</td>\n      <td>37466.882812</td>\n      <td>37455.558594</td>\n      <td>37473.175781</td>\n      <td>37524.074219</td>\n      <td>37448.699219</td>\n      <td>37785.699219</td>\n      <td>37900.609375</td>\n      <td>37722.441406</td>\n      <td>37647.261719</td>\n      <td>37569.449219</td>\n      <td>37513.953125</td>\n      <td>37388.851562</td>\n      <td>36500.656250</td>\n      <td>36385.816406</td>\n      <td>36398.691406</td>\n      <td>36132.300781</td>\n      <td>36099.589844</td>\n      <td>36225.386719</td>\n      <td>36246.609375</td>\n      <td>35884.214844</td>\n      <td>35794.199219</td>\n      <td>35801.464844</td>\n      <td>35595.703125</td>\n      <td>35594.414062</td>\n      <td>35620.476562</td>\n      <td>35580.953125</td>\n      <td>35400.820312</td>\n      <td>35487.273438</td>\n      <td>35491.156250</td>\n      <td>35502.875000</td>\n      <td>35551.351562</td>\n      <td>35612.113281</td>\n      <td>35575.347656</td>\n      <td>35291.933594</td>\n      <td>35294.433594</td>\n      <td>35240.332031</td>\n      <td>36083.226562</td>\n      <td>36274.691406</td>\n      <td>36142.460938</td>\n      <td>36617.410156</td>\n      <td>36528.296875</td>\n      <td>36365.453125</td>\n      <td>36279.300781</td>\n      <td>36515.265625</td>\n      <td>36490.078125</td>\n      <td>36742.324219</td>\n      <td>36698.449219</td>\n      <td>36666.105469</td>\n      <td>36641.476562</td>\n      <td>36498.023438</td>\n      <td>36463.804688</td>\n      <td>36442.343750</td>\n      <td>36340.074219</td>\n      <td>36256.136719</td>\n      <td>36541.910156</td>\n      <td>36477.402344</td>\n      <td>36495.328125</td>\n      <td>36800.109375</td>\n      <td>36862.921875</td>\n      <td>36760.453125</td>\n      <td>36657.753906</td>\n      <td>36861.910156</td>\n      <td>36862.207031</td>\n      <td>36806.250000</td>\n      <td>41644.941406</td>\n    </tr>\n    <tr>\n      <th>2023-11-27 15:00:00+00:00</th>\n      <td>36766.648438</td>\n      <td>-0.021445</td>\n      <td>-0.028201</td>\n      <td>0.023853</td>\n      <td>1.489534e-10</td>\n      <td>-0.045298</td>\n      <td>-0.028201</td>\n      <td>-0.007789</td>\n      <td>0.037856</td>\n      <td>0.121317</td>\n      <td>2.979069e-10</td>\n      <td>-0.129105</td>\n      <td>0.037856</td>\n      <td>0.036436</td>\n      <td>0.216375</td>\n      <td>0.364721</td>\n      <td>4.766510e-09</td>\n      <td>-0.328285</td>\n      <td>0.216375</td>\n      <td>36807.449219</td>\n      <td>36955.343750</td>\n      <td>36992.609375</td>\n      <td>37046.425781</td>\n      <td>37110.019531</td>\n      <td>37386.000000</td>\n      <td>37351.371094</td>\n      <td>37293.785156</td>\n      <td>37295.492188</td>\n      <td>37315.109375</td>\n      <td>37382.367188</td>\n      <td>37319.820312</td>\n      <td>37318.031250</td>\n      <td>37511.992188</td>\n      <td>37500.851562</td>\n      <td>37460.746094</td>\n      <td>37674.539062</td>\n      <td>37622.535156</td>\n      <td>37600.101562</td>\n      <td>37400.328125</td>\n      <td>37261.785156</td>\n      <td>37287.253906</td>\n      <td>37245.613281</td>\n      <td>37453.195312</td>\n      <td>37369.542969</td>\n      <td>37548.851562</td>\n      <td>37648.285156</td>\n      <td>37712.632812</td>\n      <td>37721.714844</td>\n      <td>37784.953125</td>\n      <td>37782.777344</td>\n      <td>37823.695312</td>\n      <td>37808.441406</td>\n      <td>37803.851562</td>\n      <td>37790.101562</td>\n      <td>37763.789062</td>\n      <td>37778.171875</td>\n      <td>37737.050781</td>\n      <td>37774.500000</td>\n      <td>37806.699219</td>\n      <td>37752.734375</td>\n      <td>37828.062500</td>\n      <td>37826.925781</td>\n      <td>37846.503906</td>\n      <td>37845.960938</td>\n      <td>37793.910156</td>\n      <td>37769.109375</td>\n      <td>37725.953125</td>\n      <td>37710.105469</td>\n      <td>37742.480469</td>\n      <td>37749.714844</td>\n      <td>37676.199219</td>\n      <td>37708.832031</td>\n      <td>37767.398438</td>\n      <td>37785.714844</td>\n      <td>37816.875000</td>\n      <td>37815.179688</td>\n      <td>37829.914062</td>\n      <td>37812.269531</td>\n      <td>37818.269531</td>\n      <td>37850.578125</td>\n      <td>37815.070312</td>\n      <td>37741.773438</td>\n      <td>37740.777344</td>\n      <td>37720.859375</td>\n      <td>37857.000000</td>\n      <td>37849.980469</td>\n      <td>37797.285156</td>\n      <td>37722.636719</td>\n      <td>37866.453125</td>\n      <td>37746.105469</td>\n      <td>38265.125000</td>\n      <td>37815.886719</td>\n      <td>37797.679688</td>\n      <td>37744.050781</td>\n      <td>37756.984375</td>\n      <td>37606.589844</td>\n      <td>37627.941406</td>\n      <td>37445.910156</td>\n      <td>37551.734375</td>\n      <td>37394.496094</td>\n      <td>37440.757812</td>\n      <td>37361.238281</td>\n      <td>37338.574219</td>\n      <td>37391.601562</td>\n      <td>37413.457031</td>\n      <td>37304.656250</td>\n      <td>37301.031250</td>\n      <td>37314.050781</td>\n      <td>37261.746094</td>\n      <td>37290.398438</td>\n      <td>37289.148438</td>\n      <td>37260.070312</td>\n      <td>37180.660156</td>\n      <td>37099.250000</td>\n      <td>36956.265625</td>\n      <td>37217.929688</td>\n      <td>37243.300781</td>\n      <td>37308.710938</td>\n      <td>37331.148438</td>\n      <td>37473.097656</td>\n      <td>37511.054688</td>\n      <td>37500.316406</td>\n      <td>37385.480469</td>\n      <td>37295.429688</td>\n      <td>37346.011719</td>\n      <td>37358.363281</td>\n      <td>37322.140625</td>\n      <td>37356.648438</td>\n      <td>37434.425781</td>\n      <td>37345.921875</td>\n      <td>37429.453125</td>\n      <td>37475.507812</td>\n      <td>37618.648438</td>\n      <td>37638.851562</td>\n      <td>37291.343750</td>\n      <td>37203.867188</td>\n      <td>36589.949219</td>\n      <td>36406.671875</td>\n      <td>36492.765625</td>\n      <td>36404.410156</td>\n      <td>36613.843750</td>\n      <td>36570.613281</td>\n      <td>36447.683594</td>\n      <td>36681.859375</td>\n      <td>36652.515625</td>\n      <td>36548.500000</td>\n      <td>36461.550781</td>\n      <td>36525.898438</td>\n      <td>36372.601562</td>\n      <td>36436.886719</td>\n      <td>36374.535156</td>\n      <td>36302.617188</td>\n      <td>36086.960938</td>\n      <td>36115.984375</td>\n      <td>35814.101562</td>\n      <td>36406.289062</td>\n      <td>36838.941406</td>\n      <td>36940.218750</td>\n      <td>36991.875000</td>\n      <td>36893.941406</td>\n      <td>36930.085938</td>\n      <td>37209.886719</td>\n      <td>37008.648438</td>\n      <td>36648.074219</td>\n      <td>37034.703125</td>\n      <td>37155.750000</td>\n      <td>37191.179688</td>\n      <td>37380.101562</td>\n      <td>37324.824219</td>\n      <td>37417.746094</td>\n      <td>37296.796875</td>\n      <td>37393.140625</td>\n      <td>37375.539062</td>\n      <td>37417.441406</td>\n      <td>37488.855469</td>\n      <td>...</td>\n      <td>37586.710938</td>\n      <td>37602.968750</td>\n      <td>37547.015625</td>\n      <td>37600.449219</td>\n      <td>37258.398438</td>\n      <td>37468.058594</td>\n      <td>37440.609375</td>\n      <td>37112.296875</td>\n      <td>37171.574219</td>\n      <td>37190.757812</td>\n      <td>37302.925781</td>\n      <td>37217.878906</td>\n      <td>37148.933594</td>\n      <td>37253.976562</td>\n      <td>37204.277344</td>\n      <td>37157.253906</td>\n      <td>37220.949219</td>\n      <td>37131.574219</td>\n      <td>37334.664062</td>\n      <td>37317.066406</td>\n      <td>37306.523438</td>\n      <td>37387.921875</td>\n      <td>36992.503906</td>\n      <td>37009.546875</td>\n      <td>36948.828125</td>\n      <td>36965.484375</td>\n      <td>36855.968750</td>\n      <td>36609.250000</td>\n      <td>36581.687500</td>\n      <td>36577.316406</td>\n      <td>36460.593750</td>\n      <td>36507.871094</td>\n      <td>36514.593750</td>\n      <td>36482.050781</td>\n      <td>36567.250000</td>\n      <td>36656.121094</td>\n      <td>36644.832031</td>\n      <td>36628.085938</td>\n      <td>36637.300781</td>\n      <td>36534.128906</td>\n      <td>36567.628906</td>\n      <td>36554.750000</td>\n      <td>36487.917969</td>\n      <td>36515.101562</td>\n      <td>36492.691406</td>\n      <td>36592.660156</td>\n      <td>36522.640625</td>\n      <td>36569.359375</td>\n      <td>36625.449219</td>\n      <td>36703.609375</td>\n      <td>36749.742188</td>\n      <td>36648.488281</td>\n      <td>36695.476562</td>\n      <td>36674.894531</td>\n      <td>36521.394531</td>\n      <td>36454.929688</td>\n      <td>36478.910156</td>\n      <td>36422.441406</td>\n      <td>36480.339844</td>\n      <td>36413.179688</td>\n      <td>36468.468750</td>\n      <td>36406.851562</td>\n      <td>36278.894531</td>\n      <td>36360.625000</td>\n      <td>36409.949219</td>\n      <td>36390.769531</td>\n      <td>36428.011719</td>\n      <td>36423.769531</td>\n      <td>36418.550781</td>\n      <td>36629.398438</td>\n      <td>36552.156250</td>\n      <td>36417.292969</td>\n      <td>36492.996094</td>\n      <td>36323.144531</td>\n      <td>36485.074219</td>\n      <td>36498.621094</td>\n      <td>35990.039062</td>\n      <td>36042.480469</td>\n      <td>36015.324219</td>\n      <td>36352.300781</td>\n      <td>36528.421875</td>\n      <td>36398.250000</td>\n      <td>36374.000000</td>\n      <td>36289.562500</td>\n      <td>36401.597656</td>\n      <td>36252.414062</td>\n      <td>36376.671875</td>\n      <td>36345.585938</td>\n      <td>36441.394531</td>\n      <td>36496.632812</td>\n      <td>36453.898438</td>\n      <td>36556.718750</td>\n      <td>36367.058594</td>\n      <td>36168.449219</td>\n      <td>36226.828125</td>\n      <td>35963.859375</td>\n      <td>35960.750000</td>\n      <td>35921.035156</td>\n      <td>36250.046875</td>\n      <td>36317.250000</td>\n      <td>36594.953125</td>\n      <td>36429.351562</td>\n      <td>36761.156250</td>\n      <td>36789.429688</td>\n      <td>37136.937500</td>\n      <td>37204.390625</td>\n      <td>37303.949219</td>\n      <td>37367.195312</td>\n      <td>37328.851562</td>\n      <td>37458.226562</td>\n      <td>37568.496094</td>\n      <td>37466.882812</td>\n      <td>37455.558594</td>\n      <td>37473.175781</td>\n      <td>37524.074219</td>\n      <td>37448.699219</td>\n      <td>37785.699219</td>\n      <td>37900.609375</td>\n      <td>37722.441406</td>\n      <td>37647.261719</td>\n      <td>37569.449219</td>\n      <td>37513.953125</td>\n      <td>37388.851562</td>\n      <td>36500.656250</td>\n      <td>36385.816406</td>\n      <td>36398.691406</td>\n      <td>36132.300781</td>\n      <td>36099.589844</td>\n      <td>36225.386719</td>\n      <td>36246.609375</td>\n      <td>35884.214844</td>\n      <td>35794.199219</td>\n      <td>35801.464844</td>\n      <td>35595.703125</td>\n      <td>35594.414062</td>\n      <td>35620.476562</td>\n      <td>35580.953125</td>\n      <td>35400.820312</td>\n      <td>35487.273438</td>\n      <td>35491.156250</td>\n      <td>35502.875000</td>\n      <td>35551.351562</td>\n      <td>35612.113281</td>\n      <td>35575.347656</td>\n      <td>35291.933594</td>\n      <td>35294.433594</td>\n      <td>35240.332031</td>\n      <td>36083.226562</td>\n      <td>36274.691406</td>\n      <td>36142.460938</td>\n      <td>36617.410156</td>\n      <td>36528.296875</td>\n      <td>36365.453125</td>\n      <td>36279.300781</td>\n      <td>36515.265625</td>\n      <td>36490.078125</td>\n      <td>36742.324219</td>\n      <td>36698.449219</td>\n      <td>36666.105469</td>\n      <td>36641.476562</td>\n      <td>36498.023438</td>\n      <td>36463.804688</td>\n      <td>36442.343750</td>\n      <td>36340.074219</td>\n      <td>36256.136719</td>\n      <td>36541.910156</td>\n      <td>36477.402344</td>\n      <td>36495.328125</td>\n      <td>36800.109375</td>\n      <td>36862.921875</td>\n      <td>36760.453125</td>\n      <td>36657.753906</td>\n      <td>36861.910156</td>\n      <td>36862.207031</td>\n      <td>41270.328125</td>\n    </tr>\n    <tr>\n      <th>2023-11-27 16:00:00+00:00</th>\n      <td>37072.050781</td>\n      <td>-0.012845</td>\n      <td>-0.019770</td>\n      <td>0.023853</td>\n      <td>1.489534e-10</td>\n      <td>-0.036697</td>\n      <td>-0.019770</td>\n      <td>0.000349</td>\n      <td>0.041053</td>\n      <td>0.121317</td>\n      <td>2.979069e-10</td>\n      <td>-0.120967</td>\n      <td>0.041053</td>\n      <td>0.044472</td>\n      <td>0.226338</td>\n      <td>0.364721</td>\n      <td>4.766510e-09</td>\n      <td>-0.320248</td>\n      <td>0.226338</td>\n      <td>36766.648438</td>\n      <td>36807.449219</td>\n      <td>36955.343750</td>\n      <td>36992.609375</td>\n      <td>37046.425781</td>\n      <td>37110.019531</td>\n      <td>37386.000000</td>\n      <td>37351.371094</td>\n      <td>37293.785156</td>\n      <td>37295.492188</td>\n      <td>37315.109375</td>\n      <td>37382.367188</td>\n      <td>37319.820312</td>\n      <td>37318.031250</td>\n      <td>37511.992188</td>\n      <td>37500.851562</td>\n      <td>37460.746094</td>\n      <td>37674.539062</td>\n      <td>37622.535156</td>\n      <td>37600.101562</td>\n      <td>37400.328125</td>\n      <td>37261.785156</td>\n      <td>37287.253906</td>\n      <td>37245.613281</td>\n      <td>37453.195312</td>\n      <td>37369.542969</td>\n      <td>37548.851562</td>\n      <td>37648.285156</td>\n      <td>37712.632812</td>\n      <td>37721.714844</td>\n      <td>37784.953125</td>\n      <td>37782.777344</td>\n      <td>37823.695312</td>\n      <td>37808.441406</td>\n      <td>37803.851562</td>\n      <td>37790.101562</td>\n      <td>37763.789062</td>\n      <td>37778.171875</td>\n      <td>37737.050781</td>\n      <td>37774.500000</td>\n      <td>37806.699219</td>\n      <td>37752.734375</td>\n      <td>37828.062500</td>\n      <td>37826.925781</td>\n      <td>37846.503906</td>\n      <td>37845.960938</td>\n      <td>37793.910156</td>\n      <td>37769.109375</td>\n      <td>37725.953125</td>\n      <td>37710.105469</td>\n      <td>37742.480469</td>\n      <td>37749.714844</td>\n      <td>37676.199219</td>\n      <td>37708.832031</td>\n      <td>37767.398438</td>\n      <td>37785.714844</td>\n      <td>37816.875000</td>\n      <td>37815.179688</td>\n      <td>37829.914062</td>\n      <td>37812.269531</td>\n      <td>37818.269531</td>\n      <td>37850.578125</td>\n      <td>37815.070312</td>\n      <td>37741.773438</td>\n      <td>37740.777344</td>\n      <td>37720.859375</td>\n      <td>37857.000000</td>\n      <td>37849.980469</td>\n      <td>37797.285156</td>\n      <td>37722.636719</td>\n      <td>37866.453125</td>\n      <td>37746.105469</td>\n      <td>38265.125000</td>\n      <td>37815.886719</td>\n      <td>37797.679688</td>\n      <td>37744.050781</td>\n      <td>37756.984375</td>\n      <td>37606.589844</td>\n      <td>37627.941406</td>\n      <td>37445.910156</td>\n      <td>37551.734375</td>\n      <td>37394.496094</td>\n      <td>37440.757812</td>\n      <td>37361.238281</td>\n      <td>37338.574219</td>\n      <td>37391.601562</td>\n      <td>37413.457031</td>\n      <td>37304.656250</td>\n      <td>37301.031250</td>\n      <td>37314.050781</td>\n      <td>37261.746094</td>\n      <td>37290.398438</td>\n      <td>37289.148438</td>\n      <td>37260.070312</td>\n      <td>37180.660156</td>\n      <td>37099.250000</td>\n      <td>36956.265625</td>\n      <td>37217.929688</td>\n      <td>37243.300781</td>\n      <td>37308.710938</td>\n      <td>37331.148438</td>\n      <td>37473.097656</td>\n      <td>37511.054688</td>\n      <td>37500.316406</td>\n      <td>37385.480469</td>\n      <td>37295.429688</td>\n      <td>37346.011719</td>\n      <td>37358.363281</td>\n      <td>37322.140625</td>\n      <td>37356.648438</td>\n      <td>37434.425781</td>\n      <td>37345.921875</td>\n      <td>37429.453125</td>\n      <td>37475.507812</td>\n      <td>37618.648438</td>\n      <td>37638.851562</td>\n      <td>37291.343750</td>\n      <td>37203.867188</td>\n      <td>36589.949219</td>\n      <td>36406.671875</td>\n      <td>36492.765625</td>\n      <td>36404.410156</td>\n      <td>36613.843750</td>\n      <td>36570.613281</td>\n      <td>36447.683594</td>\n      <td>36681.859375</td>\n      <td>36652.515625</td>\n      <td>36548.500000</td>\n      <td>36461.550781</td>\n      <td>36525.898438</td>\n      <td>36372.601562</td>\n      <td>36436.886719</td>\n      <td>36374.535156</td>\n      <td>36302.617188</td>\n      <td>36086.960938</td>\n      <td>36115.984375</td>\n      <td>35814.101562</td>\n      <td>36406.289062</td>\n      <td>36838.941406</td>\n      <td>36940.218750</td>\n      <td>36991.875000</td>\n      <td>36893.941406</td>\n      <td>36930.085938</td>\n      <td>37209.886719</td>\n      <td>37008.648438</td>\n      <td>36648.074219</td>\n      <td>37034.703125</td>\n      <td>37155.750000</td>\n      <td>37191.179688</td>\n      <td>37380.101562</td>\n      <td>37324.824219</td>\n      <td>37417.746094</td>\n      <td>37296.796875</td>\n      <td>37393.140625</td>\n      <td>37375.539062</td>\n      <td>37417.441406</td>\n      <td>...</td>\n      <td>37408.617188</td>\n      <td>37586.710938</td>\n      <td>37602.968750</td>\n      <td>37547.015625</td>\n      <td>37600.449219</td>\n      <td>37258.398438</td>\n      <td>37468.058594</td>\n      <td>37440.609375</td>\n      <td>37112.296875</td>\n      <td>37171.574219</td>\n      <td>37190.757812</td>\n      <td>37302.925781</td>\n      <td>37217.878906</td>\n      <td>37148.933594</td>\n      <td>37253.976562</td>\n      <td>37204.277344</td>\n      <td>37157.253906</td>\n      <td>37220.949219</td>\n      <td>37131.574219</td>\n      <td>37334.664062</td>\n      <td>37317.066406</td>\n      <td>37306.523438</td>\n      <td>37387.921875</td>\n      <td>36992.503906</td>\n      <td>37009.546875</td>\n      <td>36948.828125</td>\n      <td>36965.484375</td>\n      <td>36855.968750</td>\n      <td>36609.250000</td>\n      <td>36581.687500</td>\n      <td>36577.316406</td>\n      <td>36460.593750</td>\n      <td>36507.871094</td>\n      <td>36514.593750</td>\n      <td>36482.050781</td>\n      <td>36567.250000</td>\n      <td>36656.121094</td>\n      <td>36644.832031</td>\n      <td>36628.085938</td>\n      <td>36637.300781</td>\n      <td>36534.128906</td>\n      <td>36567.628906</td>\n      <td>36554.750000</td>\n      <td>36487.917969</td>\n      <td>36515.101562</td>\n      <td>36492.691406</td>\n      <td>36592.660156</td>\n      <td>36522.640625</td>\n      <td>36569.359375</td>\n      <td>36625.449219</td>\n      <td>36703.609375</td>\n      <td>36749.742188</td>\n      <td>36648.488281</td>\n      <td>36695.476562</td>\n      <td>36674.894531</td>\n      <td>36521.394531</td>\n      <td>36454.929688</td>\n      <td>36478.910156</td>\n      <td>36422.441406</td>\n      <td>36480.339844</td>\n      <td>36413.179688</td>\n      <td>36468.468750</td>\n      <td>36406.851562</td>\n      <td>36278.894531</td>\n      <td>36360.625000</td>\n      <td>36409.949219</td>\n      <td>36390.769531</td>\n      <td>36428.011719</td>\n      <td>36423.769531</td>\n      <td>36418.550781</td>\n      <td>36629.398438</td>\n      <td>36552.156250</td>\n      <td>36417.292969</td>\n      <td>36492.996094</td>\n      <td>36323.144531</td>\n      <td>36485.074219</td>\n      <td>36498.621094</td>\n      <td>35990.039062</td>\n      <td>36042.480469</td>\n      <td>36015.324219</td>\n      <td>36352.300781</td>\n      <td>36528.421875</td>\n      <td>36398.250000</td>\n      <td>36374.000000</td>\n      <td>36289.562500</td>\n      <td>36401.597656</td>\n      <td>36252.414062</td>\n      <td>36376.671875</td>\n      <td>36345.585938</td>\n      <td>36441.394531</td>\n      <td>36496.632812</td>\n      <td>36453.898438</td>\n      <td>36556.718750</td>\n      <td>36367.058594</td>\n      <td>36168.449219</td>\n      <td>36226.828125</td>\n      <td>35963.859375</td>\n      <td>35960.750000</td>\n      <td>35921.035156</td>\n      <td>36250.046875</td>\n      <td>36317.250000</td>\n      <td>36594.953125</td>\n      <td>36429.351562</td>\n      <td>36761.156250</td>\n      <td>36789.429688</td>\n      <td>37136.937500</td>\n      <td>37204.390625</td>\n      <td>37303.949219</td>\n      <td>37367.195312</td>\n      <td>37328.851562</td>\n      <td>37458.226562</td>\n      <td>37568.496094</td>\n      <td>37466.882812</td>\n      <td>37455.558594</td>\n      <td>37473.175781</td>\n      <td>37524.074219</td>\n      <td>37448.699219</td>\n      <td>37785.699219</td>\n      <td>37900.609375</td>\n      <td>37722.441406</td>\n      <td>37647.261719</td>\n      <td>37569.449219</td>\n      <td>37513.953125</td>\n      <td>37388.851562</td>\n      <td>36500.656250</td>\n      <td>36385.816406</td>\n      <td>36398.691406</td>\n      <td>36132.300781</td>\n      <td>36099.589844</td>\n      <td>36225.386719</td>\n      <td>36246.609375</td>\n      <td>35884.214844</td>\n      <td>35794.199219</td>\n      <td>35801.464844</td>\n      <td>35595.703125</td>\n      <td>35594.414062</td>\n      <td>35620.476562</td>\n      <td>35580.953125</td>\n      <td>35400.820312</td>\n      <td>35487.273438</td>\n      <td>35491.156250</td>\n      <td>35502.875000</td>\n      <td>35551.351562</td>\n      <td>35612.113281</td>\n      <td>35575.347656</td>\n      <td>35291.933594</td>\n      <td>35294.433594</td>\n      <td>35240.332031</td>\n      <td>36083.226562</td>\n      <td>36274.691406</td>\n      <td>36142.460938</td>\n      <td>36617.410156</td>\n      <td>36528.296875</td>\n      <td>36365.453125</td>\n      <td>36279.300781</td>\n      <td>36515.265625</td>\n      <td>36490.078125</td>\n      <td>36742.324219</td>\n      <td>36698.449219</td>\n      <td>36666.105469</td>\n      <td>36641.476562</td>\n      <td>36498.023438</td>\n      <td>36463.804688</td>\n      <td>36442.343750</td>\n      <td>36340.074219</td>\n      <td>36256.136719</td>\n      <td>36541.910156</td>\n      <td>36477.402344</td>\n      <td>36495.328125</td>\n      <td>36800.109375</td>\n      <td>36862.921875</td>\n      <td>36760.453125</td>\n      <td>36657.753906</td>\n      <td>36861.910156</td>\n      <td>41610.027344</td>\n    </tr>\n    <tr>\n      <th>2023-11-27 17:00:00+00:00</th>\n      <td>36927.945312</td>\n      <td>-0.016339</td>\n      <td>-0.024132</td>\n      <td>0.023853</td>\n      <td>1.489534e-10</td>\n      <td>-0.040192</td>\n      <td>-0.024132</td>\n      <td>-0.003663</td>\n      <td>0.036048</td>\n      <td>0.121317</td>\n      <td>2.979069e-10</td>\n      <td>-0.124980</td>\n      <td>0.036048</td>\n      <td>0.040348</td>\n      <td>0.221391</td>\n      <td>0.364721</td>\n      <td>4.766510e-09</td>\n      <td>-0.324373</td>\n      <td>0.221391</td>\n      <td>37072.050781</td>\n      <td>36766.648438</td>\n      <td>36807.449219</td>\n      <td>36955.343750</td>\n      <td>36992.609375</td>\n      <td>37046.425781</td>\n      <td>37110.019531</td>\n      <td>37386.000000</td>\n      <td>37351.371094</td>\n      <td>37293.785156</td>\n      <td>37295.492188</td>\n      <td>37315.109375</td>\n      <td>37382.367188</td>\n      <td>37319.820312</td>\n      <td>37318.031250</td>\n      <td>37511.992188</td>\n      <td>37500.851562</td>\n      <td>37460.746094</td>\n      <td>37674.539062</td>\n      <td>37622.535156</td>\n      <td>37600.101562</td>\n      <td>37400.328125</td>\n      <td>37261.785156</td>\n      <td>37287.253906</td>\n      <td>37245.613281</td>\n      <td>37453.195312</td>\n      <td>37369.542969</td>\n      <td>37548.851562</td>\n      <td>37648.285156</td>\n      <td>37712.632812</td>\n      <td>37721.714844</td>\n      <td>37784.953125</td>\n      <td>37782.777344</td>\n      <td>37823.695312</td>\n      <td>37808.441406</td>\n      <td>37803.851562</td>\n      <td>37790.101562</td>\n      <td>37763.789062</td>\n      <td>37778.171875</td>\n      <td>37737.050781</td>\n      <td>37774.500000</td>\n      <td>37806.699219</td>\n      <td>37752.734375</td>\n      <td>37828.062500</td>\n      <td>37826.925781</td>\n      <td>37846.503906</td>\n      <td>37845.960938</td>\n      <td>37793.910156</td>\n      <td>37769.109375</td>\n      <td>37725.953125</td>\n      <td>37710.105469</td>\n      <td>37742.480469</td>\n      <td>37749.714844</td>\n      <td>37676.199219</td>\n      <td>37708.832031</td>\n      <td>37767.398438</td>\n      <td>37785.714844</td>\n      <td>37816.875000</td>\n      <td>37815.179688</td>\n      <td>37829.914062</td>\n      <td>37812.269531</td>\n      <td>37818.269531</td>\n      <td>37850.578125</td>\n      <td>37815.070312</td>\n      <td>37741.773438</td>\n      <td>37740.777344</td>\n      <td>37720.859375</td>\n      <td>37857.000000</td>\n      <td>37849.980469</td>\n      <td>37797.285156</td>\n      <td>37722.636719</td>\n      <td>37866.453125</td>\n      <td>37746.105469</td>\n      <td>38265.125000</td>\n      <td>37815.886719</td>\n      <td>37797.679688</td>\n      <td>37744.050781</td>\n      <td>37756.984375</td>\n      <td>37606.589844</td>\n      <td>37627.941406</td>\n      <td>37445.910156</td>\n      <td>37551.734375</td>\n      <td>37394.496094</td>\n      <td>37440.757812</td>\n      <td>37361.238281</td>\n      <td>37338.574219</td>\n      <td>37391.601562</td>\n      <td>37413.457031</td>\n      <td>37304.656250</td>\n      <td>37301.031250</td>\n      <td>37314.050781</td>\n      <td>37261.746094</td>\n      <td>37290.398438</td>\n      <td>37289.148438</td>\n      <td>37260.070312</td>\n      <td>37180.660156</td>\n      <td>37099.250000</td>\n      <td>36956.265625</td>\n      <td>37217.929688</td>\n      <td>37243.300781</td>\n      <td>37308.710938</td>\n      <td>37331.148438</td>\n      <td>37473.097656</td>\n      <td>37511.054688</td>\n      <td>37500.316406</td>\n      <td>37385.480469</td>\n      <td>37295.429688</td>\n      <td>37346.011719</td>\n      <td>37358.363281</td>\n      <td>37322.140625</td>\n      <td>37356.648438</td>\n      <td>37434.425781</td>\n      <td>37345.921875</td>\n      <td>37429.453125</td>\n      <td>37475.507812</td>\n      <td>37618.648438</td>\n      <td>37638.851562</td>\n      <td>37291.343750</td>\n      <td>37203.867188</td>\n      <td>36589.949219</td>\n      <td>36406.671875</td>\n      <td>36492.765625</td>\n      <td>36404.410156</td>\n      <td>36613.843750</td>\n      <td>36570.613281</td>\n      <td>36447.683594</td>\n      <td>36681.859375</td>\n      <td>36652.515625</td>\n      <td>36548.500000</td>\n      <td>36461.550781</td>\n      <td>36525.898438</td>\n      <td>36372.601562</td>\n      <td>36436.886719</td>\n      <td>36374.535156</td>\n      <td>36302.617188</td>\n      <td>36086.960938</td>\n      <td>36115.984375</td>\n      <td>35814.101562</td>\n      <td>36406.289062</td>\n      <td>36838.941406</td>\n      <td>36940.218750</td>\n      <td>36991.875000</td>\n      <td>36893.941406</td>\n      <td>36930.085938</td>\n      <td>37209.886719</td>\n      <td>37008.648438</td>\n      <td>36648.074219</td>\n      <td>37034.703125</td>\n      <td>37155.750000</td>\n      <td>37191.179688</td>\n      <td>37380.101562</td>\n      <td>37324.824219</td>\n      <td>37417.746094</td>\n      <td>37296.796875</td>\n      <td>37393.140625</td>\n      <td>37375.539062</td>\n      <td>...