cnn_lstm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T21:40:18.693223Z",
     "start_time": "2022-04-07T21:40:15.547927Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "from typing import Generator\n",
    "\n",
    "import nest_asyncio\n",
    "import shutil\n",
    "from functools import partial\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import rapidjson\n",
    "from alive_progress import alive_bar\n",
    "from keras.optimizer_v2.adam import Adam\n",
    "from lazyft.data_loader import load_pair_data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from cnn_model import create_cnn_alternative, create_cnn\n",
    "\n",
    "from preprocessing import preprocess, create_dataflow_dataframe\n",
    "\n",
    "import keras\n",
    "import keras_tuner\n",
    "import keras_tuner as kt\n",
    "from keras import layers, losses\n",
    "from keras.backend import clear_session\n",
    "from keras.callbacks import ReduceLROnPlateau\n",
    "from keras.layers import Dense\n",
    "from keras_preprocessing.image import ImageDataGenerator\n",
    "from numba import cuda\n",
    "import util\n",
    "from data_models import Profile, Mode\n",
    "\n",
    "nest_asyncio.apply()\n",
    "\n",
    "# jt -t oceans16 -f roboto -fs 12 -cellw 100%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T21:10:53.086097Z",
     "start_time": "2022-04-07T21:10:53.082991Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "hp = kt.HyperParameters()\n",
    "image_size = 224\n",
    "\n",
    "split = 0\n",
    "profile = Profile(\n",
    "    pair=\"BTC/USDT\",\n",
    "    train_timerange=\"20210102-20211231\",\n",
    "    test_timerange=\"20211212-\",\n",
    "    timeframes=[\"5m\", \"15m\", \"30m\", \"1h\"],\n",
    "    # timeframes=[\"1h\", \"4h\", \"8h\", \"1d\"],\n",
    "    image_size=image_size,\n",
    "    batch_size=64,\n",
    "    # download_interval='15m',\n",
    ")\n",
    "profile.ensure_directories_exist()\n",
    "time_step = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "def zscore(input_data: pd.Series):\n",
    "    pass\n",
    "\n",
    "\n",
    "def de_zscore(input_data: pd.Series, standardized_value: float):\n",
    "    pass"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-11 07:55:41.234 | INFO     | lazyft.downloader:download_missing_historical_data:302 - Checking if download is needed for BTC/USDT @ 1h interval(s)\n",
      "2022-04-11 07:55:41.239 | INFO     | lazyft.downloader:download:371 - Downloading 1561 days worth of market data for BTC/USDT @ 1h ticker-interval(s)...\n",
      "Downloading pair data |                                        | ▃▅▇ 0/1 [0%] in 4s (0.0/s, eta: -) 2022-04-11 07:55:45.728 | INFO     | lazyft.downloader:download_watcher:442 - Downloaded history for BTC/USDT @ 1h (1/1)\n",
      "Downloading pair data |████████████████████████████████████████| 1/1 [100%] in 4.5s (0.22/s)                            \n",
      "2022-04-11 07:55:45.904 | INFO     | lazyft.downloader:download_missing_historical_data:334 - Finished downloading data for 1 pairs @ 1h\n",
      "2022-04-11 07:55:45.905 | INFO     | lazyft.downloader:download_missing_historical_data:339 - Data is up to date\n",
      "2022-04-11 07:55:45.966 | INFO     | lazyft.data_loader:load_pair_data:40 - Loaded 37474 rows for BTC/USDT, data starts at 2018-01-01 00:00:00+00:00\n"
     ]
    }
   ],
   "source": [
    "data = load_pair_data(profile.pair, '1h', timerange='20180101-')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|    | date                      |    open |    high |     low |   close |   volume |       roc |   answer |\n",
      "|---:|:--------------------------|--------:|--------:|--------:|--------:|---------:|----------:|---------:|\n",
      "|  1 | 2018-01-01 01:00:00+00:00 | 13529   | 13595.9 | 13155.4 | 13203.1 |  383.697 | -2.40927  |  13330.2 |\n",
      "|  2 | 2018-01-01 02:00:00+00:00 | 13203   | 13418.4 | 13200   | 13330.2 |  429.065 |  0.962807 |  13410   |\n",
      "|  3 | 2018-01-01 03:00:00+00:00 | 13330.3 | 13611.3 | 13290   | 13410   |  420.087 |  0.599017 |  13601   |\n",
      "|  4 | 2018-01-01 04:00:00+00:00 | 13435   | 13623.3 | 13322.1 | 13601   |  340.807 |  1.42416  |  13559   |\n",
      "|  5 | 2018-01-01 05:00:00+00:00 | 13615.2 | 13699   | 13526.5 | 13559   |  404.229 | -0.308948 |  13780.4 |\n",
      "|████████████████████████████████████████| 37453/37453 [100%] in 21.8s (1719.05/s)                                      \n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from pandas_ta import zscore, roc\n",
    "\n",
    "nd = data.copy()\n",
    "nd['roc'] = roc(nd['close'], length=1)\n",
    "nd['answer'] = nd['close'].shift(-1)\n",
    "nd = nd.dropna()\n",
    "print(nd.head(5).to_markdown())\n",
    "drop: pd.DataFrame = nd.drop(columns=['date'])\n",
    "\n",
    "# for k, s in drop.items():\n",
    "#     s_as_n = s.to_numpy().reshape((1,-1))\n",
    "#     scaler=  ss.fit(s_as_n)\n",
    "#     nd.loc[:, k] = scaler.transform(s_as_n)\n",
    "# gb = data.groupby(np.arange(len(data)) // 10)\n",
    "gb = util.roll(nd, time_step, sort=False, group_keys=False)\n",
    "x_input = np.zeros((len(gb), time_step, 6))\n",
    "y_answers = np.zeros((len(gb), time_step))\n",
    "with alive_bar(len(gb), force_tty=True) as bar:\n",
    "    for i, d in gb:\n",
    "        # for k, s in d.items():\n",
    "        #     s_as_n = s.to_numpy().reshape((1,-1))\n",
    "        #     scaler = ss.fit(s_as_n)\n",
    "        #     nd.loc[:, k] = scaler.transform(s_as_n)\n",
    "        # print(d.head(10))\n",
    "        dropped = d.drop(columns=['date']).to_numpy()\n",
    "        scaler: StandardScaler = StandardScaler().fit(dropped)\n",
    "        x_scaled = scaler.transform(dropped)\n",
    "        answers = x_scaled[:, -1]\n",
    "        x_input[i - 1] = x_scaled[:, :-1]\n",
    "        # print(x_scaled, '\\n', x_scaled.shape)\n",
    "        y_answers[i - 1] = answers\n",
    "        bar()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_input, y_answers, test_size=0.2, random_state=1)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training input samples: 22471\n",
      "Validation input samples: 7491\n",
      "Testing input samples: 7491\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    f'\\nTraining input samples: {len(X_train)}',\n",
    "    f'Validation input samples: {len(X_val)}',\n",
    "    f'Testing input samples: {len(X_test)}',\n",
    "    sep='\\n'\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Build"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "outputs": [],
   "source": [
    "learning_rate_reduction = ReduceLROnPlateau(\n",
    "    monitor=\"val_loss\", patience=3, verbose=0, factor=0.5, min_lr=0.00001\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "outputs": [],
   "source": [
    "def build_model(hp: kt.HyperParameters = kt.HyperParameters()):\n",
    "    kernel_size = hp.Choice('conv1_kernel', values=[1, 3, 5, 7], default=1)\n",
    "    conv1_filters = hp.Int('conv1_filters', min_value=16, max_value=128, step=16, default=32)\n",
    "    lstm_units = hp.Int('lstm_units', min_value=32, max_value=256, default=64, step=16)\n",
    "    model = keras.Sequential()\n",
    "    input_shape = X_train[0].shape\n",
    "    model.add(layers.Input(input_shape))\n",
    "    model.add(layers.Conv1D(conv1_filters, kernel_size=kernel_size, activation='tanh', padding='same'))\n",
    "    model.add(layers.MaxPooling1D(pool_size=1, padding='same'))\n",
    "    model.add(layers.LSTM(lstm_units, activation='tanh'))\n",
    "    model.add(layers.Dense(1))\n",
    "    model.compile(optimizer=Adam(learning_rate=0.001), loss='mae')\n",
    "    # model.summary(expand_nested=True)\n",
    "    return model"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "outputs": [],
   "source": [
    "tuner = kt.BayesianOptimization(\n",
    "    build_model,\n",
    "    objective='val_loss',\n",
    "    max_trials=10,\n",
    "    overwrite=True,\n",
    "    directory=\"tuner\",\n",
    "    project_name=profile.datestamp,\n",
    "    executions_per_trial=1,\n",
    "    # hyperparameters=hp\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T16:05:53.329418Z",
     "start_time": "2022-04-07T16:04:15.069287Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 10 Complete [00h 00m 29s]\n",
      "val_loss: 0.7937811613082886\n",
      "\n",
      "Best val_loss So Far: 0.7937811613082886\n",
      "Total elapsed time: 00h 04m 22s\n",
      "INFO:tensorflow:Oracle triggered exit\n"
     ]
    }
   ],
   "source": [
    "\n",
    "tuner.search(\n",
    "    X_train, y_train,\n",
    "    validation_data=(X_val, y_val),\n",
    "    batch_size=64,\n",
    "    epochs=10,\n",
    "    # callbacks=[keras.callbacks.TensorBoard(\"tuner/tb/\" + profile.datestamp)],\n",
    ")\n",
    "clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "model = build_model()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T20:32:53.457067Z",
     "start_time": "2022-04-07T20:32:04.785329Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "352/352 [==============================] - 3s 7ms/step - loss: 0.7929 - val_loss: 0.7932 - lr: 0.0010\n",
      "Epoch 2/100\n",
      "352/352 [==============================] - 2s 4ms/step - loss: 0.7929 - val_loss: 0.7932 - lr: 0.0010\n",
      "Epoch 3/100\n",
      "352/352 [==============================] - 1s 4ms/step - loss: 0.7929 - val_loss: 0.7932 - lr: 0.0010\n",
      "Epoch 4/100\n",
      "352/352 [==============================] - 1s 4ms/step - loss: 0.7929 - val_loss: 0.7932 - lr: 0.0010\n",
      "Epoch 5/100\n",
      "352/352 [==============================] - 1s 4ms/step - loss: 0.7927 - val_loss: 0.7931 - lr: 5.0000e-04\n",
      "Epoch 6/100\n",
      "352/352 [==============================] - 1s 4ms/step - loss: 0.7927 - val_loss: 0.7931 - lr: 5.0000e-04\n",
      "Epoch 7/100\n",
      "352/352 [==============================] - 1s 4ms/step - loss: 0.7927 - val_loss: 0.7931 - lr: 5.0000e-04\n",
      "Epoch 8/100\n",
      "352/352 [==============================] - 2s 4ms/step - loss: 0.7927 - val_loss: 0.7931 - lr: 5.0000e-04\n",
      "Epoch 9/100\n",
      "352/352 [==============================] - 2s 5ms/step - loss: 0.7926 - val_loss: 0.7931 - lr: 2.5000e-04\n",
      "Epoch 10/100\n",
      "352/352 [==============================] - 2s 4ms/step - loss: 0.7926 - val_loss: 0.7931 - lr: 2.5000e-04\n",
      "Epoch 11/100\n",
      "352/352 [==============================] - 2s 6ms/step - loss: 0.7926 - val_loss: 0.7931 - lr: 2.5000e-04\n",
      "Epoch 12/100\n",
      "352/352 [==============================] - 2s 5ms/step - loss: 0.7926 - val_loss: 0.7931 - lr: 1.2500e-04\n",
      "Epoch 13/100\n",
      "352/352 [==============================] - 2s 5ms/step - loss: 0.7926 - val_loss: 0.7931 - lr: 1.2500e-04\n",
      "Epoch 14/100\n",
      "352/352 [==============================] - 1s 4ms/step - loss: 0.7926 - val_loss: 0.7931 - lr: 1.2500e-04\n",
      "Epoch 15/100\n",
      "352/352 [==============================] - 2s 4ms/step - loss: 0.7925 - val_loss: 0.7931 - lr: 6.2500e-05\n",
      "Epoch 16/100\n",
      "352/352 [==============================] - 2s 4ms/step - loss: 0.7925 - val_loss: 0.7931 - lr: 6.2500e-05\n",
      "Epoch 17/100\n",
      "352/352 [==============================] - 2s 5ms/step - loss: 0.7925 - val_loss: 0.7931 - lr: 6.2500e-05\n",
      "Epoch 18/100\n",
      "352/352 [==============================] - 1s 4ms/step - loss: 0.7925 - val_loss: 0.7931 - lr: 3.1250e-05\n",
      "Epoch 19/100\n",
      "352/352 [==============================] - 1s 4ms/step - loss: 0.7925 - val_loss: 0.7931 - lr: 3.1250e-05\n",
      "Epoch 20/100\n",
      "352/352 [==============================] - 1s 4ms/step - loss: 0.7925 - val_loss: 0.7931 - lr: 3.1250e-05\n",
      "Epoch 21/100\n",
      "352/352 [==============================] - 1s 4ms/step - loss: 0.7925 - val_loss: 0.7931 - lr: 1.5625e-05\n",
      "Epoch 22/100\n",
      "352/352 [==============================] - 1s 4ms/step - loss: 0.7925 - val_loss: 0.7931 - lr: 1.5625e-05\n",
      "Epoch 23/100\n",
      "352/352 [==============================] - 2s 4ms/step - loss: 0.7925 - val_loss: 0.7931 - lr: 1.5625e-05\n",
      "Epoch 24/100\n",
      "352/352 [==============================] - 1s 4ms/step - loss: 0.7925 - val_loss: 0.7931 - lr: 1.0000e-05\n",
      "Epoch 25/100\n",
      "352/352 [==============================] - 1s 4ms/step - loss: 0.7925 - val_loss: 0.7931 - lr: 1.0000e-05\n",
      "Epoch 26/100\n",
      "352/352 [==============================] - 2s 5ms/step - loss: 0.7925 - val_loss: 0.7931 - lr: 1.0000e-05\n",
      "Epoch 27/100\n",
      "191/352 [===============>..............] - ETA: 0s - loss: 0.7918"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "Input \u001B[0;32mIn [93]\u001B[0m, in \u001B[0;36m<cell line: 1>\u001B[0;34m()\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43mmodel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m      2\u001B[0m \u001B[43m    \u001B[49m\u001B[43mX_train\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my_train\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mepochs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m100\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m      3\u001B[0m \u001B[43m    \u001B[49m\u001B[43mvalidation_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mX_val\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my_val\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m      4\u001B[0m \u001B[43m    \u001B[49m\u001B[43mbatch_size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m64\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m      5\u001B[0m \u001B[43m    \u001B[49m\u001B[43mcallbacks\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\u001B[43mlearning_rate_reduction\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m      6\u001B[0m \u001B[43m)\u001B[49m\n\u001B[1;32m      7\u001B[0m clear_session()\n",
      "File \u001B[0;32m~/PycharmProjects/cnn_gaf/venv/lib/python3.9/site-packages/keras/utils/traceback_utils.py:64\u001B[0m, in \u001B[0;36mfilter_traceback.<locals>.error_handler\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m     62\u001B[0m filtered_tb \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m     63\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m---> 64\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfn\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m     65\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:  \u001B[38;5;66;03m# pylint: disable=broad-except\u001B[39;00m\n\u001B[1;32m     66\u001B[0m   filtered_tb \u001B[38;5;241m=\u001B[39m _process_traceback_frames(e\u001B[38;5;241m.\u001B[39m__traceback__)\n",
      "File \u001B[0;32m~/PycharmProjects/cnn_gaf/venv/lib/python3.9/site-packages/keras/engine/training.py:1384\u001B[0m, in \u001B[0;36mModel.fit\u001B[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001B[0m\n\u001B[1;32m   1377\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m tf\u001B[38;5;241m.\u001B[39mprofiler\u001B[38;5;241m.\u001B[39mexperimental\u001B[38;5;241m.\u001B[39mTrace(\n\u001B[1;32m   1378\u001B[0m     \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtrain\u001B[39m\u001B[38;5;124m'\u001B[39m,\n\u001B[1;32m   1379\u001B[0m     epoch_num\u001B[38;5;241m=\u001B[39mepoch,\n\u001B[1;32m   1380\u001B[0m     step_num\u001B[38;5;241m=\u001B[39mstep,\n\u001B[1;32m   1381\u001B[0m     batch_size\u001B[38;5;241m=\u001B[39mbatch_size,\n\u001B[1;32m   1382\u001B[0m     _r\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m):\n\u001B[1;32m   1383\u001B[0m   callbacks\u001B[38;5;241m.\u001B[39mon_train_batch_begin(step)\n\u001B[0;32m-> 1384\u001B[0m   tmp_logs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtrain_function\u001B[49m\u001B[43m(\u001B[49m\u001B[43miterator\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   1385\u001B[0m   \u001B[38;5;28;01mif\u001B[39;00m data_handler\u001B[38;5;241m.\u001B[39mshould_sync:\n\u001B[1;32m   1386\u001B[0m     context\u001B[38;5;241m.\u001B[39masync_wait()\n",
      "File \u001B[0;32m~/PycharmProjects/cnn_gaf/venv/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py:150\u001B[0m, in \u001B[0;36mfilter_traceback.<locals>.error_handler\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m    148\u001B[0m filtered_tb \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m    149\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 150\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfn\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    151\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m    152\u001B[0m   filtered_tb \u001B[38;5;241m=\u001B[39m _process_traceback_frames(e\u001B[38;5;241m.\u001B[39m__traceback__)\n",
      "File \u001B[0;32m~/PycharmProjects/cnn_gaf/venv/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py:915\u001B[0m, in \u001B[0;36mFunction.__call__\u001B[0;34m(self, *args, **kwds)\u001B[0m\n\u001B[1;32m    912\u001B[0m compiler \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mxla\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_jit_compile \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnonXla\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    914\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m OptionalXlaContext(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_jit_compile):\n\u001B[0;32m--> 915\u001B[0m   result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwds\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    917\u001B[0m new_tracing_count \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mexperimental_get_tracing_count()\n\u001B[1;32m    918\u001B[0m without_tracing \u001B[38;5;241m=\u001B[39m (tracing_count \u001B[38;5;241m==\u001B[39m new_tracing_count)\n",
      "File \u001B[0;32m~/PycharmProjects/cnn_gaf/venv/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py:947\u001B[0m, in \u001B[0;36mFunction._call\u001B[0;34m(self, *args, **kwds)\u001B[0m\n\u001B[1;32m    944\u001B[0m   \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_lock\u001B[38;5;241m.\u001B[39mrelease()\n\u001B[1;32m    945\u001B[0m   \u001B[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001B[39;00m\n\u001B[1;32m    946\u001B[0m   \u001B[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001B[39;00m\n\u001B[0;32m--> 947\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_stateless_fn\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwds\u001B[49m\u001B[43m)\u001B[49m  \u001B[38;5;66;03m# pylint: disable=not-callable\u001B[39;00m\n\u001B[1;32m    948\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_stateful_fn \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m    949\u001B[0m   \u001B[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001B[39;00m\n\u001B[1;32m    950\u001B[0m   \u001B[38;5;66;03m# in parallel.\u001B[39;00m\n\u001B[1;32m    951\u001B[0m   \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_lock\u001B[38;5;241m.\u001B[39mrelease()\n",
      "File \u001B[0;32m~/PycharmProjects/cnn_gaf/venv/lib/python3.9/site-packages/tensorflow/python/eager/function.py:2956\u001B[0m, in \u001B[0;36mFunction.__call__\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m   2953\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_lock:\n\u001B[1;32m   2954\u001B[0m   (graph_function,\n\u001B[1;32m   2955\u001B[0m    filtered_flat_args) \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_maybe_define_function(args, kwargs)\n\u001B[0;32m-> 2956\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mgraph_function\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_flat\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m   2957\u001B[0m \u001B[43m    \u001B[49m\u001B[43mfiltered_flat_args\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcaptured_inputs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mgraph_function\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcaptured_inputs\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/PycharmProjects/cnn_gaf/venv/lib/python3.9/site-packages/tensorflow/python/eager/function.py:1853\u001B[0m, in \u001B[0;36mConcreteFunction._call_flat\u001B[0;34m(self, args, captured_inputs, cancellation_manager)\u001B[0m\n\u001B[1;32m   1849\u001B[0m possible_gradient_type \u001B[38;5;241m=\u001B[39m gradients_util\u001B[38;5;241m.\u001B[39mPossibleTapeGradientTypes(args)\n\u001B[1;32m   1850\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m (possible_gradient_type \u001B[38;5;241m==\u001B[39m gradients_util\u001B[38;5;241m.\u001B[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001B[1;32m   1851\u001B[0m     \u001B[38;5;129;01mand\u001B[39;00m executing_eagerly):\n\u001B[1;32m   1852\u001B[0m   \u001B[38;5;66;03m# No tape is watching; skip to running the function.\u001B[39;00m\n\u001B[0;32m-> 1853\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_build_call_outputs(\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_inference_function\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m   1854\u001B[0m \u001B[43m      \u001B[49m\u001B[43mctx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcancellation_manager\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcancellation_manager\u001B[49m\u001B[43m)\u001B[49m)\n\u001B[1;32m   1855\u001B[0m forward_backward \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_select_forward_and_backward_functions(\n\u001B[1;32m   1856\u001B[0m     args,\n\u001B[1;32m   1857\u001B[0m     possible_gradient_type,\n\u001B[1;32m   1858\u001B[0m     executing_eagerly)\n\u001B[1;32m   1859\u001B[0m forward_function, args_with_tangents \u001B[38;5;241m=\u001B[39m forward_backward\u001B[38;5;241m.\u001B[39mforward()\n",