</td>\n      <td>37462.246094</td>\n      <td>37408.617188</td>\n      <td>37586.710938</td>\n      <td>37602.968750</td>\n      <td>37547.015625</td>\n      <td>37600.449219</td>\n      <td>37258.398438</td>\n      <td>37468.058594</td>\n      <td>37440.609375</td>\n      <td>37112.296875</td>\n      <td>37171.574219</td>\n      <td>37190.757812</td>\n      <td>37302.925781</td>\n      <td>37217.878906</td>\n      <td>37148.933594</td>\n      <td>37253.976562</td>\n      <td>37204.277344</td>\n      <td>37157.253906</td>\n      <td>37220.949219</td>\n      <td>37131.574219</td>\n      <td>37334.664062</td>\n      <td>37317.066406</td>\n      <td>37306.523438</td>\n      <td>37387.921875</td>\n      <td>36992.503906</td>\n      <td>37009.546875</td>\n      <td>36948.828125</td>\n      <td>36965.484375</td>\n      <td>36855.968750</td>\n      <td>36609.250000</td>\n      <td>36581.687500</td>\n      <td>36577.316406</td>\n      <td>36460.593750</td>\n      <td>36507.871094</td>\n      <td>36514.593750</td>\n      <td>36482.050781</td>\n      <td>36567.250000</td>\n      <td>36656.121094</td>\n      <td>36644.832031</td>\n      <td>36628.085938</td>\n      <td>36637.300781</td>\n      <td>36534.128906</td>\n      <td>36567.628906</td>\n      <td>36554.750000</td>\n      <td>36487.917969</td>\n      <td>36515.101562</td>\n      <td>36492.691406</td>\n      <td>36592.660156</td>\n      <td>36522.640625</td>\n      <td>36569.359375</td>\n      <td>36625.449219</td>\n      <td>36703.609375</td>\n      <td>36749.742188</td>\n      <td>36648.488281</td>\n      <td>36695.476562</td>\n      <td>36674.894531</td>\n      <td>36521.394531</td>\n      <td>36454.929688</td>\n      <td>36478.910156</td>\n      <td>36422.441406</td>\n      <td>36480.339844</td>\n      <td>36413.179688</td>\n      <td>36468.468750</td>\n      <td>36406.851562</td>\n      <td>36278.894531</td>\n      <td>36360.625000</td>\n      <td>36409.949219</td>\n      <td>36390.769531</td>\n      <td>36428.011719</td>\n      <td>36423.769531</td>\n      <td>36418.550781</td>\n      <td>36629.398438</td>\n      <td>36552.156250</td>\n      <td>36417.292969</td>\n      <td>36492.996094</td>\n      <td>36323.144531</td>\n      <td>36485.074219</td>\n      <td>36498.621094</td>\n      <td>35990.039062</td>\n      <td>36042.480469</td>\n      <td>36015.324219</td>\n      <td>36352.300781</td>\n      <td>36528.421875</td>\n      <td>36398.250000</td>\n      <td>36374.000000</td>\n      <td>36289.562500</td>\n      <td>36401.597656</td>\n      <td>36252.414062</td>\n      <td>36376.671875</td>\n      <td>36345.585938</td>\n      <td>36441.394531</td>\n      <td>36496.632812</td>\n      <td>36453.898438</td>\n      <td>36556.718750</td>\n      <td>36367.058594</td>\n      <td>36168.449219</td>\n      <td>36226.828125</td>\n      <td>35963.859375</td>\n      <td>35960.750000</td>\n      <td>35921.035156</td>\n      <td>36250.046875</td>\n      <td>36317.250000</td>\n      <td>36594.953125</td>\n      <td>36429.351562</td>\n      <td>36761.156250</td>\n      <td>36789.429688</td>\n      <td>37136.937500</td>\n      <td>37204.390625</td>\n      <td>37303.949219</td>\n      <td>37367.195312</td>\n      <td>37328.851562</td>\n      <td>37458.226562</td>\n      <td>37568.496094</td>\n      <td>37466.882812</td>\n      <td>37455.558594</td>\n      <td>37473.175781</td>\n      <td>37524.074219</td>\n      <td>37448.699219</td>\n      <td>37785.699219</td>\n      <td>37900.609375</td>\n      <td>37722.441406</td>\n      <td>37647.261719</td>\n      <td>37569.449219</td>\n      <td>37513.953125</td>\n      <td>37388.851562</td>\n      <td>36500.656250</td>\n      <td>36385.816406</td>\n      <td>36398.691406</td>\n      <td>36132.300781</td>\n      <td>36099.589844</td>\n      <td>36225.386719</td>\n      <td>36246.609375</td>\n      <td>35884.214844</td>\n      <td>35794.199219</td>\n      <td>35801.464844</td>\n      <td>35595.703125</td>\n      <td>35594.414062</td>\n      <td>35620.476562</td>\n      <td>35580.953125</td>\n      <td>35400.820312</td>\n      <td>35487.273438</td>\n      <td>35491.156250</td>\n      <td>35502.875000</td>\n      <td>35551.351562</td>\n      <td>35612.113281</td>\n      <td>35575.347656</td>\n      <td>35291.933594</td>\n      <td>35294.433594</td>\n      <td>35240.332031</td>\n      <td>36083.226562</td>\n      <td>36274.691406</td>\n      <td>36142.460938</td>\n      <td>36617.410156</td>\n      <td>36528.296875</td>\n      <td>36365.453125</td>\n      <td>36279.300781</td>\n      <td>36515.265625</td>\n      <td>36490.078125</td>\n      <td>36742.324219</td>\n      <td>36698.449219</td>\n      <td>36666.105469</td>\n      <td>36641.476562</td>\n      <td>36498.023438</td>\n      <td>36463.804688</td>\n      <td>36442.343750</td>\n      <td>36340.074219</td>\n      <td>36256.136719</td>\n      <td>36541.910156</td>\n      <td>36477.402344</td>\n      <td>36495.328125</td>\n      <td>36800.109375</td>\n      <td>36862.921875</td>\n      <td>36760.453125</td>\n      <td>36657.753906</td>\n      <td>41589.976562</td>\n    </tr>\n  </tbody>\n</table>\n<p>25106 rows × 356 columns</p>\n</div>"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_proc = get_features(btc)\n",
    "df_proc"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:26.413852100Z",
     "start_time": "2023-12-04T17:05:26.020589600Z"
    }
   },
   "id": "249ebefa68c5042a"
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "355\n"
     ]
    }
   ],
   "source": [
    "feature_names = df_proc.drop('target', axis=1).columns.tolist()\n",
    "# TODO - he has 174 total... but 19 x 14 = 266\n",
    "print(len(feature_names))\n",
    "assert 'target' not in feature_names"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:27.297537800Z",
     "start_time": "2023-12-04T17:05:26.412852200Z"
    }
   },
   "id": "8cef18821bfcd70b"
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Training"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a595d4aa4762f580"
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model for BTC\n",
      "Entering time series cross-validation loop\n",
      "\n",
      "Processing split 1/5\n",
      "Number of train data: 4186\n",
      "Number of val data: 3848\n",
      "Trained model saved to 'trained_model_id_fold0.pkl'\n",
      "\n",
      "Processing split 2/5\n",
      "Number of train data: 8370\n",
      "Number of val data: 3848\n",
      "Trained model saved to 'trained_model_id_fold1.pkl'\n",
      "\n",
      "Processing split 3/5\n",
      "Number of train data: 12554\n",
      "Number of val data: 3848\n",
      "Trained model saved to 'trained_model_id_fold2.pkl'\n",
      "\n",
      "Processing split 4/5\n",
      "Number of train data: 16738\n",
      "Number of val data: 3848\n",
      "Trained model saved to 'trained_model_id_fold3.pkl'\n",
      "\n",
      "Processing split 5/5\n",
      "Number of train data: 20922\n",
      "Number of val data: 3848\n",
      "Trained model saved to 'trained_model_id_fold4.pkl'\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OOF corr score of BTC is 0.9004\n"
     ]
    }
   ],
   "source": [
    "print(f\"Training model for BTC\")\n",
    "\n",
    "oof_pred, oof_valid = train_and_evaluate_time_series_model(df_proc, feature_names, params)\n",
    "\n",
    "corr_ = corr_score(np.array(oof_pred), np.array(oof_valid))\n",
    "\n",
    "print(f'OOF corr score of BTC is {corr_:.4f}')\n",
    "print('')\n",
    "print('')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:32.104417800Z",
     "start_time": "2023-12-04T17:05:26.523538400Z"
    }
   },
   "id": "f510efb9afba0ab6"
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "0.900428701348794"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(np.array(oof_pred), np.array(oof_valid))[0, 1]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:32.197610600Z",
     "start_time": "2023-12-04T17:05:32.105414500Z"
    }
   },
   "id": "1906db0e5a5454cd"
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Processing split 1/5\n",
      "\n",
      "Processing split 2/5\n",
      "\n",
      "Processing split 3/5\n",
      "\n",
      "Processing split 4/5\n",
      "\n",
      "Processing split 5/5\n"
     ]
    },
    {
     "data": {
      "text/plain": "[32650.53,\n 32605.94,\n 32690.24,\n 32636.77,\n 32586.82,\n 32488.52,\n 32325.57,\n 32334.95,\n 32343.96,\n 32307.0,\n 32444.84,\n 32558.1,\n 32482.64,\n 32319.8,\n 32179.39,\n 32211.07,\n 32153.66,\n 32276.28,\n 32505.44,\n 32640.0,\n 33153.47,\n 33644.67,\n 33576.23,\n 33626.6,\n 33691.41,\n 33696.43,\n 33708.83,\n 33767.05,\n 33915.29,\n 33833.73,\n 33634.53,\n 33859.41,\n 33902.88,\n 33887.89,\n 33862.45,\n 33823.67,\n 33893.41,\n 33901.98,\n 34462.99,\n 34328.87,\n 34295.63,\n 33869.66,\n 33937.91,\n 34161.05,\n 34055.76,\n 34279.08,\n 34045.1,\n 33947.13,\n 34207.68,\n 34180.74,\n 34383.22,\n 34348.77,\n 34479.93,\n 34710.54,\n 34584.53,\n 34546.12,\n 34403.24,\n 34411.81,\n 34316.55,\n 34200.0,\n 34119.87,\n 34206.17,\n 34233.05,\n 34465.29,\n 34435.7,\n 34385.16,\n 34481.19,\n 34575.55,\n 34673.58,\n 35418.7,\n 36771.34,\n 38332.11,\n 38051.14,\n 38306.33,\n 38295.07,\n 38588.41,\n 38648.01,\n 38164.28,\n 38219.99,\n 38249.05,\n 38643.62,\n 38500.23,\n 38669.19,\n 38322.35,\n 38320.0,\n 38579.17,\n 39047.61,\n 39220.95,\n 39726.72,\n 39356.96,\n 37739.46,\n 37128.25,\n 37601.11,\n 37243.05,\n 37458.47,\n 36887.89,\n 36577.95,\n 36835.32,\n 36977.92,\n 37146.98,\n 36998.71,\n 37146.53,\n 37273.26,\n 37399.03,\n 37637.17,\n 38090.13,\n 38378.3,\n 38482.44,\n 38266.1,\n 38230.13,\n 37638.41,\n 37796.6,\n 37929.25,\n 37793.21,\n 38049.76,\n 38345.96,\n 39210.64,\n 39487.31,\n 39107.1,\n 39887.11,\n 39873.29,\n 39856.76,\n 39696.1,\n 40147.99,\n 39562.78,\n 39900.14,\n 39531.16,\n 39935.56,\n 40202.43,\n 40782.55,\n 39702.41,\n 39662.88,\n 39544.88,\n 39705.69,\n 40136.33,\n 38972.03,\n 40447.62,\n 40404.69,\n 39951.42,\n 40110.96,\n 39772.25,\n 40030.0,\n 39444.65,\n 39666.76,\n 39731.32,\n 39636.96,\n 39992.64,\n 40127.67,\n 40020.29,\n 39965.78,\n 39949.61,\n 40410.0,\n 40198.7,\n 39788.03,\n 39743.25,\n 39878.68,\n 39959.33,\n 39966.45,\n 39746.38,\n 39619.97,\n 39541.58,\n 39684.09,\n 39728.62,\n 39559.0,\n 39789.53,\n 40024.76,\n 39914.19,\n 40119.08,\n 40003.9,\n 39989.54,\n 39818.53,\n 39745.03,\n 39867.01,\n 39738.12,\n 38755.03,\n 38825.2,\n 38876.67,\n 38675.19,\n 38748.74,\n 38847.89,\n 39042.45,\n 39088.95,\n 38939.24,\n 39033.87,\n 39105.08,\n 39894.72,\n 40700.0,\n 41198.94,\n 41375.34,\n 42236.5,\n 41907.29,\n 42000.0,\n 41975.57,\n 41834.07,\n 41831.83,\n 41701.04,\n 41533.52,\n 41573.32,\n 41721.06,\n 41656.44,\n 41799.66,\n 41611.59,\n 41675.47,\n 41401.51,\n 41424.18,\n 41560.59,\n 41310.48,\n 41194.02,\n 41404.97,\n 41340.27,\n 41524.78,\n 42000.0,\n 41890.92,\n 41493.0,\n 41758.74,\n 41744.46,\n 41843.16,\n 42477.46,\n 42067.31,\n 41849.25,\n 41750.0,\n 41724.13,\n 41776.1,\n 41936.76,\n 41510.58,\n 41610.83,\n 41574.34,\n 41430.74,\n 41349.28,\n 