      "File \u001B[0;32m~/PycharmProjects/cnn_gaf/venv/lib/python3.9/site-packages/tensorflow/python/eager/function.py:499\u001B[0m, in \u001B[0;36m_EagerDefinedFunction.call\u001B[0;34m(self, ctx, args, cancellation_manager)\u001B[0m\n\u001B[1;32m    497\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m _InterpolateFunctionError(\u001B[38;5;28mself\u001B[39m):\n\u001B[1;32m    498\u001B[0m   \u001B[38;5;28;01mif\u001B[39;00m cancellation_manager \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m--> 499\u001B[0m     outputs \u001B[38;5;241m=\u001B[39m \u001B[43mexecute\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    500\u001B[0m \u001B[43m        \u001B[49m\u001B[38;5;28;43mstr\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msignature\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mname\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    501\u001B[0m \u001B[43m        \u001B[49m\u001B[43mnum_outputs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_num_outputs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    502\u001B[0m \u001B[43m        \u001B[49m\u001B[43minputs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    503\u001B[0m \u001B[43m        \u001B[49m\u001B[43mattrs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mattrs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    504\u001B[0m \u001B[43m        \u001B[49m\u001B[43mctx\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mctx\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    505\u001B[0m   \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m    506\u001B[0m     outputs \u001B[38;5;241m=\u001B[39m execute\u001B[38;5;241m.\u001B[39mexecute_with_cancellation(\n\u001B[1;32m    507\u001B[0m         \u001B[38;5;28mstr\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msignature\u001B[38;5;241m.\u001B[39mname),\n\u001B[1;32m    508\u001B[0m         num_outputs\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_num_outputs,\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    511\u001B[0m         ctx\u001B[38;5;241m=\u001B[39mctx,\n\u001B[1;32m    512\u001B[0m         cancellation_manager\u001B[38;5;241m=\u001B[39mcancellation_manager)\n",
      "File \u001B[0;32m~/PycharmProjects/cnn_gaf/venv/lib/python3.9/site-packages/tensorflow/python/eager/execute.py:54\u001B[0m, in \u001B[0;36mquick_execute\u001B[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001B[0m\n\u001B[1;32m     52\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m     53\u001B[0m   ctx\u001B[38;5;241m.\u001B[39mensure_initialized()\n\u001B[0;32m---> 54\u001B[0m   tensors \u001B[38;5;241m=\u001B[39m \u001B[43mpywrap_tfe\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mTFE_Py_Execute\u001B[49m\u001B[43m(\u001B[49m\u001B[43mctx\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_handle\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdevice_name\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mop_name\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m     55\u001B[0m \u001B[43m                                      \u001B[49m\u001B[43minputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mattrs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnum_outputs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m     56\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m core\u001B[38;5;241m.\u001B[39m_NotOkStatusException \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m     57\u001B[0m   \u001B[38;5;28;01mif\u001B[39;00m name \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "source": [
    "model.fit(\n",
    "    X_train, y_train, epochs=100,\n",
    "    validation_data=(X_val, y_val),\n",
    "    batch_size=64,\n",
    "    callbacks=[learning_rate_reduction],\n",
    ")\n",
    "clear_session()"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### VGG 16\n",
    "<img src=\"https://neurohive.io/wp-content/uploads/2018/11/vgg16-1-e1542731207177.png\" width=\"500\" height=\"300\" />"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from keras.applications.vgg16 import VGG16\n",
    "\n",
    "vgg16 = VGG16(\n",
    "    include_top=False,\n",
    "    weights=None,\n",
    "    input_shape=test_img[0].shape,\n",
    "    classifier_activation='softmax'\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T20:33:02.444934Z",
     "start_time": "2022-04-07T20:33:00.964809Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/15 [==============================] - 1s 88ms/step - loss: 0.1240 - accuracy: 0.9641\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.1239885613322258, 0.9640591740608215]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(validate_generator)\n",
    "clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T19:25:44.697452Z",
     "start_time": "2022-04-07T19:25:44.663308Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.73703267e-03, 9.98262942e-01],\n",
       "       [1.65773654e-05, 9.99983430e-01],\n",
       "       [6.44575746e-04, 9.99355376e-01],\n",
       "       [9.99999523e-01, 5.23124640e-07],\n",
       "       [9.99993682e-01, 6.29351462e-06],\n",
       "       [9.99997139e-01, 2.81512689e-06],\n",
       "       [1.88813135e-02, 9.81118679e-01],\n",
       "       [3.22594610e-03, 9.96774018e-01],\n",
       "       [9.97419238e-01, 2.58078892e-03],\n",
       "       [8.33522121e-04, 9.99166489e-01],\n",
       "       [9.53888595e-01, 4.61113565e-02],\n",
       "       [1.20793134e-02, 9.87920642e-01],\n",
       "       [4.00322629e-03, 9.95996833e-01],\n",
       "       [3.96189193e-04, 9.99603808e-01],\n",
       "       [3.31265875e-03, 9.96687353e-01],\n",
       "       [1.51765504e-04, 9.99848247e-01],\n",
       "       [2.36065313e-03, 9.97639418e-01],\n",
       "       [2.10060249e-03, 9.97899413e-01],\n",
       "       [9.99999046e-01, 9.70398446e-07],\n",
       "       [9.99909043e-01, 9.09386072e-05],\n",
       "       [9.99989748e-01, 1.02635622e-05],\n",
       "       [1.69562735e-02, 9.83043730e-01],\n",
       "       [9.99999285e-01, 7.63856178e-07],\n",
       "       [9.85830963e-01, 1.41690485e-02],\n",
       "       [9.99999762e-01, 2.38877107e-07],\n",
       "       [2.45678565e-03, 9.97543216e-01],\n",
       "       [3.30792321e-03, 9.96692061e-01],\n",
       "       [9.99997854e-01, 2.20407628e-06],\n",
       "       [4.46248706e-03, 9.95537519e-01],\n",
       "       [9.98372614e-01, 1.62740180e-03],\n",
       "       [2.57825898e-03, 9.97421741e-01],\n",
       "       [9.14800679e-04, 9.99085188e-01]], dtype=float32)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(test_generator)\n",
    "clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T20:35:13.614021Z",
     "start_time": "2022-04-07T20:35:12.742014Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-07 16:35:13.063686: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: 90pcta/assets\n",
      "2022-04-07 16:35:13.612 | INFO     | data_models:save:115 - Saved profile to 90pcta/profile.json\n"
     ]
    }
   ],
   "source": [
    "model.save(\"90pcta\")\n",
    "profile.save(Path(\"90pcta\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Summarize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T16:06:02.061468Z",
     "start_time": "2022-04-07T16:06:02.050557Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'conv1_units': 32, 'conv1_kernel': 3, 'conv2_units': 112, 'conv2_kernel': 3, 'conv3_units': 128, 'conv3_kernel': 3, 'conv4_units': 96, 'conv4_kernel': 5, 'conv5_units': 128, 'conv5_kernel': 5, 'conv6_units': 32, 'conv6_kernel': 5, 'conv7_units': 32, 'conv7_kernel': 3, 'dropout1': 0.0, 'dropout2': 0.5, 'loss': 'mean_absolute_error'}\n"
     ]
    }
   ],
   "source": [
    "# print(tuner.results_summary())\n",
    "print(tuner.get_best_hyperparameters()[0].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T16:06:09.230492Z",
     "start_time": "2022-04-07T16:06:08.635939Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88/88 [==============================] - 8s 92ms/step - loss: 0.7006 - accuracy: 0.5434\n"
     ]
    }
   ],
   "source": [
    "best_model = tuner.get_best_models()[0]\n",
    "scores = best_model.evaluate(test_generator, return_dict=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 19:14:10.606 | INFO     | data_models:save:117 - Saved profile to tuner/20220408181048/profile.json\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 36, 36, 32)        2432      \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 36, 36, 32)       128       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 32, 32, 256)       205056    \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 32, 32, 256)      1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 16, 16, 32)        73760     \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 16, 16, 32)       128       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 16, 16, 32)        0         \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 12, 12, 128)       102528    \n",
      "                                                                 \n",
      " batch_normalization_3 (Batc  (None, 12, 12, 128)      512       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 10, 10, 256)       295168    \n",
      "                                                                 \n",
      " batch_normalization_4 (Batc  (None, 10, 10, 256)      1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 5, 5, 32)          73760     \n",
      "                                                                 \n",
      " batch_normalization_5 (Batc  (None, 5, 5, 32)         128       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 5, 5, 32)          0         \n",
      "                                                                 \n",
      " conv2d_6 (Conv2D)           (None, 1, 1, 208)         166608    \n",
      "                                                                 \n",
      " batch_normalization_6 (Batc  (None, 1, 1, 208)        832       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 208)               0         \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 208)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 2)                 418       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 923,506\n",
      "Trainable params: 921,618\n",
      "Non-trainable params: 1,888\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "if scores[\"accuracy\"] > 0.54:\n",
    "    best_model.save(Path(\"tuner\", profile.datestamp, \"best_model.h5\"))\n",
    "    profile.save(directory=Path(\"tuner\", profile.datestamp))\n",
    "    best_model.summary()\n",
    "else:\n",
    "    shutil.rmtree(Path(\"tuner\", profile.datestamp))\n",
    "clear_session()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T16:43:17.086723Z",
     "start_time": "2022-04-07T16:43:16.070812Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-07 12:43:16.752140: I tensorflow/stream_executor/cuda/cuda_dnn.cc:368] Loaded cuDNN version 8302\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 809ms/step - loss: 0.4992 - accuracy: 0.6250\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-07 12:43:17.019377: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2022-04-07 12:43:17.019846: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2022-04-07 12:43:17.019863: W tensorflow/stream_executor/gpu/asm_compiler.cc:80] Couldn't get ptxas version string: INTERNAL: Couldn't invoke ptxas --version\n",
      "2022-04-07 12:43:17.020327: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2022-04-07 12:43:17.020355: W tensorflow/stream_executor/gpu/redzone_allocator.cc:314] INTERNAL: Failed to launch ptxas\n",
      "Relying on driver to perform ptx compilation. \n",
      "Modify $PATH to customize ptxas location.\n",
      "This message will be only logged once.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'loss': 0.49920588731765747, 'accuracy': 0.625}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = util.load_specific_tuner_model(\"tuner/20220407120059\")[0][1]\n",
    "model.evaluate(test_generator, return_dict=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Backtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T21:21:43.511827Z",
     "start_time": "2022-04-07T21:21:43.507206Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# del best_model\n",
    "from lazyft.command_parameters import BacktestParameters\n",
    "\n",
    "params = BacktestParameters(\n",
    "    config_path=\"user_data/config.json\",\n",
    "    # timerange='20211121-20211212',\n",
    "    # timerange='20210110-20210131',\n",
    "    # timerange='20220102-20220116',\n",
    "    timerange=\"20220101-\",\n",
    "    interval=profile.min_tf,\n",
    "    # days=365,\n",
    "    stake_amount=\"unlimited\",\n",
    "    starting_balance=1000,\n",
    "    max_open_trades=1,\n",
    "    pairs=[\n",
    "        \"BTC/USDT\",\n",
    "        # 'ETH/USDT',\n",
    "    ],\n",
    "    download_data=True,\n",
    "    custom_settings={\n",
    "        \"min_buy_threshold\": 0.5,\n",
    "        \"specific_model\":    \"tuner/20220408181048\",\n",
    "        #         \"timeframes\": profile.timeframes,\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T21:22:05.527524Z",
     "start_time": "2022-04-07T21:21:44.464479Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-09 02:28:35.754 | INFO     | lazyft.parameter_tools:remove_params_file:36 - Removing strategy params: /home/raphael/PycharmProjects/cnn_gaf/user_data/strategies/my_gym_cnn2.json\n",
      "2022-04-09 02:28:35.784 | INFO     | lazyft.strategy:save_strategy_text_to_database:188 - Strategy SagesGymCNN2 already in database...skipping\n",
      "2022-04-09 02:28:35.785 | INFO     | lazyft.backtest.runner:update_spaces:384 - Updating custom spaces...\n",
      "2022-04-09 02:28:36.913 | INFO     | lazyft.downloader:download_missing_historical_data:302 - Checking if download is needed for BTC/USDT, BTC/USDT @ 5m interval(s)\n",
      "2022-04-09 02:28:36.916 | INFO     | lazyft.downloader:download_missing_historical_data:339 - Data is up to date\n",
      "2022-04-09 02:28:37.999 | INFO     | lazyft.backtest.runner:pre_execute:179 - Running command: \"freqtrade backtesting -c /home/raphael/PycharmProjects/cnn_gaf/user_data/config.json --user-data-dir /home/raphael/PycharmProjects/cnn_gaf/user_data --strategy-path /home/raphael/PycharmProjects/cnn_gaf/user_data/strategies --timerange 20220101- --pairs BTC/USDT --starting-balance 1000 --stake-amount unlimited --max-open-trades 1 --timeframe 5m -s SagesGymCNN2\"\n",
      "2022-04-09 02:28:38.000 | INFO     | lazyft.backtest.runner:pre_execute:182 - Backtesting SagesGymCNN2 with params id \"null\" - 4478348ad3214e26891a7e5e57615abe\n",
      "WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-09 02:28:38.863641: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-09 02:28:38.885467: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-09 02:28:38.885580: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-09 02:28:38.885933: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-04-09 02:28:38.886267: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-09 02:28:38.886363: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-09 02:28:38.886449: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-09 02:28:39.173461: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-09 02:28:39.173600: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-09 02:28:39.173695: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-09 02:28:39.173771: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6219 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1070 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-09 02:28:42.935 | INFO     | writer:fill_writer_queue:61 - Creating writer queue... | Save folder: /tmp/BTC_USDT/1640707200-1649455200/5m_15m_30m_1h\n",
      "Filename: /home/raphael/PycharmProjects/cnn_gaf/preprocessing.py\n",
      "\n",
      "Line #    Mem usage    Increment  Occurrences   Line Contents\n",
      "=============================================================\n",
      "    90  1383.40 MiB  1383.40 MiB           1   @memory_profiler.profile(precision=2)\n",
      "    91                                         def preprocess_prediction(custom_timeframes, df, profile):\n",
      "    92                                             \"\"\"\n",
      "    93                                             It takes a dataframe, splits it into timeframes, writes the images to disk, and returns the path to\n",
      "    94                                             the folder where the images are stored\n",
      "    95                                         \n",
      "    96                                             :param custom_timeframes: a list of timeframes to use for prediction. If you want to use all the\n",
      "    97                                             timeframes in the profile, leave this as None\n",
      "    98                                             :param df: the dataframe of the data you want to predict on\n",
      "    99                                             :param profile: the profile object that contains all the parameters for the model\n",
      "   100                                             :return: The parent folder of the temporary folder and a list of the keys of the quad_series\n",
      "   101                                             dictionary.\n",
      "   102                                             \"\"\"\n",
      "   103  1440.09 MiB    56.36 MiB           2       quad_series = split_timeframes(\n",
      "   104  1383.40 MiB     0.00 MiB           1           df,\n",
      "   105  1383.40 MiB     0.00 MiB           1           profile.window_size,\n",
      "   106  1383.40 MiB     0.00 MiB           1           profile.timeframes,\n",
      "   107  1383.73 MiB     0.33 MiB           1           util.get_dates_from_df(df, profile.max_tf),\n",
      "   108  1383.73 MiB     0.00 MiB           1           download_interval=profile.download_interval,\n",
      "   109  1383.73 MiB     0.00 MiB           1           custom_timeframes=custom_timeframes,\n",
      "   110                                             )\n",
      "   111  1440.09 MiB     0.00 MiB           1       start_date = df.iloc[0][\"date\"]\n",
      "   112  1440.09 MiB     0.00 MiB           1       end_date = df.iloc[-1][\"date\"]\n",
      "   113  1440.09 MiB     0.00 MiB           2       tmp_folder = Path(\n",
      "   114  1440.09 MiB     0.00 MiB           1           '/tmp',\n",
      "   115  1440.09 MiB     0.00 MiB           1           profile.formatted_pair,\n",
      "   116  1440.09 MiB     0.00 MiB           1           f'{int(start_date.timestamp())}-{int(end_date.timestamp())}',\n",
      "   117  1440.09 MiB     0.00 MiB           1           \"_\".join(profile.timeframes),\n",
      "   118                                             )\n",
      "   119  1440.09 MiB     0.00 MiB           1       tmp_folder.mkdir(exist_ok=True, parents=True)\n",
      "   120  1440.09 MiB     0.00 MiB           1       write_images(fill_writer_queue(quad_series, tmp_folder))\n",
      "   121  1440.09 MiB     0.00 MiB           1       return tmp_folder.parent, list(quad_series.keys())\n",
      "\n",
      "\n",
      "Found 2411 validated image filenames belonging to 1 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-09 02:28:44.233430: I tensorflow/stream_executor/cuda/cuda_dnn.cc:368] Loaded cuDNN version 8302\n",
      "2022-04-09 02:28:44.548863: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2022-04-09 02:28:44.549243: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2022-04-09 02:28:44.549256: W tensorflow/stream_executor/gpu/asm_compiler.cc:80] Couldn't get ptxas version string: INTERNAL: Couldn't invoke ptxas --version\n",
      "2022-04-09 02:28:44.549599: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2022-04-09 02:28:44.549626: W tensorflow/stream_executor/gpu/redzone_allocator.cc:314] INTERNAL: Failed to launch ptxas\n",
      "Relying on driver to perform ptx compilation. \n",
      "Modify $PATH to customize ptxas location.\n",
      "This message will be only logged once.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "202112291200 -> 0 -> 0\n",
      "202112291300 -> 1 -> 1\n",
      "202112291400 -> 1 -> 1\n",
      "202112291500 -> 1 -> 1\n",
      "202112291600 -> 1 -> 1\n",
      "202112291700 -> 0 -> 0\n",
      "202112291800 -> 0 -> 0\n",
      "202112291900 -> 1 -> 1\n",
      "202112292000 -> 1 -> 1\n",
      "202112292100 -> 1 -> 1\n",
      "202112292200 -> 1 -> 1\n",
      "202112292300 -> 1 -> 1\n",
      "202112300000 -> 0 -> 0\n",
      "202112300100 -> 1 -> 1\n",
      "202112300200 -> 1 -> 1\n",
      "202112300300 -> 0 -> 0\n",
      "202112300400 -> 1 -> 1\n",
      "202112300500 -> 1 -> 1\n",
      "202112300600 -> 1 -> 1\n",
      "202112300700 -> 0 -> 0\n",
      "202112300800 -> 0 -> 0\n",
      "202112300900 -> 1 -> 1\n",
      "202112301000 -> 0 -> 0\n",
      "202112301100 -> 1 -> 1\n",
      "202112301200 -> 1 -> 1\n",
      "202112301300 -> 1 -> 1\n",
      "202112301400 -> 0 -> 0\n",
      "202112301500 -> 0 -> 0\n",