41294.3,\n 41264.85,\n 41089.27,\n 41071.08,\n 41266.8,\n 41265.52,\n 41089.21,\n 40282.97,\n 39884.83,\n 39525.24,\n 39603.11,\n 39793.58,\n 39796.1,\n 39669.64,\n 39728.77,\n 40274.23,\n 40098.15,\n 39520.11,\n 39617.44,\n 39707.3,\n 39544.24,\n 39275.35,\n 39581.53,\n 39633.66,\n 39709.88,\n 39811.64,\n 39705.17,\n 39655.08,\n 39177.05,\n 38857.62,\n 39222.92,\n 39427.73,\n 39158.82,\n 39341.82,\n 38837.26,\n 38902.62,\n 38656.29,\n 38327.39,\n 38255.37,\n 38368.34,\n 38601.95,\n 38533.98,\n 38639.57,\n 38722.92,\n 38489.78,\n 38401.47,\n 38498.07,\n 38255.57,\n 38168.4,\n 38399.81,\n 38003.89,\n 38193.15,\n 38006.61,\n 38100.0,\n 38313.98,\n 38338.17,\n 38198.69,\n 38402.57,\n 38336.95,\n 38088.83,\n 37965.29,\n 37951.49,\n 37999.31,\n 37803.67,\n 37808.8,\n 37747.46,\n 37811.68,\n 38088.55,\n 38161.11,\n 38850.9,\n 39026.27,\n 39127.57,\n 39314.53,\n 39483.1,\n 39383.24,\n 39574.73,\n 39762.07,\n 39744.7,\n 39845.15,\n 39731.32,\n 39738.06,\n 39553.6,\n 39491.25,\n 39366.16,\n 39433.99,\n 39357.93,\n 39366.68,\n 39080.66,\n 38865.15,\n 38480.85,\n 38035.24,\n 38030.43,\n 37999.98,\n 38036.64,\n 38027.8,\n 39006.5,\n 38775.53,\n 39753.27,\n 40724.94,\n 40867.33,\n 40698.04,\n 40907.42,\n 40932.82,\n 40938.58,\n 40893.01,\n 40079.21,\n 40613.83,\n 40357.95,\n 40236.02,\n 40168.01,\n 40550.0,\n 41037.25,\n 40735.39,\n 40962.7,\n 40638.15,\n 40704.53,\n 40526.08,\n 40778.11,\n 40955.15,\n 40791.0,\n 42208.72,\n 42543.96,\n 42930.04,\n 42822.21,\n 42936.35,\n 42723.65,\n 42470.0,\n 42794.78,\n 42845.11,\n 43297.96,\n 43203.83,\n 43324.83,\n 43720.0,\n 43319.48,\n 43449.95,\n 43274.23,\n 43249.89,\n 43490.76,\n 43654.61,\n 43692.24,\n 43846.65,\n 43556.84,\n 43206.1,\n 43534.91,\n 43408.29,\n 43738.56,\n 43453.01,\n 43577.12,\n 43977.46,\n 43963.28,\n 43566.15,\n 44072.49,\n 44630.13,\n 44619.23,\n 44143.51,\n 43982.18,\n 44377.66,\n 44373.84,\n 44802.92,\n 45066.99,\n 44858.53,\n 44511.13,\n 44760.67,\n 44415.5,\n 44487.67,\n 45044.21,\n 44770.03,\n 43942.54,\n 44004.26,\n 43778.08,\n 43705.94,\n 43624.7,\n 43747.31,\n 43871.89,\n 44261.88,\n 44244.8,\n 43839.45,\n 43217.78,\n 43125.24,\n 43497.87,\n 43526.44,\n 43610.33,\n 43568.23,\n 43494.92,\n 43826.76,\n 43751.68,\n 44894.06,\n 45672.79,\n 45969.51,\n 45705.0,\n 45425.03,\n 45652.06,\n 45903.92,\n 45924.81,\n 45861.97,\n 46222.35,\n 46003.53,\n 45475.72,\n 45721.93,\n 46254.61,\n 46302.14,\n 45975.18,\n 46020.07,\n 46094.03,\n 45709.03,\n 45474.1,\n 45579.87,\n 45568.1,\n 45966.11,\n 46142.82,\n 45983.3,\n 45358.39,\n 45356.73,\n 45943.73,\n 45560.97,\n 45369.21,\n 45192.77,\n 44821.58,\n 45120.46,\n 45308.81,\n 45518.88,\n 45637.28,\n 45706.41,\n 45482.36,\n 45604.47,\n 45750.63,\n 45772.35,\n 45454.81,\n 45585.51,\n 45624.73,\n 45802.96,\n 46103.06,\n 46359.84,\n 46213.88,\n 46116.38,\n 46063.56,\n 45952.7,\n 46006.95,\n 46297.52,\n 46532.68,\n 46476.66,\n 46357.08,\n 46535.26,\n 46329.31,\n 46474.7,\n 46344.82,\n 46349.85,\n 45775.22,\n 45537.41,\n 45769.92,\n 46007.84,\n 46222.45,\n 45937.5,\n 45603.91,\n 45183.13,\n 45236.83,\n 45267.91,\n 45251.68,\n 45367.14,\n 45363.84,\n 44752.25,\n 44612.87,\n 44454.56,\n 44115.92,\n 44196.63,\n 44018.11,\n 44134.91,\n 44450.33,\n 44408.34,\n 44498.01,\n 44266.54,\n 43990.28,\n 44432.59,\n 44518.39,\n 44881.76,\n 45312.17,\n 45181.35,\n 45276.56,\n 45277.66,\n 45864.59,\n 46134.71,\n 46189.66,\n 46249.29,\n 46334.06,\n 46161.71,\n 46540.45,\n 46556.56,\n 46457.95,\n 46398.29,\n 46545.07,\n 46503.23,\n 46518.75,\n 46482.85,\n 47599.12,\n 47592.46,\n 47586.2,\n 47824.77,\n 47652.74,\n 47600.78,\n 47577.22,\n 47563.15,\n 47485.85,\n 47563.13,\n 47542.21,\n 47643.16,\n 47630.44,\n 47021.74,\n 46333.28,\n 46596.15,\n 46303.77,\n 46548.21,\n 46948.0,\n 46979.5,\n 46896.78,\n 46838.01,\n 46775.78,\n 47085.27,\n 46668.36,\n 47060.21,\n 46887.54,\n 47100.29,\n 47235.99,\n 47158.53,\n 47219.07,\n 46909.23,\n 46747.58,\n 46642.46,\n 46452.79,\n 45777.69,\n 45764.2,\n 46049.99,\n 45952.4,\n 45960.4,\n 45828.32,\n 46078.51,\n 45907.57,\n 46200.25,\n 45902.73,\n 46002.36,\n 46315.52,\n 46367.35,\n 46722.18,\n 46861.68,\n 47262.58,\n 47023.87,\n 47600.66,\n 47665.6,\n 48012.8,\n 47376.2,\n 47426.45,\n 47635.87,\n 47374.0,\n 47327.5,\n 47154.76,\n 47183.65,\n 47445.54,\n 47548.19,\n 47325.88,\n 46423.9,\n 46104.23,\n 46356.69,\n 46450.36,\n 46491.5,\n 46494.89,\n 46102.39,\n 46073.29,\n 46075.93,\n 46139.81,\n 45919.2,\n 45772.48,\n 46300.53,\n 46469.51,\n 46436.92,\n 46394.91,\n 46055.57,\n 45770.15,\n 45946.57,\n 46215.44,\n 46869.82,\n 46648.98,\n 46872.26,\n 46658.9,\n 46390.11,\n 45957.37,\n 45723.24,\n 45653.04,\n 45808.08,\n 45742.39,\n 45470.36,\n 44987.54,\n 44768.79,\n 44916.04,\n 44679.68,\n 44813.75,\n 45125.44,\n 44939.69,\n 45072.4,\n 45082.78,\n 45452.46,\n 45409.32,\n 44967.09,\n 45252.67,\n 45394.19,\n 45198.88,\n 44749.34,\n 45006.09,\n 45267.67,\n 45399.95,\n 45838.99,\n 45894.08,\n 45832.55,\n 45432.68,\n 44920.83,\n 44549.58,\n 44590.34,\n 44874.23,\n 44721.29,\n 45043.1,\n 44967.35,\n 44598.49,\n 44065.23,\n 44133.99,\n 44403.32,\n 44382.83,\n 44664.63,\n 44706.42,\n 44771.95,\n 44366.25,\n 44225.68,\n 44565.36,\n 44989.58,\n 45458.87,\n 45789.4,\n 45617.13,\n 45724.81,\n 46586.62,\n 46710.0,\n 46589.09,\n 46630.0,\n 46807.14,\n 46763.38,\n 47137.49,\n 47203.24,\n 47159.27,\n 47056.23,\n 47100.85,\n 47156.61,\n 47173.18,\n 46809.02,\n 46897.92,\n 47205.6,\n 47237.25,\n 46932.87,\n 47170.38,\n 47785.67,\n 48573.09,\n 48730.36,\n 48783.96,\n 48693.88,\n 48473.83,\n 48699.36,\n 48664.5,\n 49355.99,\n 49234.71,\n 49347.09,\n 48918.48,\n 49067.49,\n 48874.8,\n 48861.99,\n 48649.63,\n 48883.47,\n 48707.18,\n 48981.88,\n 48905.87,\n 48839.17,\n 49322.15,\n 48547.8,\n 49248.72,\n 49173.7,\n 49034.9,\n 49228.91,\n 49532.76,\n 49699.53,\n 49509.17,\n 49006.57,\n 49030.89,\n 49188.84,\n 49374.86,\n 48864.71,\n 49006.32,\n 49074.47,\n 48875.08,\n 48825.89,\n 49162.91,\n 49161.19,\n 49204.2,\n 49236.73,\n 49038.04,\n 49247.82,\n 49172.55,\n 49174.66,\n 48897.74,\n 48895.67,\n 48283.74,\n 48549.44,\n 48655.37,\n 48713.94,\n 48425.92,\n 48690.42,\n 48422.42,\n 48719.73,\n 49296.77,\n 49285.71,\n 49462.13,\n 49863.07,\n 50094.57,\n 50297.4,\n 50336.36,\n 50257.09,\n 50186.44,\n 50166.01,\n 50286.77,\n 50325.27,\n 50255.84,\n 50419.24,\n 50315.36,\n 50191.83,\n 49745.21,\n 49541.22,\n 49717.45,\n 49433.08,\n 49454.25,\n 49245.07,\n 49514.28,\n 49580.99,\n 49678.94,\n 49497.34,\n 49231.58,\n 48979.66,\n 49203.43,\n 49353.38,\n 49498.28,\n 49586.65,\n 49812.45,\n 49802.19,\n 49710.73,\n 49619.18,\n 49309.04,\n 49448.8,\n 49343.61,\n 48605.35,\n 48517.99,\n 48307.15,\n 47950.0,\n 48005.26,\n 48199.04,\n 48250.0,\n 48348.81,\n 48214.77,\n 48339.62,\n 47696.18,\n 48012.63,\n 48170.82,\n 48212.24,\n 47984.76,\n 48017.23,\n 48519.17,\n 48321.64,\n 48148.93,\n 47285.31,\n 47258.49,\n 47486.37,\n 47584.24,\n 47782.82,\n 47681.49,\n 48452.66,\n 48817.03,\n 48723.44,\n 48838.14,\n 48939.97,\n 48679.66,\n 48689.28,\n 49095.62,\n 48899.09,\n 48997.95,\n 49298.0,\n 48751.0,\n 48706.23,\n 47902.64,\n 47883.51,\n 47068.24,\n 46898.82,\n 46884.01,\n 46918.87,\n 46789.82,\n 46983.59,\n 46852.97,\n 46934.38,\n 47211.44,\n 46844.32,\n 46803.69,\n 46904.37,\n 46968.01,\n 47013.89,\n 46937.04,\n 47030.88,\n 47275.76,\n 47183.79,\n 46846.65,\n 47259.12,\n 47238.38,\n 47235.97,\n 46838.71,\n 46929.71,\n 47123.99,\n 47178.45,\n 47500.69,\n 47199.45,\n 47218.19,\n 47431.63,\n 47474.8,\n 47366.3,\n 47298.15,\n 48151.7,\n 48201.52,\n 48210.89,\n 48193.49,\n 48346.59,\n 48333.74,\n 48917.09,\n 48987.85,\n 49007.6,\n 49075.92,\n 49280.0,\n 49164.28,\n 48961.6,\n 48876.16,\n 48987.24,\n 48897.5,\n 49027.63,\n 48968.93,\n 48952.03,\n 48949.6,\n 48863.33,\n 48701.12,\n 48900.84,\n 48757.64,\n 48661.52,\n 48823.52,\n 48999.19,\n 49044.0,\n 48830.95,\n 48678.34,\n 48761.02,\n 48973.13,\n 48732.24,\n 48932.74,\n 49603.26,\n 49384.07,\n 48616.66,\n 48254.95,\n 48187.85,\n 48351.55,\n 48466.29,\n 48521.86,\n 48524.58,\n 48596.81,\n 48563.81,\n 48189.32,\n 48394.69,\n 48357.78,\n 48254.12,\n 48500.0,\n 48688.91,\n 48814.9,\n 48779.98,\n 48830.61,\n 48924.57,\n 48917.0,\n 49146.77,\n 48790.27,\n 48439.0,\n 48525.63,\n 48424.0,\n 47992.95,\n 47894.73,\n 48036.02,\n 47973.96,\n 47962.26,\n 48108.12,\n 47943.15,\n 48038.59,\n 47949.31,\n 47600.04,\n 47791.66,\n 47707.58,\n 48063.41,\n 48198.05,\n 48346.47,\n 48520.58,\n 48641.77,\n 48641.31,\n 48415.95,\n 47630.36,\n 46994.09,\n 46846.09,\n 46881.35,\n 47025.0,\n 47161.89,\n 47070.0,\n 47157.1,\n 47012.82,\n 47776.36,\n 47759.02,\n 47407.58,\n 47437.64,\n 47995.37,\n 47834.43,\n 47895.03,\n 47265.24,\n 47399.14,\n 47368.0,\n 47119.05,\n 47314.27,\n 47327.76,\n 46999.99,\n 46941.17,\n 47108.35,\n 47124.19,\n 46779.78,\n 46873.99,\n 46897.75,\n 47058.96,\n 47158.98,\n 47450.25,\n 47335.48,\n 47194.09,\n 47369.78,\n 47671.74,\n 47538.28,\n 47734.09,\n 47736.67,\n 47486.73,\n 47308.14,\n 47524.18,\n 48466.84,\n 48761.49,\n 48676.93,\n 48165.26,\n 48275.39,\n 48439.98,\n 48422.34,\n 48846.19,\n 48813.69,\n 49723.65,\n 49594.75,\n 49580.85,\n 49400.24,\n 49470.25,\n 49976.25,\n 49880.82,\n 50124.02,\n 49838.37,\n 50068.12,\n 50101.11,\n 50038.68,\n 50149.79,\n 49554.57,\n 49560.8,\n 49601.49,\n 49540.1,\n ...]"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_baseline, y_val_baseline = BaselinePreviousHour().run_embargo_cv(df_proc)\n",
    "y_pred_baseline"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:32.639609400Z",
     "start_time": "2023-12-04T17:05:32.197610600Z"
    }
   },
   "id": "c807dda6975787d9"