      "202112301600 -> 0 -> 0\n",
      "202112301700 -> 1 -> 1\n",
      "202112301800 -> 1 -> 1\n",
      "202112301900 -> 1 -> 1\n",
      "202112302000 -> 1 -> 1\n",
      "202112302100 -> 1 -> 1\n",
      "202112302200 -> 1 -> 1\n",
      "202112302300 -> 1 -> 1\n",
      "202112310000 -> 1 -> 1\n",
      "202112310100 -> 1 -> 1\n",
      "202112310200 -> 0 -> 0\n",
      "202112310300 -> 1 -> 1\n",
      "202112310400 -> 0 -> 0\n",
      "202112310500 -> 1 -> 1\n",
      "202112310600 -> 1 -> 1\n",
      "202112310700 -> 1 -> 1\n",
      "202112310800 -> 1 -> 1\n",
      "202112310900 -> 1 -> 1\n",
      "202112311000 -> 1 -> 1\n",
      "202112311100 -> 1 -> 1\n",
      "202112311200 -> 1 -> 1\n",
      "202112311300 -> 1 -> 1\n",
      "202112311400 -> 1 -> 1\n",
      "202112311500 -> 1 -> 1\n",
      "202112311600 -> 1 -> 1\n",
      "202112311700 -> 1 -> 1\n",
      "202112311800 -> 1 -> 1\n",
      "202112311900 -> 1 -> 1\n",
      "202112312000 -> 1 -> 1\n",
      "202112312100 -> 1 -> 1\n",
      "202112312200 -> 1 -> 1\n",
      "202112312300 -> 1 -> 1\n",
      "202201010000 -> 1 -> 1\n",
      "202201010100 -> 1 -> 1\n",
      "202201010200 -> 1 -> 1\n",
      "202201010300 -> 0 -> 0\n",
      "202201010400 -> 0 -> 0\n",
      "202201010500 -> 0 -> 0\n",
      "202201010600 -> 1 -> 1\n",
      "202201010700 -> 1 -> 1\n",
      "202201010800 -> 1 -> 1\n",
      "202201010900 -> 1 -> 1\n",
      "202201011000 -> 1 -> 1\n",
      "202201011100 -> 1 -> 1\n",
      "202201011200 -> 1 -> 1\n",
      "202201011300 -> 1 -> 1\n",
      "202201011400 -> 1 -> 1\n",
      "202201011500 -> 1 -> 1\n",
      "202201011600 -> 1 -> 1\n",
      "202201011700 -> 1 -> 1\n",
      "202201011800 -> 1 -> 1\n",
      "202201011900 -> 1 -> 1\n",
      "202201012000 -> 0 -> 0\n",
      "202201012100 -> 0 -> 0\n",
      "202201012200 -> 1 -> 1\n",
      "202201012300 -> 1 -> 1\n",
      "202201020000 -> 1 -> 1\n",
      "202201020100 -> 1 -> 1\n",
      "202201020200 -> 1 -> 1\n",
      "202201020300 -> 0 -> 0\n",
      "202201020400 -> 0 -> 0\n",
      "202201020500 -> 0 -> 0\n",
      "202201020600 -> 0 -> 0\n",
      "202201020700 -> 1 -> 1\n",
      "202201020800 -> 1 -> 1\n",
      "202201020900 -> 0 -> 0\n",
      "202201021000 -> 1 -> 1\n",
      "202201021100 -> 0 -> 0\n",
      "202201021200 -> 0 -> 0\n",
      "202201021300 -> 0 -> 0\n",
      "202201021400 -> 0 -> 0\n",
      "202201021500 -> 1 -> 1\n",
      "202201021600 -> 1 -> 1\n",
      "202201021700 -> 1 -> 1\n",
      "202201021800 -> 1 -> 1\n",
      "202201021900 -> 1 -> 1\n",
      "202201022000 -> 1 -> 1\n",
      "202201022100 -> 1 -> 1\n",
      "202201022200 -> 1 -> 1\n",
      "202201022300 -> 1 -> 1\n",
      "202201030000 -> 1 -> 1\n",
      "202201030100 -> 1 -> 1\n",
      "202201030200 -> 1 -> 1\n",
      "202201030300 -> 0 -> 0\n",
      "202201030400 -> 0 -> 0\n",
      "202201030500 -> 0 -> 0\n",
      "202201030600 -> 0 -> 0\n",
      "202201030700 -> 1 -> 1\n",
      "202201030800 -> 1 -> 1\n",
      "202201030900 -> 1 -> 1\n",
      "202201031000 -> 0 -> 0\n",
      "202201031100 -> 0 -> 0\n",
      "202201031200 -> 0 -> 0\n",
      "202201031300 -> 0 -> 0\n",
      "202201031400 -> 0 -> 0\n",
      "202201031500 -> 0 -> 0\n",
      "202201031600 -> 1 -> 1\n",
      "202201031700 -> 1 -> 1\n",
      "202201031800 -> 1 -> 1\n",
      "202201031900 -> 0 -> 0\n",
      "202201032000 -> 0 -> 0\n",
      "202201032100 -> 1 -> 1\n",
      "202201032200 -> 1 -> 1\n",
      "202201032300 -> 1 -> 1\n",
      "202201040000 -> 1 -> 1\n",
      "202201040100 -> 0 -> 0\n",
      "202201040200 -> 1 -> 1\n",
      "202201040300 -> 0 -> 0\n",
      "202201040400 -> 0 -> 0\n",
      "202201040500 -> 0 -> 0\n",
      "202201040600 -> 1 -> 1\n",
      "202201040700 -> 1 -> 1\n",
      "202201040800 -> 1 -> 1\n",
      "202201040900 -> 1 -> 1\n",
      "202201041000 -> 1 -> 1\n",
      "202201041100 -> 1 -> 1\n",
      "202201041200 -> 0 -> 0\n",
      "202201041300 -> 0 -> 0\n",
      "202201041400 -> 1 -> 1\n",
      "202201041500 -> 1 -> 1\n",
      "202201041600 -> 1 -> 1\n",
      "202201041700 -> 1 -> 1\n",
      "202201041800 -> 1 -> 1\n",
      "202201041900 -> 1 -> 1\n",
      "202201042000 -> 0 -> 0\n",
      "202201042100 -> 0 -> 0\n",
      "202201042200 -> 0 -> 0\n",
      "202201042300 -> 0 -> 0\n",
      "202201050000 -> 0 -> 0\n",
      "202201050100 -> 0 -> 0\n",
      "202201050200 -> 0 -> 0\n",
      "202201050300 -> 1 -> 1\n",
      "202201050400 -> 0 -> 0\n",
      "202201050500 -> 1 -> 1\n",
      "202201050600 -> 1 -> 1\n",
      "202201050700 -> 0 -> 0\n",
      "202201050800 -> 0 -> 0\n",
      "202201050900 -> 0 -> 0\n",
      "202201051000 -> 0 -> 0\n",
      "202201051100 -> 1 -> 1\n",
      "202201051200 -> 1 -> 1\n",
      "202201051300 -> 1 -> 1\n",
      "202201051400 -> 1 -> 1\n",
      "202201051500 -> 0 -> 0\n",
      "202201051600 -> 0 -> 0\n",
      "202201051700 -> 0 -> 0\n",
      "202201051800 -> 0 -> 0\n",
      "202201051900 -> 1 -> 1\n",
      "202201052000 -> 0 -> 0\n",
      "202201052100 -> 1 -> 1\n",
      "202201052200 -> 0 -> 0\n",
      "202201052300 -> 1 -> 1\n",
      "202201060000 -> 0 -> 0\n",
      "202201060100 -> 0 -> 0\n",
      "202201060200 -> 0 -> 0\n",
      "202201060300 -> 1 -> 1\n",
      "202201060400 -> 1 -> 1\n",
      "202201060500 -> 1 -> 1\n",
      "202201060600 -> 1 -> 1\n",
      "202201060700 -> 1 -> 1\n",
      "202201060800 -> 1 -> 1\n",
      "202201060900 -> 1 -> 1\n",
      "202201061000 -> 1 -> 1\n",
      "202201061100 -> 1 -> 1\n",
      "202201061200 -> 1 -> 1\n",
      "202201061300 -> 1 -> 1\n",
      "202201061400 -> 1 -> 1\n",
      "202201061500 -> 1 -> 1\n",
      "202201061600 -> 1 -> 1\n",
      "202201061700 -> 1 -> 1\n",
      "202201061800 -> 1 -> 1\n",
      "202201061900 -> 1 -> 1\n",
      "202201062000 -> 0 -> 0\n",
      "202201062100 -> 1 -> 1\n",
      "202201062200 -> 1 -> 1\n",
      "202201062300 -> 1 -> 1\n",
      "202201070000 -> 0 -> 0\n",
      "202201070100 -> 0 -> 0\n",
      "202201070200 -> 0 -> 0\n",
      "202201070300 -> 0 -> 0\n",
      "202201070400 -> 0 -> 0\n",
      "202201070500 -> 1 -> 1\n",
      "202201070600 -> 1 -> 1\n",
      "202201070700 -> 1 -> 1\n",
      "202201070800 -> 0 -> 0\n",
      "202201070900 -> 0 -> 0\n",
      "202201071000 -> 1 -> 1\n",
      "202201071100 -> 1 -> 1\n",
      "202201071200 -> 0 -> 0\n",
      "202201071300 -> 1 -> 1\n",
      "202201071400 -> 1 -> 1\n",
      "202201071500 -> 1 -> 1\n",
      "202201071600 -> 0 -> 0\n",
      "202201071700 -> 0 -> 0\n",
      "202201071800 -> 0 -> 0\n",
      "202201071900 -> 1 -> 1\n",
      "202201072000 -> 1 -> 1\n",
      "202201072100 -> 1 -> 1\n",
      "202201072200 -> 1 -> 1\n",
      "202201072300 -> 1 -> 1\n",
      "202201080000 -> 1 -> 1\n",
      "202201080100 -> 1 -> 1\n",
      "202201080200 -> 1 -> 1\n",
      "202201080300 -> 1 -> 1\n",
      "202201080400 -> 1 -> 1\n",
      "202201080500 -> 0 -> 0\n",
      "202201080600 -> 1 -> 1\n",
      "202201080700 -> 1 -> 1\n",
      "202201080800 -> 1 -> 1\n",
      "202201080900 -> 1 -> 1\n",
      "202201081000 -> 0 -> 0\n",
      "202201081100 -> 1 -> 1\n",
      "202201081200 -> 1 -> 1\n",
      "202201081300 -> 0 -> 0\n",
      "202201081400 -> 0 -> 0\n",
      "202201081500 -> 1 -> 1\n",
      "202201081600 -> 1 -> 1\n",
      "202201081700 -> 1 -> 1\n",
      "202201081800 -> 1 -> 1\n",
      "202201081900 -> 1 -> 1\n",
      "202201082000 -> 0 -> 0\n",
      "202201082100 -> 0 -> 0\n",
      "202201082200 -> 0 -> 0\n",
      "202201082300 -> 1 -> 1\n",
      "202201090000 -> 1 -> 1\n",
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      "202201101500 -> 1 -> 1\n",
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      "202201102000 -> 1 -> 1\n",
      "202201102100 -> 1 -> 1\n",
      "202201102200 -> 1 -> 1\n",
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      "202201110200 -> 0 -> 0\n",
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      "202201110400 -> 1 -> 1\n",
      "202201110500 -> 1 -> 1\n",
      "202201110600 -> 1 -> 1\n",
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      "202201110800 -> 0 -> 0\n",
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      "202201111100 -> 1 -> 1\n",
      "202201111200 -> 1 -> 1\n",
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      "202201111500 -> 0 -> 0\n",
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      "202201111900 -> 1 -> 1\n",
      "202201112000 -> 1 -> 1\n",
      "202201112100 -> 1 -> 1\n",
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      "202201112300 -> 1 -> 1\n",
      "202201120000 -> 0 -> 0\n",
      "202201120100 -> 1 -> 1\n",
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      "202201120300 -> 0 -> 0\n",
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      "202201120700 -> 1 -> 1\n",
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      "202201120900 -> 1 -> 1\n",
      "202201121000 -> 1 -> 1\n",
      "202201121100 -> 1 -> 1\n",
      "202201121200 -> 0 -> 0\n",
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      "202201121500 -> 1 -> 1\n",
      "202201121600 -> 1 -> 1\n",
      "202201121700 -> 1 -> 1\n",
      "202201121800 -> 1 -> 1\n",
      "202201121900 -> 0 -> 0\n",
      "202201122000 -> 0 -> 0\n",
      "202201122100 -> 0 -> 0\n",
      "202201122200 -> 0 -> 0\n",
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      "202201130100 -> 0 -> 0\n",
      "202201130200 -> 0 -> 0\n",
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      "202201130400 -> 0 -> 0\n",
      "202201130500 -> 1 -> 1\n",
      "202201130600 -> 1 -> 1\n",
      "202201130700 -> 1 -> 1\n",
      "202201130800 -> 0 -> 0\n",
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      "202201131000 -> 0 -> 0\n",
      "202201131100 -> 0 -> 0\n",
      "202201131200 -> 1 -> 1\n",
      "202201131300 -> 0 -> 0\n",
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      "202201131500 -> 1 -> 1\n",
      "202201131600 -> 0 -> 0\n",
      "202201131700 -> 0 -> 0\n",
      "202201131800 -> 0 -> 0\n",
      "202201131900 -> 0 -> 0\n",
      "202201132000 -> 0 -> 0\n",
      "202201132100 -> 1 -> 1\n",
      "202201132200 -> 1 -> 1\n",
      "202201132300 -> 1 -> 1\n",
      "202201140000 -> 0 -> 0\n",
      "202201140100 -> 1 -> 1\n",
      "202201140200 -> 0 -> 0\n",
      "202201140300 -> 0 -> 0\n",
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      "202201140500 -> 0 -> 0\n",
      "202201140600 -> 0 -> 0\n",
      "202201140700 -> 1 -> 1\n",
      "202201140800 -> 0 -> 0\n",
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      "202201141000 -> 0 -> 0\n",
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      "202201141200 -> 0 -> 0\n",
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      "202201141400 -> 1 -> 1\n",
      "202201141500 -> 1 -> 1\n",
      "202201141600 -> 1 -> 1\n",
      "202201141700 -> 1 -> 1\n",
      "202201141800 -> 0 -> 0\n",
      "202201141900 -> 1 -> 1\n",
      "202201142000 -> 1 -> 1\n",
      "202201142100 -> 1 -> 1\n",
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      "202201150000 -> 1 -> 1\n",
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      "202201150200 -> 0 -> 0\n",
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      "202201150600 -> 1 -> 1\n",
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      "202201150800 -> 1 -> 1\n",
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      "202201151000 -> 1 -> 1\n",
      "202201151100 -> 0 -> 0\n",
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      "202201151500 -> 1 -> 1\n",
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      "202201151900 -> 1 -> 1\n",
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      "202201152100 -> 0 -> 0\n",
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      "202201160700 -> 1 -> 1\n",
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      "202201161000 -> 1 -> 1\n",
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      "202201161200 -> 0 -> 0\n",
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      "202201161600 -> 1 -> 1\n",
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      "202201162100 -> 1 -> 1\n",
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      "202201162300 -> 0 -> 0\n",
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      "202202220000 -> 0 -> 0\n",
      "202202220100 -> 1 -> 1\n",
      "202202220200 -> 0 -> 0\n",
      "202202220300 -> 0 -> 0\n",
      "202202220400 -> 0 -> 0\n",
      "202202220500 -> 0 -> 0\n",
      "202202220600 -> 0 -> 0\n",
      "202202220700 -> 0 -> 0\n",
      "202202220800 -> 0 -> 0\n",
      "202202220900 -> 0 -> 0\n",
      "202202221000 -> 0 -> 0\n",
      "202202221100 -> 1 -> 1\n",
      "202202221200 -> 1 -> 1\n",
      "202202221300 -> 1 -> 1\n",
      "202202221400 -> 1 -> 1\n",
      "202202221500 -> 0 -> 0\n",
      "202202221600 -> 0 -> 0\n",
      "202202221700 -> 0 -> 0\n",
      "202202221800 -> 1 -> 1\n",
      "202202221900 -> 1 -> 1\n",
      "202202222000 -> 1 -> 1\n",
      "202202222100 -> 1 -> 1\n",
      "202202222200 -> 1 -> 1\n",
      "202202222300 -> 1 -> 1\n",
      "202202230000 -> 1 -> 1\n",
      "202202230100 -> 0 -> 0\n",
      "202202230200 -> 1 -> 1\n",
      "202202230300 -> 1 -> 1\n",
      "202202230400 -> 1 -> 1\n",
      "202202230500 -> 1 -> 1\n",
      "202202230600 -> 1 -> 1\n",
      "202202230700 -> 1 -> 1\n",
      "202202230800 -> 0 -> 0\n",
      "202202230900 -> 1 -> 1\n",
      "202202231000 -> 1 -> 1\n",
      "202202231100 -> 1 -> 1\n",
      "202202231200 -> 1 -> 1\n",
      "202202231300 -> 1 -> 1\n",
      "202202231400 -> 1 -> 1\n",
      "202202231500 -> 1 -> 1\n",
      "202202231600 -> 1 -> 1\n",
      "202202231700 -> 1 -> 1\n",
      "202202231800 -> 1 -> 1\n",
      "202202231900 -> 0 -> 0\n",
      "202202232000 -> 0 -> 0\n",
      "202202232100 -> 0 -> 0\n",
      "202202232200 -> 0 -> 0\n",
      "202202232300 -> 1 -> 1\n",
      "202202240000 -> 0 -> 0\n",
      "202202240100 -> 0 -> 0\n",
      "202202240200 -> 0 -> 0\n",
      "202202240300 -> 0 -> 0\n",
      "202202240400 -> 1 -> 1\n",
      "202202240500 -> 1 -> 1\n",
      "202202240600 -> 1 -> 1\n",
      "202202240700 -> 1 -> 1\n",
      "202202240800 -> 1 -> 1\n",
      "202202240900 -> 1 -> 1\n",
      "202202241000 -> 1 -> 1\n",
      "202202241100 -> 1 -> 1\n",
      "202202241200 -> 1 -> 1\n",
      "202202241300 -> 1 -> 1\n",
      "202202241400 -> 1 -> 1\n",
      "202202241500 -> 0 -> 0\n",
      "202202241600 -> 0 -> 0\n",
      "202202241700 -> 1 -> 1\n",
      "202202241800 -> 0 -> 0\n",
      "202202241900 -> 1 -> 1\n",
      "202202242000 -> 1 -> 1\n",
      "202202242100 -> 1 -> 1\n",
      "202202242200 -> 0 -> 0\n",
      "202202242300 -> 0 -> 0\n",
      "202202250000 -> 0 -> 0\n",
      "202202250100 -> 0 -> 0\n",
      "202202250200 -> 0 -> 0\n",
      "202202250300 -> 0 -> 0\n",
      "202202250400 -> 0 -> 0\n",
      "202202250500 -> 1 -> 1\n",
      "202202250600 -> 1 -> 1\n",
      "202202250700 -> 1 -> 1\n",
      "202202250800 -> 1 -> 1\n",
      "202202250900 -> 1 -> 1\n",
      "202202251000 -> 1 -> 1\n",
      "202202251100 -> 0 -> 0\n",
      "202202251200 -> 0 -> 0\n",
      "202202251300 -> 0 -> 0\n",
      "202202251400 -> 0 -> 0\n",
      "202202251500 -> 1 -> 1\n",
      "202202251600 -> 1 -> 1\n",
      "202202251700 -> 1 -> 1\n",
      "202202251800 -> 1 -> 1\n",
      "202202251900 -> 1 -> 1\n",
      "202202252000 -> 0 -> 0\n",
      "202202252100 -> 0 -> 0\n",
      "202202252200 -> 0 -> 0\n",
      "202202252300 -> 0 -> 0\n",
      "202202260000 -> 1 -> 1\n",
      "202202260100 -> 1 -> 1\n",
      "202202260200 -> 0 -> 0\n",
      "202202260300 -> 0 -> 0\n",
      "202202260400 -> 0 -> 0\n",
      "202202260500 -> 0 -> 0\n",
      "202202260600 -> 1 -> 1\n",
      "202202260700 -> 1 -> 1\n",
      "202202260800 -> 1 -> 1\n",
      "202202260900 -> 1 -> 1\n",
      "202202261000 -> 1 -> 1\n",
      "202202261100 -> 0 -> 0\n",
      "202202261200 -> 0 -> 0\n",
      "202202261300 -> 0 -> 0\n",
      "202202261400 -> 1 -> 1\n",
      "202202261500 -> 1 -> 1\n",
      "202202261600 -> 1 -> 1\n",
      "202202261700 -> 1 -> 1\n",
      "202202261800 -> 1 -> 1\n",
      "202202261900 -> 0 -> 0\n",
      "202202262000 -> 1 -> 1\n",
      "202202262100 -> 1 -> 1\n",
      "202202262200 -> 1 -> 1\n",
      "202202262300 -> 1 -> 1\n",
      "202202270000 -> 1 -> 1\n",
      "202202270100 -> 1 -> 1\n",
      "202202270200 -> 0 -> 0\n",
      "202202270300 -> 0 -> 0\n",
      "202202270400 -> 0 -> 0\n",
      "202202270500 -> 0 -> 0\n",
      "202202270600 -> 0 -> 0\n",
      "202202270700 -> 0 -> 0\n",
      "202202270800 -> 1 -> 1\n",
      "202202270900 -> 1 -> 1\n",
      "202202271000 -> 1 -> 1\n",
      "202202271100 -> 1 -> 1\n",
      "202202271200 -> 0 -> 0\n",
      "202202271300 -> 0 -> 0\n",
      "202202271400 -> 0 -> 0\n",
      "202202271500 -> 0 -> 0\n",
      "202202271600 -> 0 -> 0\n",
      "202202271700 -> 0 -> 0\n",
      "202202271800 -> 0 -> 0\n",
      "202202271900 -> 0 -> 0\n",
      "202202272000 -> 0 -> 0\n",
      "202202272100 -> 0 -> 0\n",
      "202202272200 -> 0 -> 0\n",
      "202202272300 -> 1 -> 1\n",
      "202202280000 -> 1 -> 1\n",
      "202202280100 -> 1 -> 1\n",
      "202202280200 -> 1 -> 1\n",
      "202202280300 -> 1 -> 1\n",
      "202202280400 -> 0 -> 0\n",
      "202202280500 -> 0 -> 0\n",
      "202202280600 -> 0 -> 0\n",
      "202202280700 -> 0 -> 0\n",
      "202202280800 -> 0 -> 0\n",
      "202202280900 -> 0 -> 0\n",
      "202202281000 -> 0 -> 0\n",
      "202202281100 -> 1 -> 1\n",
      "202202281200 -> 0 -> 0\n",
      "202202281300 -> 0 -> 0\n",
      "202202281400 -> 1 -> 1\n",
      "202202281500 -> 0 -> 0\n",
      "202202281600 -> 0 -> 0\n",
      "202202281700 -> 1 -> 1\n",
      "202202281800 -> 1 -> 1\n",
      "202202281900 -> 1 -> 1\n",
      "202202282000 -> 1 -> 1\n",
      "202202282100 -> 1 -> 1\n",
      "202202282200 -> 1 -> 1\n",
      "202202282300 -> 1 -> 1\n",
      "202203010000 -> 0 -> 0\n",
      "202203010100 -> 0 -> 0\n",
      "202203010200 -> 0 -> 0\n",
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      "202203010400 -> 0 -> 0\n",
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      "202203010600 -> 0 -> 0\n",
      "202203010700 -> 0 -> 0\n",
      "202203010800 -> 0 -> 0\n",
      "202203010900 -> 1 -> 1\n",
      "202203011000 -> 1 -> 1\n",
      "202203011100 -> 1 -> 1\n",
      "202203011200 -> 1 -> 1\n",
      "202203011300 -> 1 -> 1\n",
      "202203011400 -> 0 -> 0\n",
      "202203011500 -> 0 -> 0\n",
      "202203011600 -> 1 -> 1\n",
      "202203011700 -> 0 -> 0\n",
      "202203011800 -> 0 -> 0\n",
      "202203011900 -> 0 -> 0\n",
      "202203012000 -> 0 -> 0\n",
      "202203012100 -> 0 -> 0\n",
      "202203012200 -> 1 -> 1\n",
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      "202203020000 -> 0 -> 0\n",
      "202203020100 -> 1 -> 1\n",
      "202203020200 -> 0 -> 0\n",
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      "202203021800 -> 1 -> 1\n",
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      "202203030800 -> 0 -> 0\n",
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      "202203031100 -> 0 -> 0\n",
      "202203031200 -> 1 -> 1\n",
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      "202203031500 -> 0 -> 0\n",
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      "202203140800 -> 0 -> 0\n",
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      "202203151500 -> 1 -> 1\n",
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      "202203171500 -> 0 -> 0\n",
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      "202203180700 -> 1 -> 1\n",
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      "202203181800 -> 0 -> 0\n",
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      "202203190000 -> 1 -> 1\n",
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      "202203190800 -> 0 -> 0\n",
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      "202204060600 -> 0 -> 0\n",
      "202204060700 -> 0 -> 0\n",
      "202204060800 -> 0 -> 0\n",
      "202204060900 -> 0 -> 0\n",
      "202204061000 -> 0 -> 0\n",
      "202204061100 -> 0 -> 0\n",
      "202204061200 -> 1 -> 1\n",
      "202204061300 -> 1 -> 1\n",
      "202204061400 -> 1 -> 1\n",
      "202204061500 -> 1 -> 1\n",
      "202204061600 -> 1 -> 1\n",
      "202204061700 -> 1 -> 1\n",
      "202204061800 -> 1 -> 1\n",
      "202204061900 -> 1 -> 1\n",
      "202204062000 -> 1 -> 1\n",
      "202204062100 -> 1 -> 1\n",
      "202204062200 -> 1 -> 1\n",
      "202204062300 -> 0 -> 0\n",
      "202204070000 -> 0 -> 0\n",
      "202204070100 -> 1 -> 1\n",
      "202204070200 -> 1 -> 1\n",
      "202204070300 -> 0 -> 0\n",
      "202204070400 -> 0 -> 0\n",
      "202204070500 -> 0 -> 0\n",
      "202204070600 -> 0 -> 0\n",
      "202204070700 -> 1 -> 1\n",
      "202204070800 -> 1 -> 1\n",
      "202204070900 -> 1 -> 1\n",
      "202204071000 -> 1 -> 1\n",
      "202204071100 -> 1 -> 1\n",
      "202204071200 -> 0 -> 0\n",
      "202204071300 -> 0 -> 0\n",
      "202204071400 -> 1 -> 1\n",
      "202204071500 -> 0 -> 0\n",
      "202204071600 -> 0 -> 0\n",
      "202204071700 -> 0 -> 0\n",
      "202204071800 -> 1 -> 1\n",
      "202204071900 -> 1 -> 1\n",
      "202204072000 -> 1 -> 1\n",
      "202204072100 -> 0 -> 0\n",
      "202204072200 -> 0 -> 0\n",