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "0.972053049313982"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(np.array(y_pred_baseline), np.array(y_val_baseline))[0, 1]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:32.733589400Z",
     "start_time": "2023-12-04T17:05:32.638725800Z"
    }
   },
   "id": "e603ab89c4d5432b"
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5382.365544869987\n",
      "2855.044\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(np.array(oof_pred), np.array(oof_valid), squared=False))\n",
    "print(mean_squared_error(np.array(y_pred_baseline, dtype=np.float32), np.array(y_val_baseline, dtype=np.float32), squared=False))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:32.827168700Z",
     "start_time": "2023-12-04T17:05:32.734590300Z"
    }
   },
   "id": "94c5de9865a1c6af"
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [
    {
     "data": {
      "text/plain": "                             ds             y\n0     2021-01-01 06:00:00+00:00  29234.150391\n1     2021-01-01 07:00:00+00:00  29162.500000\n2     2021-01-01 08:00:00+00:00  29076.769531\n3     2021-01-01 09:00:00+00:00  29284.919922\n4     2021-01-01 10:00:00+00:00  29286.630859\n...                         ...           ...\n25605 2023-12-04 13:00:00+00:00  41753.480469\n25606 2023-12-04 14:00:00+00:00  41644.941406\n25607 2023-12-04 15:00:00+00:00  41270.328125\n25608 2023-12-04 16:00:00+00:00  41610.027344\n25609 2023-12-04 17:00:00+00:00  41589.976562\n\n[25610 rows x 2 columns]",
      "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>ds</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2021-01-01 06:00:00+00:00</td>\n      <td>29234.150391</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2021-01-01 07:00:00+00:00</td>\n      <td>29162.500000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2021-01-01 08:00:00+00:00</td>\n      <td>29076.769531</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2021-01-01 09:00:00+00:00</td>\n      <td>29284.919922</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2021-01-01 10:00:00+00:00</td>\n      <td>29286.630859</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>25605</th>\n      <td>2023-12-04 13:00:00+00:00</td>\n      <td>41753.480469</td>\n    </tr>\n    <tr>\n      <th>25606</th>\n      <td>2023-12-04 14:00:00+00:00</td>\n      <td>41644.941406</td>\n    </tr>\n    <tr>\n      <th>25607</th>\n      <td>2023-12-04 15:00:00+00:00</td>\n      <td>41270.328125</td>\n    </tr>\n    <tr>\n      <th>25608</th>\n      <td>2023-12-04 16:00:00+00:00</td>\n      <td>41610.027344</td>\n    </tr>\n    <tr>\n      <th>25609</th>\n      <td>2023-12-04 17:00:00+00:00</td>\n      <td>41589.976562</td>\n    </tr>\n  </tbody>\n</table>\n<p>25610 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc_np = btc.reset_index()\n",
    "df_np = btc.reset_index().loc[:, ['timestamp', 'close']]\n",
    "df_np.columns = ['ds', 'y']\n",
    "df_np"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:26:00.302136100Z",
     "start_time": "2023-12-04T17:26:00.066013900Z"
    }
   },
   "id": "82ae1adf9d7277d0"
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.988% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n",
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n",
      "WARNING - (NP.forecaster.fit) - When Global modeling with local normalization, metrics are displayed in normalized scale.\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.985% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Dataframe freq automatically defined as H\n",
      "INFO - (NP.config.init_data_params) - Setting normalization to global as only one dataframe provided for training.\n",
      "INFO - (NP.config.set_auto_batch_epoch) - Auto-set batch_size to 64\n",
      "INFO - (NP.config.set_auto_batch_epoch) - Auto-set epochs to 86\n"
     ]
    },
    {
     "data": {
      "text/plain": "Finding best initial lr:   0%|          | 0/257 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "c961fbc1af954022b664085423770ca8"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Training: 0it [00:00, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "ae38b0c4e997414a9984d99193aac13f"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "             MAE          RMSE      Loss  RegLoss  epoch\n0   57969.347656  70583.648438  0.672764      0.0      0\n1   41845.128906  51963.093750  0.415436      0.0      1\n2   21490.468750  27191.716797  0.134948      0.0      2\n3   10382.566406  13027.647461  0.029441      0.0      3\n4    7325.110352   9369.751953  0.015341      0.0      4\n5    5363.526855   6983.641113  0.008777      0.0      5\n6    4519.962891   5779.569336  0.006142      0.0      6\n7    4146.595703   5266.720215  0.005081      0.0      7\n8    3880.800293   4927.694824  0.004386      0.0      8\n9    3705.020264   4682.658691  0.003907      0.0      9\n10   3574.118652   4484.502930  0.003556      0.0     10\n11   3460.175537   4336.632812  0.003310      0.0     11\n12   3356.002441   4209.272461  0.003120      0.0     12\n13   3233.317871   4071.898438  0.002925      0.0     13\n14   3101.666260   3940.434570  0.002753      0.0     14\n15   2957.147461   3762.074463  0.002518      0.0     15\n16   2892.472656   3689.486816  0.002444      0.0     16\n17   2840.737061   3629.638184  0.002378      0.0     17\n18   2776.585938   3535.872314  0.002262      0.0     18\n19   2864.328613   3640.139404  0.002421      0.0     19\n20   2802.385986   3561.071533  0.002307      0.0     20\n21   2742.967041   3494.443848  0.002223      0.0     21\n22   2912.012451   3685.715820  0.002502      0.0     22\n23   2876.735596   3638.560791  0.002423      0.0     23\n24   2897.110352   3666.037109  0.002466      0.0     24\n25   2817.975586   3557.632080  0.002309      0.0     25\n26   2852.545654   3609.786621  0.002380      0.0     26\n27   2855.466797   3613.608643  0.002380      0.0     27\n28   2796.964844   3539.579834  0.002274      0.0     28\n29   2854.152588   3621.257812  0.002401      0.0     29\n30   2846.317871   3583.183105  0.002357      0.0     30\n31   2874.774414   3623.978271  0.002419      0.0     31\n32   2719.032715   3454.187988  0.002157      0.0     32\n33   2835.290283   3584.219727  0.002346      0.0     33\n34   2817.011230   3554.515625  0.002314      0.0     34\n35   2788.892090   3528.159668  0.002259      0.0     35\n36   2719.689453   3461.031494  0.002165      0.0     36\n37   2787.394531   3517.684082  0.002256      0.0     37\n38   2688.448975   3407.191650  0.002097      0.0     38\n39   2704.397705   3423.416992  0.002106      0.0     39\n40   2689.814453   3408.888672  0.002086      0.0     40\n41   2752.133057   3477.115967  0.002198      0.0     41\n42   2740.914551   3469.265869  0.002172      0.0     42\n43   2656.346680   3371.596924  0.002040      0.0     43\n44   2671.520752   3395.488770  0.002071      0.0     44\n45   2632.119873   3332.507812  0.002000      0.0     45\n46   2625.604980   3337.157227  0.001995      0.0     46\n47   2618.159668   3326.314697  0.001988      0.0     47\n48   2586.802002   3294.449707  0.001930      0.0     48\n49   2583.943604   3287.279297  0.001929      0.0     49\n50   2534.085938   3222.636963  0.001843      0.0     50\n51   2492.012451   3182.956787  0.001799      0.0     51\n52   2491.415771   3179.018311  0.001787      0.0     52\n53   2510.341553   3198.034668  0.001820      0.0     53\n54   2469.221191   3147.938965  0.001744      0.0     54\n55   2453.924561   3132.968262  0.001736      0.0     55\n56   2447.684082   3121.587891  0.001718      0.0     56\n57   2436.578369   3106.266846  0.001696      0.0     57\n58   2381.877686   3054.822510  0.001634      0.0     58\n59   2372.033936   3038.941650  0.001611      0.0     59\n60   2346.062988   2999.292725  0.001567      0.0     60\n61   2340.913574   2998.841553  0.001564      0.0     61\n62   2330.394531   2985.282715  0.001548      0.0     62\n63   2280.219238   2924.455566  0.001481      0.0     63\n64   2296.447998   2946.624512  0.001509      0.0     64\n65   2297.803223   2949.051025  0.001506      0.0     65\n66   2262.233154   2907.888672  0.001460      0.0     66\n67   2234.652832   2873.557617  0.001422      0.0     67\n68   2223.727295   2860.289062  0.001403      0.0     68\n69   2218.819336   2852.170654  0.001402      0.0     69\n70   2194.520020   2834.074463  0.001379      0.0     70\n71   2183.292969   2817.958984  0.001359      0.0     71\n72   2183.476074   2812.659180  0.001356      0.0     72\n73   2174.000244   2802.465576  0.001344      0.0     73\n74   2148.022705   2775.273438  0.001312      0.0     74\n75   2153.370850   2772.981201  0.001312      0.0     75\n76   2139.551270   2766.576172  0.001305      0.0     76\n77   2135.695801   2756.121582  0.001291      0.0     77\n78   2123.918945   2744.832275  0.001280      0.0     78\n79   2114.754395   2733.782715  0.001268      0.0     79\n80   2107.243896   2726.316162  0.001261      0.0     80\n81   2103.283203   2721.282959  0.001254      0.0     81\n82   2094.362793   2716.834717  0.001247      0.0     82\n83   2095.176514   2710.973633  0.001244      0.0     83\n84   2091.364014   2709.462158  0.001240      0.0     84\n85   2092.496338   2708.945801  0.001240      0.0     85",