      "202204072300 -> 1 -> 1\n",
      "202204080000 -> 0 -> 0\n",
      "202204080100 -> 0 -> 0\n",
      "202204080200 -> 0 -> 0\n",
      "202204080300 -> 0 -> 0\n",
      "202204080400 -> 1 -> 1\n",
      "202204080500 -> 1 -> 1\n",
      "202204080600 -> 1 -> 1\n",
      "202204080700 -> 1 -> 1\n",
      "202204080800 -> 1 -> 1\n",
      "202204080900 -> 1 -> 1\n",
      "202204081000 -> 1 -> 1\n",
      "202204081100 -> 1 -> 1\n",
      "202204081200 -> 1 -> 1\n",
      "202204081300 -> 1 -> 1\n",
      "202204081400 -> 1 -> 1\n",
      "202204081500 -> 1 -> 1\n",
      "202204081600 -> 1 -> 1\n",
      "202204081700 -> 0 -> 0\n",
      "202204081800 -> 0 -> 0\n",
      "202204081900 -> 1 -> 1\n",
      "202204082000 -> 1 -> 1\n",
      "202204082100 -> 0 -> 0\n",
      "202204082200 -> 0 -> 0\n",
      "0.000    27731\n",
      "1.000     1428\n",
      "Name: buy, dtype: int64 buy signals\n",
      "Result for strategy SagesGymCNN2\n",
      "========================================================== BACKTESTING REPORT =========================================================\n",
      "|     Pair |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit USDT |   Tot Profit % |   Avg Duration |   Win  Draw  Loss  Win% |\n",
      "|----------+--------+----------------+----------------+-------------------+----------------+----------------+-------------------------|\n",
      "| BTC/USDT |    708 |          -0.27 |        -189.99 |          -850.660 |         -85.07 |        1:02:00 |   216     0   492  30.5 |\n",
      "|    TOTAL |    708 |          -0.27 |        -189.99 |          -850.660 |         -85.07 |        1:02:00 |   216     0   492  30.5 |\n",
      "========================================================== BUY TAG STATS ===========================================================\n",
      "|   TAG |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit USDT |   Tot Profit % |   Avg Duration |   Win  Draw  Loss  Win% |\n",
      "|-------+--------+----------------+----------------+-------------------+----------------+----------------+-------------------------|\n",
      "| TOTAL |    708 |          -0.27 |        -189.99 |          -850.660 |         -85.07 |        1:02:00 |   216     0   492  30.5 |\n",
      "===================================================== SELL REASON STATS =====================================================\n",
      "|   Sell Reason |   Sells |   Win  Draws  Loss  Win% |   Avg Profit % |   Cum Profit % |   Tot Profit USDT |   Tot Profit % |\n",
      "|---------------+---------+--------------------------+----------------+----------------+-------------------+----------------|\n",
      "|   sell_signal |     708 |    216     0   492  30.5 |          -0.27 |        -189.99 |           -850.66 |        -189.99 |\n",
      "====================================================== LEFT OPEN TRADES REPORT ======================================================\n",
      "|   Pair |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit USDT |   Tot Profit % |   Avg Duration |   Win  Draw  Loss  Win% |\n",
      "|--------+--------+----------------+----------------+-------------------+----------------+----------------+-------------------------|\n",
      "|  TOTAL |      0 |           0.00 |           0.00 |             0.000 |           0.00 |           0:00 |     0     0     0     0 |\n",
      "=============== SUMMARY METRICS ================\n",
      "| Metric                 | Value               |\n",
      "|------------------------+---------------------|\n",
      "| Backtesting from       | 2022-01-01 00:00:00 |\n",
      "| Backtesting to         | 2022-04-08 22:00:00 |\n",
      "| Max open trades        | 1                   |\n",
      "|                        |                     |\n",
      "| Total/Daily Avg Trades | 708 / 7.3           |\n",
      "| Starting balance       | 1000 USDT           |\n",
      "| Final balance          | 149.34 USDT         |\n",
      "| Absolute profit        | -850.66 USDT        |\n",
      "| Total profit %         | -85.07%             |\n",
      "| Trades per day         | 7.3                 |\n",
      "| Avg. daily profit %    | -0.88%              |\n",
      "| Avg. stake amount      | 433.98 USDT         |\n",
      "| Total trade volume     | 307258.083 USDT     |\n",
      "|                        |                     |\n",
      "| Best Pair              | BTC/USDT -189.99%   |\n",
      "| Worst Pair             | BTC/USDT -189.99%   |\n",
      "| Best trade             | BTC/USDT 3.90%      |\n",
      "| Worst trade            | BTC/USDT -4.75%     |\n",
      "| Best day               | 16.537 USDT         |\n",
      "| Worst day              | -49.22 USDT         |\n",
      "| Days win/draw/lose     | 12 / 0 / 86         |\n",
      "| Avg. Duration Winners  | 1:01:00             |\n",
      "| Avg. Duration Loser    | 1:02:00             |\n",
      "| Rejected Buy signals   | 0                   |\n",
      "| Entry/Exit Timeouts    | 113 / 0             |\n",
      "|                        |                     |\n",
      "| Min balance            | 149.34 USDT         |\n",
      "| Max balance            | 999.703 USDT        |\n",
      "| Drawdown (Account)     | 85.06%              |\n",
      "| Drawdown               | 850.363 USDT        |\n",
      "| Drawdown high          | -0.297 USDT         |\n",
      "| Drawdown low           | -850.66 USDT        |\n",
      "| Drawdown Start         | 2022-01-01 01:55:00 |\n",
      "| Drawdown End           | 2022-04-08 19:55:00 |\n",
      "| Market change          | -12.71%             |\n",
      "================================================\n",
      "\n",
      "2022-04-09 02:28:57.102 | INFO     | lazyft.backtest.runner:on_finished:254 - Elapsed time: 19.10\n",
      "2022-04-09 02:28:57.116 | INFO     | lazyft.parameter_tools:remove_params_file:36 - Removing strategy params: /home/raphael/PycharmProjects/cnn_gaf/user_data/strategies/my_gym_cnn2.json\n",
      "2022-04-09 02:28:57.117 | SUCCESS  | lazyft.backtest.runner:on_finished:258 - Backtest SagesGymCNN2 finished successfully\n",
      "2022-04-09 02:28:57.117 | INFO     | lazyft.strategy:delete_temporary_strategy_backup_dir:243 - Temporary folder \"None\" does not exist...skipping\n",
      "2022-04-09 02:28:57.181 | INFO     | lazyft.backtest.runner:save:359 - Created report id 15: {'start_date': datetime.datetime(2022, 1, 1, 0, 0), 'end_date': datetime.datetime(2022, 4, 8, 22, 0), 'trades': 708, 'wins': 216, 'losses': 492, 'draws': 0, 'drawdown': 0.8506152999745955, 'avg_duration': '1:02:00', 'profit_mean_pct': -0.0026834395621468925, 'profit_sum_pct': -189.99, 'profit_total_abs': -850.65967585, 'profit_total_pct': -0.8506999999999999, 'duration_avg': datetime.timedelta(seconds=3720)}\n",
      "         pair                 open_date                close_date  open_rate  \\\n",
      "0    BTC/USDT 2022-01-01 00:55:00+00:00 2022-01-01 01:55:00+00:00      46.7K   \n",
      "1    BTC/USDT 2022-01-01 05:55:00+00:00 2022-01-01 06:55:00+00:00      47.2K   \n",
      "2    BTC/USDT 2022-01-01 07:55:00+00:00 2022-01-01 09:00:00+00:00      47.1K   \n",
      "3    BTC/USDT 2022-01-01 09:55:00+00:00 2022-01-01 10:55:00+00:00      47.1K   \n",
      "4    BTC/USDT 2022-01-01 11:55:00+00:00 2022-01-01 12:55:00+00:00      46.9K   \n",
      "..        ...                       ...                       ...        ...   \n",
      "703  BTC/USDT 2022-04-08 08:55:00+00:00 2022-04-08 10:00:00+00:00      43.8K   \n",
      "704  BTC/USDT 2022-04-08 10:55:00+00:00 2022-04-08 12:05:00+00:00      43.6K   \n",
      "705  BTC/USDT 2022-04-08 12:55:00+00:00 2022-04-08 13:55:00+00:00      43.2K   \n",
      "706  BTC/USDT 2022-04-08 15:55:00+00:00 2022-04-08 16:55:00+00:00      43.7K   \n",
      "707  BTC/USDT 2022-04-08 18:55:00+00:00 2022-04-08 19:55:00+00:00      42.9K   \n",
      "\n",
      "     close_rate  profit_abs  \n",
      "0         46.8K      -0.297  \n",
      "1         47.1K      -5.503  \n",
      "2         47.1K      -1.343  \n",
      "3         46.9K      -4.689  \n",
      "4         47.1K       3.222  \n",
      "..          ...         ...  \n",
      "703       43.5K      -1.230  \n",
      "704       43.3K      -1.260  \n",
      "705       42.9K      -1.327  \n",
      "706       43.5K      -0.935  \n",
      "707       42.7K      -1.047  \n",
      "\n",
      "[708 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "runner = params.run(\"SagesGymCNN2\", load_from_hash=False, stdout=True)\n",
    "report = runner.save()\n",
    "print(\n",
    "    report.trades[\n",
    "        [\"pair\", \"open_date\", \"close_date\", \"open_rate\", \"close_rate\", \"profit_abs\"]\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "from lazyft.reports import get_backtest_repo\n",
    "\n",
    "report = get_backtest_repo().first()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-07T21:22:57.344308Z",
     "start_time": "2022-04-07T21:22:57.290426Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|     | open_date                 | close_date                |   close_rate |   open_rate |   profit_ratio |\n",
      "|----:|:--------------------------|:--------------------------|-------------:|------------:|---------------:|\n",
      "|   0 | 2022-01-01 00:55:00+00:00 | 2022-01-01 01:55:00+00:00 |      46769.6 |     46690.2 |    -0.00029976 |\n",
      "|   1 | 2022-01-01 05:55:00+00:00 | 2022-01-01 06:55:00+00:00 |      47072.3 |     47240.7 |    -0.00555518 |\n",
      "|   2 | 2022-01-01 07:55:00+00:00 | 2022-01-01 09:00:00+00:00 |      47124.6 |     47094.7 |    -0.00136311 |\n",
      "|   3 | 2022-01-01 09:55:00+00:00 | 2022-01-01 10:55:00+00:00 |      46938   |     47068.5 |    -0.00476586 |\n",
      "|   4 | 2022-01-01 11:55:00+00:00 | 2022-01-01 12:55:00+00:00 |      47100   |     46851.7 |     0.00329068 |\n",
      "|   5 | 2022-01-01 13:55:00+00:00 | 2022-01-01 14:55:00+00:00 |      46895.4 |     47011.6 |    -0.00446501 |\n",
      "|   6 | 2022-01-01 15:55:00+00:00 | 2022-01-01 17:00:00+00:00 |      47269.2 |     47195.3 |    -0.0004353  |\n",
      "|   7 | 2022-01-01 17:55:00+00:00 | 2022-01-01 18:55:00+00:00 |      47513.4 |     47770   |    -0.00735926 |\n",
      "|   8 | 2022-01-01 21:55:00+00:00 | 2022-01-01 23:10:00+00:00 |      47343   |     47306.1 |    -0.00121974 |\n",
      "|   9 | 2022-01-01 23:55:00+00:00 | 2022-01-02 00:55:00+00:00 |      47669.5 |     47571.4 |     6.067e-05  |\n",
      "|  10 | 2022-01-02 01:55:00+00:00 | 2022-01-02 02:55:00+00:00 |      47375.3 |     47366.4 |    -0.0018109  |\n",
      "|  11 | 2022-01-02 06:55:00+00:00 | 2022-01-02 07:55:00+00:00 |      47147.8 |     47170   |    -0.00246728 |\n",