      "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>MAE</th>\n      <th>RMSE</th>\n      <th>Loss</th>\n      <th>RegLoss</th>\n      <th>epoch</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>57969.347656</td>\n      <td>70583.648438</td>\n      <td>0.672764</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>41845.128906</td>\n      <td>51963.093750</td>\n      <td>0.415436</td>\n      <td>0.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>21490.468750</td>\n      <td>27191.716797</td>\n      <td>0.134948</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10382.566406</td>\n      <td>13027.647461</td>\n      <td>0.029441</td>\n      <td>0.0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>7325.110352</td>\n      <td>9369.751953</td>\n      <td>0.015341</td>\n      <td>0.0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5363.526855</td>\n      <td>6983.641113</td>\n      <td>0.008777</td>\n      <td>0.0</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>4519.962891</td>\n      <td>5779.569336</td>\n      <td>0.006142</td>\n      <td>0.0</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>4146.595703</td>\n      <td>5266.720215</td>\n      <td>0.005081</td>\n      <td>0.0</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>3880.800293</td>\n      <td>4927.694824</td>\n      <td>0.004386</td>\n      <td>0.0</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>3705.020264</td>\n      <td>4682.658691</td>\n      <td>0.003907</td>\n      <td>0.0</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>3574.118652</td>\n      <td>4484.502930</td>\n      <td>0.003556</td>\n      <td>0.0</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>3460.175537</td>\n      <td>4336.632812</td>\n      <td>0.003310</td>\n      <td>0.0</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>3356.002441</td>\n      <td>4209.272461</td>\n      <td>0.003120</td>\n      <td>0.0</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>3233.317871</td>\n      <td>4071.898438</td>\n      <td>0.002925</td>\n      <td>0.0</td>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>3101.666260</td>\n      <td>3940.434570</td>\n      <td>0.002753</td>\n      <td>0.0</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>2957.147461</td>\n      <td>3762.074463</td>\n      <td>0.002518</td>\n      <td>0.0</td>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>2892.472656</td>\n      <td>3689.486816</td>\n      <td>0.002444</td>\n      <td>0.0</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>2840.737061</td>\n      <td>3629.638184</td>\n      <td>0.002378</td>\n      <td>0.0</td>\n      <td>17</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>2776.585938</td>\n      <td>3535.872314</td>\n      <td>0.002262</td>\n      <td>0.0</td>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>2864.328613</td>\n      <td>3640.139404</td>\n      <td>0.002421</td>\n      <td>0.0</td>\n      <td>19</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>2802.385986</td>\n      <td>3561.071533</td>\n      <td>0.002307</td>\n      <td>0.0</td>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>2742.967041</td>\n      <td>3494.443848</td>\n      <td>0.002223</td>\n      <td>0.0</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>2912.012451</td>\n      <td>3685.715820</td>\n      <td>0.002502</td>\n      <td>0.0</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>2876.735596</td>\n      <td>3638.560791</td>\n      <td>0.002423</td>\n      <td>0.0</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>2897.110352</td>\n      <td>3666.037109</td>\n      <td>0.002466</td>\n      <td>0.0</td>\n      <td>24</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>2817.975586</td>\n      <td>3557.632080</td>\n      <td>0.002309</td>\n      <td>0.0</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>2852.545654</td>\n      <td>3609.786621</td>\n      <td>0.002380</td>\n      <td>0.0</td>\n      <td>26</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>2855.466797</td>\n      <td>3613.608643</td>\n      <td>0.002380</td>\n      <td>0.0</td>\n      <td>27</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>2796.964844</td>\n      <td>3539.579834</td>\n      <td>0.002274</td>\n      <td>0.0</td>\n      <td>28</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>2854.152588</td>\n      <td>3621.257812</td>\n      <td>0.002401</td>\n      <td>0.0</td>\n      <td>29</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>2846.317871</td>\n      <td>3583.183105</td>\n      <td>0.002357</td>\n      <td>0.0</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>2874.774414</td>\n      <td>3623.978271</td>\n      <td>0.002419</td>\n      <td>0.0</td>\n      <td>31</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>2719.032715</td>\n      <td>3454.187988</td>\n      <td>0.002157</td>\n      <td>0.0</td>\n      <td>32</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>2835.290283</td>\n      <td>3584.219727</td>\n      <td>0.002346</td>\n      <td>0.0</td>\n      <td>33</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>2817.011230</td>\n      <td>3554.515625</td>\n      <td>0.002314</td>\n      <td>0.0</td>\n      <td>34</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>2788.892090</td>\n      <td>3528.159668</td>\n      <td>0.002259</td>\n      <td>0.0</td>\n      <td>35</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>2719.689453</td>\n      <td>3461.031494</td>\n      <td>0.002165</td>\n      <td>0.0</td>\n      <td>36</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>2787.394531</td>\n      <td>3517.684082</td>\n      <td>0.002256</td>\n      <td>0.0</td>\n      <td>37</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>2688.448975</td>\n      <td>3407.191650</td>\n      <td>0.002097</td>\n      <td>0.0</td>\n      <td>38</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>2704.397705</td>\n      <td>3423.416992</td>\n      <td>0.002106</td>\n      <td>0.0</td>\n      <td>39</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>2689.814453</td>\n      <td>3408.888672</td>\n      <td>0.002086</td>\n      <td>0.0</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>2752.133057</td>\n      <td>3477.115967</td>\n      <td>0.002198</td>\n      <td>0.0</td>\n      <td>41</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>2740.914551</td>\n      <td>3469.265869</td>\n      <td>0.002172</td>\n      <td>0.0</td>\n      <td>42</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>2656.346680</td>\n      <td>3371.596924</td>\n      <td>0.002040</td>\n      <td>0.0</td>\n      <td>43</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>2671.520752</td>\n      <td>3395.488770</td>\n      <td>0.002071</td>\n      <td>0.0</td>\n      <td>44</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>2632.119873</td>\n      <td>3332.507812</td>\n      <td>0.002000</td>\n      <td>0.0</td>\n      <td>45</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>2625.604980</td>\n      <td>3337.157227</td>\n      <td>0.001995</td>\n      <td>0.0</td>\n      <td>46</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>2618.159668</td>\n      <td>3326.314697</td>\n      <td>0.001988</td>\n      <td>0.0</td>\n      <td>47</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>2586.802002</td>\n      <td>3294.449707</td>\n      <td>0.001930</td>\n      <td>0.0</td>\n      <td>48</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>2583.943604</td>\n      <td>3287.279297</td>\n      <td>0.001929</td>\n      <td>0.0</td>\n      <td>49</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>2534.085938</td>\n      <td>3222.636963</td>\n      <td>0.001843</td>\n      <td>0.0</td>\n      <td>50</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>2492.012451</td>\n      <td>3182.956787</td>\n      <td>0.001799</td>\n      <td>0.0</td>\n      <td>51</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>2491.415771</td>\n      <td>3179.018311</td>\n      <td>0.001787</td>\n      <td>0.0</td>\n      <td>52</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>2510.341553</td>\n      <td>3198.034668</td>\n      <td>0.001820</td>\n      <td>0.0</td>\n      <td>53</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>2469.221191</td>\n      <td>3147.938965</td>\n      <td>0.001744</td>\n      <td>0.0</td>\n      <td>54</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>2453.924561</td>\n      <td>3132.968262</td>\n      <td>0.001736</td>\n      <td>0.0</td>\n      <td>55</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>2447.684082</td>\n      <td>3121.587891</td>\n      <td>0.001718</td>\n      <td>0.0</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>2436.578369</td>\n      <td>3106.266846</td>\n      <td>0.001696</td>\n      <td>0.0</td>\n      <td>57</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>2381.877686</td>\n      <td>3054.822510</td>\n      <td>0.001634</td>\n      <td>0.0</td>\n      <td>58</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>2372.033936</td>\n      <td>3038.941650</td>\n      <td>0.001611</td>\n      <td>0.0</td>\n      <td>59</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>2346.062988</td>\n      <td>2999.292725</td>\n      <td>0.001567</td>\n      <td>0.0</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>2340.913574</td>\n      <td>2998.841553</td>\n      <td>0.001564</td>\n      <td>0.0</td>\n      <td>61</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>2330.394531</td>\n      <td>2985.282715</td>\n      <td>0.001548</td>\n      <td>0.0</td>\n      <td>62</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>2280.219238</td>\n      <td>2924.455566</td>\n      <td>0.001481</td>\n      <td>0.0</td>\n      <td>63</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>2296.447998</td>\n      <td>2946.624512</td>\n      <td>0.001509</td>\n      <td>0.0</td>\n      <td>64</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>2297.803223</td>\n      <td>2949.051025</td>\n      <td>0.001506</td>\n      <td>0.0</td>\n      <td>65</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>2262.233154</td>\n      <td>2907.888672</td>\n      <td>0.001460</td>\n      <td>0.0</td>\n      <td>66</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>2234.652832</td>\n      <td>2873.557617</td>\n      <td>0.001422</td>\n      <td>0.0</td>\n      <td>67</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>2223.727295</td>\n      <td>2860.289062</td>\n      <td>0.001403</td>\n      <td>0.0</td>\n      <td>68</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>2218.819336</td>\n      <td>2852.170654</td>\n      <td>0.001402</td>\n      <td>0.0</td>\n      <td>69</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>2194.520020</td>\n      <td>2834.074463</td>\n      <td>0.001379</td>\n      <td>0.0</td>\n      <td>70</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>2183.292969</td>\n      <td>2817.958984</td>\n      <td>0.001359</td>\n      <td>0.0</td>\n      <td>71</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>2183.476074</td>\n      <td>2812.659180</td>\n      <td>0.001356</td>\n      <td>0.0</td>\n      <td>72</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>2174.000244</td>\n      <td>2802.465576</td>\n      <td>0.001344</td>\n      <td>0.0</td>\n      <td>73</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>2148.022705</td>\n      <td>2775.273438</td>\n      <td>0.001312</td>\n      <td>0.0</td>\n      <td>74</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>2153.370850</td>\n      <td>2772.981201</td>\n      <td>0.001312</td>\n      <td>0.0</td>\n      <td>75</td>\n    </tr>\n    <tr>\n      <th>76</th>\n      <td>2139.551270</td>\n      <td>2766.576172</td>\n      <td>0.001305</td>\n      <td>0.0</td>\n      <td>76</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>2135.695801</td>\n      <td>2756.121582</td>\n      <td>0.001291</td>\n      <td>0.0</td>\n      <td>77</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>2123.918945</td>\n      <td>2744.832275</td>\n      <td>0.001280</td>\n      <td>0.0</td>\n      <td>78</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>2114.754395</td>\n      <td>2733.782715</td>\n      <td>0.001268</td>\n      <td>0.0</td>\n      <td>79</td>\n    </tr>\n    <tr>\n      <th>80</th>\n      <td>2107.243896</td>\n      <td>2726.316162</td>\n      <td>0.001261</td>\n      <td>0.0</td>\n      <td>80</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>2103.283203</td>\n      <td>2721.282959</td>\n      <td>0.001254</td>\n      <td>0.0</td>\n      <td>81</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>2094.362793</td>\n      <td>2716.834717</td>\n      <td>0.001247</td>\n      <td>0.0</td>\n      <td>82</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>2095.176514</td>\n      <td>2710.973633</td>\n      <td>0.001244</td>\n      <td>0.0</td>\n      <td>83</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>2091.364014</td>\n      <td>2709.462158</td>\n      <td>0.001240</td>\n      <td>0.0</td>\n      <td>84</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>2092.496338</td>\n      <td>2708.945801</td>\n      <td>0.001240</td>\n      <td>0.0</td>\n      <td>85</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from neuralprophet import NeuralProphet\n",
    "\n",
    "m = NeuralProphet()\n",
    "\n",
    "btc_np = btc.reset_index()\n",
    "df_np = btc.reset_index().loc[:, ['timestamp', 'close']]\n",
    "df_np.columns = ['ds', 'y']\n",
    "df_np['ds'] = pd.to_datetime(df_np['ds'])\n",
    "df_np['ds'] = df_np['ds'].dt.tz_localize(None)\n",
    "df_np['y'] = df_np['y'].astype(np.float64)\n",
    "df_train, df_test = m.split_df(df_np, freq=\"H\", valid_p=0.2)\n",
    "\n",
    "m.fit(df_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:32:34.087749800Z",
     "start_time": "2023-12-04T17:31:22.715886800Z"
    }
   },
   "id": "7f79f1c5429cea83"
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.98% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.98% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n"
     ]
    },
    {
     "data": {
      "text/plain": "Predicting: 321it [00:00, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "f9908c67f823477a809f540c3b96d1e8"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n"
     ]
    }
   ],
   "source": [
    "np_pred = m.predict(df_test)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:34:24.137276500Z",
     "start_time": "2023-12-04T17:34:23.679026800Z"
    }
   },
   "id": "d1f03ae40e024d12"
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1440x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=plt.figaspect(1/3))\n",
    "ax.plot(np_pred['y'][5075:5100], label='y_true')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:39:04.544387500Z",
     "start_time": "2023-12-04T17:39:04.286068Z"
    }
   },
   "id": "22b541d617700d78"
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1440x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1440x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1440x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=plt.figaspect(1/3))\n",
    "ax.plot(oof_valid, label='valid')\n",
    "ax.plot(oof_pred, label='pred')\n",
    "ax.legend()\n",
    "plt.title(f\"LightGBM\")\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=plt.figaspect(1/3))\n",
    "ax.plot(y_val_baseline, label='valid')\n",
    "ax.plot(y_pred_baseline, label='pred')\n",
    "ax.legend()\n",
    "plt.title(f\"Baseline\")\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=plt.figaspect(1/3))\n",
    "ax.plot(np_pred['y'], label='y_true')\n",
    "ax.plot(np_pred['yhat1'], label='yhat')\n",
    "ax.legend()\n",
    "plt.title(f\"NeuralProphet\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:35:04.637807500Z",
     "start_time": "2023-12-04T17:35:04.035909500Z"
    }
   },
   "id": "3d55aa001b669e4f"
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "[41753.48, 41644.94, 41270.33, 41610.027, 41589.977]"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oof_valid[-5:]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:33.317724Z",
     "start_time": "2023-12-04T17:05:33.223346100Z"
    }
   },
   "id": "45d71cf91015af2b"
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "data": {
      "text/plain": "array([False, False, False, ...,  True,  True,  True])"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_proc.iloc[-len(oof_valid):]['target'].values == oof_valid"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:33.412416800Z",
     "start_time": "2023-12-04T17:05:33.318722200Z"
    }
   },
   "id": "3bfb4874ccc9fedb"
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "(0.9004289589317316, 112670387.49386072)"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = np.array(oof_pred)\n",
    "valid = np.array(oof_valid)\n",
    "len_data = len(pred)\n",
    "mean_pred = pred.mean()\n",
    "mean_valid = valid.mean()\n",
    "var_pred = pred.var(dtype=np.float32)\n",
    "var_valid = valid.var(dtype=np.float32)\n",
    "\n",
    "cov = np.sum((pred * valid)) / len_data - mean_pred * mean_valid\n",
    "corr = cov / np.sqrt(var_pred * var_valid)\n",
    "corr,cov"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:33.506002700Z",
     "start_time": "2023-12-04T17:05:33.412416800Z"
    }
   },
   "id": "89bdfbe4c5ff4373"
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "data": {
      "text/plain": "(106995620.0, 146337180.0)"
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_pred, var_valid"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:33.599430400Z",
     "start_time": "2023-12-04T17:05:33.506002700Z"
    }
   },
   "id": "427bac5d5e8eb97f"
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "numpy.float32"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(np.array(oof_valid)[0])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:33.693353100Z",
     "start_time": "2023-12-04T17:05:33.599430400Z"
    }
   },
   "id": "de7e171f5f43c2b2"
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "146337180.0"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(oof_valid).var(dtype=np.float32)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:33.787904200Z",
     "start_time": "2023-12-04T17:05:33.694322200Z"
    }
   },
   "id": "8bb9c0e551428983"
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "(106995615.01607303, 146337180.0)"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.var(), valid.var()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:33.883589300Z",
     "start_time": "2023-12-04T17:05:33.788911400Z"
    }
   },
   "id": "355e8216b00ca7a3"
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "array([27763173.62498883, 32054303.82835343, 30329726.42576243, ...,\n       35219431.20625952, 32424788.97577543, 30630026.82154284])"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.square(pred - mean_valid)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:33.977412300Z",
     "start_time": "2023-12-04T17:05:33.883589300Z"
    }
   },
   "id": "3c59375a9f6c4d38"
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
      "text/plain": "(106995620.0, 146337180.0)"
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_pred, var_valid"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-04T17:05:34.071580700Z",
     "start_time": "2023-12-04T17:05:33.978413100Z"
    }
   },
   "id": "ca49191a02223488"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}