      "|  12 | 2022-01-02 09:55:00+00:00 | 2022-01-02 11:05:00+00:00 |      47210.5 |     47289.4 |    -0.00366227 |\n",
      "|  13 | 2022-01-02 14:55:00+00:00 | 2022-01-02 15:55:00+00:00 |      47307.8 |     47127.2 |     0.00182674 |\n",
      "|  14 | 2022-01-02 16:55:00+00:00 | 2022-01-02 18:00:00+00:00 |      47392.3 |     47660   |    -0.00760469 |\n",
      "|  15 | 2022-01-02 18:55:00+00:00 | 2022-01-02 19:55:00+00:00 |      46930.3 |     47068   |    -0.00491623 |\n",
      "|  16 | 2022-01-02 20:55:00+00:00 | 2022-01-02 21:55:00+00:00 |      47006.9 |     47011.1 |    -0.0020861  |\n",
      "|  17 | 2022-01-02 22:55:00+00:00 | 2022-01-03 00:05:00+00:00 |      47286.2 |     47286.9 |    -0.00201404 |\n",
      "|  18 | 2022-01-03 00:55:00+00:00 | 2022-01-03 01:55:00+00:00 |      47074.3 |     47118.5 |    -0.00293313 |\n",
      "|  19 | 2022-01-03 06:55:00+00:00 | 2022-01-03 07:55:00+00:00 |      47027.2 |     46984.9 |    -0.0010993  |\n",
      "|  20 | 2022-01-03 08:55:00+00:00 | 2022-01-03 09:55:00+00:00 |      46948.4 |     46952.1 |    -0.00207601 |\n",
      "|  21 | 2022-01-03 15:55:00+00:00 | 2022-01-03 16:55:00+00:00 |      46417.9 |     46720   |    -0.00845041 |\n",
      "|  22 | 2022-01-03 17:55:00+00:00 | 2022-01-03 18:55:00+00:00 |      46461.3 |     46668.2 |    -0.00642193 |\n",
      "|  23 | 2022-01-03 20:55:00+00:00 | 2022-01-03 21:55:00+00:00 |      46114.1 |     45881   |     0.00307238 |\n",
      "|  24 | 2022-01-03 22:55:00+00:00 | 2022-01-04 00:00:00+00:00 |      46433.9 |     46160.8 |     0.00390819 |\n",
      "|  25 | 2022-01-04 01:55:00+00:00 | 2022-01-04 03:00:00+00:00 |      46117.6 |     46072.2 |    -0.00101478 |\n",
      "|  26 | 2022-01-04 05:55:00+00:00 | 2022-01-04 06:55:00+00:00 |      46329.4 |     46059.9 |     0.00384138 |\n",
      "|  27 | 2022-01-04 07:55:00+00:00 | 2022-01-04 08:55:00+00:00 |      46453.3 |     46500   |    -0.00300073 |\n",
      "|  28 | 2022-01-04 09:55:00+00:00 | 2022-01-04 10:55:00+00:00 |      46674   |     46300   |     0.0060634  |\n",
      "|  29 | 2022-01-04 13:55:00+00:00 | 2022-01-04 14:55:00+00:00 |      47400   |     46851   |     0.00969615 |\n",
      "|  30 | 2022-01-04 15:55:00+00:00 | 2022-01-04 16:55:00+00:00 |      46829.3 |     47040   |    -0.00646864 |\n",
      "|  31 | 2022-01-04 17:55:00+00:00 | 2022-01-04 19:00:00+00:00 |      45833   |     46747.5 |    -0.0215215  |\n",
      "|  32 | 2022-01-05 02:55:00+00:00 | 2022-01-05 03:55:00+00:00 |      46461.5 |     46346.2 |     0.00048439 |\n",
      "|  33 | 2022-01-05 04:55:00+00:00 | 2022-01-05 05:55:00+00:00 |      46320.4 |     46419.1 |    -0.00412111 |\n",
      "|  34 | 2022-01-05 10:55:00+00:00 | 2022-01-05 11:55:00+00:00 |      46259.3 |     46723.2 |    -0.0119071  |\n",
      "|  35 | 2022-01-05 18:55:00+00:00 | 2022-01-05 20:00:00+00:00 |      44793.6 |     45995.3 |    -0.0280732  |\n",
      "|  36 | 2022-01-05 20:55:00+00:00 | 2022-01-05 22:00:00+00:00 |      43748.4 |     44130.6 |    -0.01064    |\n",
      "|  37 | 2022-01-05 22:55:00+00:00 | 2022-01-06 00:05:00+00:00 |      43451.1 |     43469.8 |    -0.00242687 |\n",
      "|  38 | 2022-01-06 02:55:00+00:00 | 2022-01-06 03:55:00+00:00 |      42950   |     43323.8 |    -0.010609   |\n",
      "|  39 | 2022-01-06 04:55:00+00:00 | 2022-01-06 05:55:00+00:00 |      43050   |     42850   |     0.00266012 |\n",
      "|  40 | 2022-01-06 06:55:00+00:00 | 2022-01-06 07:55:00+00:00 |      43198.5 |     43020.3 |     0.00213432 |\n",
      "|  41 | 2022-01-06 08:55:00+00:00 | 2022-01-06 10:00:00+00:00 |      42595.9 |     43059.3 |    -0.0127368  |\n",
      "|  42 | 2022-01-06 10:55:00+00:00 | 2022-01-06 11:55:00+00:00 |      42853.6 |     42855.4 |    -0.00203945 |\n",
      "|  43 | 2022-01-06 12:55:00+00:00 | 2022-01-06 13:55:00+00:00 |      43123.6 |     42900   |     0.00320277 |\n",
      "|  44 | 2022-01-06 14:55:00+00:00 | 2022-01-06 15:55:00+00:00 |      43015   |     42879.2 |     0.00116247 |\n",
      "|  45 | 2022-01-06 16:55:00+00:00 | 2022-01-06 18:00:00+00:00 |      43035.2 |     42890.9 |     0.00136009 |\n",
      "|  46 | 2022-01-06 18:55:00+00:00 | 2022-01-06 19:55:00+00:00 |      43450   |     43292.5 |     0.00163208 |\n",
      "|  47 | 2022-01-06 20:55:00+00:00 | 2022-01-06 21:55:00+00:00 |      43142.4 |     43219   |    -0.00376637 |\n",
      "|  48 | 2022-01-06 22:55:00+00:00 | 2022-01-06 23:55:00+00:00 |      43129.3 |     43125   |    -0.00189826 |\n",
      "|  49 | 2022-01-07 04:55:00+00:00 | 2022-01-07 05:55:00+00:00 |      41698.3 |     41650   |    -0.00084113 |\n",
      "|  50 | 2022-01-07 06:55:00+00:00 | 2022-01-07 07:55:00+00:00 |      41649   |     41460.7 |     0.00253337 |\n",
      "|  51 | 2022-01-07 09:55:00+00:00 | 2022-01-07 11:00:00+00:00 |      42239.4 |     42470.2 |    -0.00742178 |\n",
      "|  52 | 2022-01-07 12:55:00+00:00 | 2022-01-07 14:00:00+00:00 |      41430.3 |     42425   |    -0.0253982  |\n",
      "|  53 | 2022-01-07 14:55:00+00:00 | 2022-01-07 15:55:00+00:00 |      41161.6 |     42011.5 |    -0.022187   |\n",
      "|  54 | 2022-01-07 18:55:00+00:00 | 2022-01-07 19:55:00+00:00 |      41827   |     41566.1 |     0.004265   |\n",
      "|  55 | 2022-01-07 20:55:00+00:00 | 2022-01-07 21:55:00+00:00 |      41920   |     41911.6 |    -0.00179893 |\n",
      "|  56 | 2022-01-07 22:55:00+00:00 | 2022-01-07 23:55:00+00:00 |      41487.8 |     41548.8 |    -0.0034625  |\n",
      "|  57 | 2022-01-08 00:55:00+00:00 | 2022-01-08 02:00:00+00:00 |      41890.4 |     41943.4 |    -0.0032598  |\n",
      "|  58 | 2022-01-08 03:55:00+00:00 | 2022-01-08 04:55:00+00:00 |      41946.1 |     41773.9 |     0.00211595 |\n",
      "|  59 | 2022-01-08 05:55:00+00:00 | 2022-01-08 06:55:00+00:00 |      41880.6 |     41949   |    -0.00362601 |\n",
      "|  60 | 2022-01-08 07:55:00+00:00 | 2022-01-08 09:00:00+00:00 |      42002.5 |     41892.3 |     0.00062753 |\n",
      "|  61 | 2022-01-08 10:55:00+00:00 | 2022-01-08 11:55:00+00:00 |      41931.4 |     41890.1 |    -0.00101287 |\n",
      "|  62 | 2022-01-08 14:55:00+00:00 | 2022-01-08 16:05:00+00:00 |      41609.6 |     41866   |    -0.0081103  |\n",
      "|  63 | 2022-01-08 16:55:00+00:00 | 2022-01-08 18:00:00+00:00 |      40984.1 |     41454.4 |    -0.0133203  |\n",
      "|  64 | 2022-01-08 18:55:00+00:00 | 2022-01-08 19:55:00+00:00 |      40722   |     40888.6 |    -0.00606435 |\n",
      "|  65 | 2022-01-08 22:55:00+00:00 | 2022-01-09 00:10:00+00:00 |      41581.4 |     41978.1 |    -0.011429   |\n",
      "|  66 | 2022-01-09 00:55:00+00:00 | 2022-01-09 01:55:00+00:00 |      41696.1 |     41698   |    -0.00204467 |\n",
      "|  67 | 2022-01-09 02:55:00+00:00 | 2022-01-09 04:00:00+00:00 |      41821.8 |     42139.7 |    -0.00952711 |\n",
      "|  68 | 2022-01-09 07:55:00+00:00 | 2022-01-09 08:55:00+00:00 |      41700.8 |     41809.6 |    -0.00459555 |\n",
      "|  69 | 2022-01-09 09:55:00+00:00 | 2022-01-09 11:00:00+00:00 |      41647   |     41568.5 |    -0.00011164 |\n",
      "|  70 | 2022-01-09 11:55:00+00:00 | 2022-01-09 13:05:00+00:00 |      41747   |     41801.9 |    -0.00330848 |\n",
      "|  71 | 2022-01-10 14:55:00+00:00 | 2022-01-10 15:55:00+00:00 |      40824.8 |     40815.7 |    -0.00177549 |\n",
      "|  72 | 2022-01-10 19:55:00+00:00 | 2022-01-10 20:55:00+00:00 |      41549.3 |     41385.9 |     0.00194255 |\n",
      "|  73 | 2022-01-10 21:55:00+00:00 | 2022-01-10 23:00:00+00:00 |      41576.8 |     41624.3 |    -0.00313856 |\n",
      "|  74 | 2022-01-10 23:55:00+00:00 | 2022-01-11 01:05:00+00:00 |      41636.8 |     41800   |    -0.00589331 |\n",
      "|  75 | 2022-01-11 03:55:00+00:00 | 2022-01-11 04:55:00+00:00 |      42284.7 |     42236.6 |    -0.00086122 |\n",
      "|  76 | 2022-01-11 05:55:00+00:00 | 2022-01-11 06:55:00+00:00 |      42065   |     42109   |    -0.00304011 |\n",
      "|  77 | 2022-01-11 10:55:00+00:00 | 2022-01-11 11:55:00+00:00 |      41857.5 |     41675.8 |     0.00235193 |\n",
      "|  78 | 2022-01-11 12:55:00+00:00 | 2022-01-11 13:55:00+00:00 |      41688   |     41891.6 |    -0.00684822 |\n",
      "|  79 | 2022-01-11 18:55:00+00:00 | 2022-01-11 19:55:00+00:00 |      42954.9 |     42786.4 |     0.00193136 |\n",
      "|  80 | 2022-01-11 20:55:00+00:00 | 2022-01-11 21:55:00+00:00 |      42681.2 |     42812.7 |    -0.00506176 |\n",
      "|  81 | 2022-01-11 22:55:00+00:00 | 2022-01-11 23:55:00+00:00 |      42736   |     42745   |    -0.00220813 |\n",
      "|  82 | 2022-01-12 00:55:00+00:00 | 2022-01-12 01:55:00+00:00 |      42600.5 |     42667.4 |    -0.00356445 |\n",
      "|  83 | 2022-01-12 06:55:00+00:00 | 2022-01-12 07:55:00+00:00 |      42562   |     42677.7 |    -0.00470337 |\n",
      "|  84 | 2022-01-12 08:55:00+00:00 | 2022-01-12 09:55:00+00:00 |      42729.8 |     42635   |     0.00022108 |\n",
      "|  85 | 2022-01-12 10:55:00+00:00 | 2022-01-12 12:05:00+00:00 |      42935.9 |     42842.3 |     0.00018379 |\n",
      "|  86 | 2022-01-12 14:55:00+00:00 | 2022-01-12 15:55:00+00:00 |      43554.6 |     43748   |    -0.00640858 |\n",
      "|  87 | 2022-01-12 16:55:00+00:00 | 2022-01-12 17:55:00+00:00 |      43783.4 |     43577.8 |     0.00270965 |\n",
      "|  88 | 2022-01-13 04:55:00+00:00 | 2022-01-13 05:55:00+00:00 |      43674.5 |     43634.9 |    -0.00109297 |\n",
      "|  89 | 2022-01-13 06:55:00+00:00 | 2022-01-13 07:55:00+00:00 |      43668.4 |     43682.9 |    -0.00232791 |\n",
      "|  90 | 2022-01-13 11:55:00+00:00 | 2022-01-13 12:55:00+00:00 |      43749.3 |     43646.4 |     0.00035396 |\n",
      "|  91 | 2022-01-13 14:55:00+00:00 | 2022-01-13 15:55:00+00:00 |      43058   |     44020.9 |    -0.0238282  |\n",
      "|  92 | 2022-01-13 20:55:00+00:00 | 2022-01-13 22:00:00+00:00 |      42819.6 |     42618.2 |     0.00271707 |\n",
      "|  93 | 2022-01-13 22:55:00+00:00 | 2022-01-14 00:00:00+00:00 |      42525.2 |     42568.1 |    -0.00300425 |\n",
      "|  94 | 2022-01-14 06:55:00+00:00 | 2022-01-14 08:00:00+00:00 |      42645   |     42824.7 |    -0.00618626 |\n",
      "|  95 | 2022-01-14 13:55:00+00:00 | 2022-01-14 14:55:00+00:00 |      42928   |     42180.3 |     0.0156929  |\n",
      "|  96 | 2022-01-14 15:55:00+00:00 | 2022-01-14 16:55:00+00:00 |      43194   |     43084.4 |     0.00053937 |\n",
      "|  97 | 2022-01-14 19:55:00+00:00 | 2022-01-14 20:55:00+00:00 |      43082   |     43131.1 |    -0.00313273 |\n",
      "|  98 | 2022-01-14 21:55:00+00:00 | 2022-01-14 22:55:00+00:00 |      43316.9 |     43262.1 |    -0.00073476 |\n",
      "|  99 | 2022-01-14 23:55:00+00:00 | 2022-01-15 00:55:00+00:00 |      43077   |     43044.9 |    -0.00125376 |\n",
      "| 100 | 2022-01-15 02:55:00+00:00 | 2022-01-15 04:05:00+00:00 |      42927.9 |     43012.5 |    -0.00396071 |\n",
      "| 101 | 2022-01-15 04:55:00+00:00 | 2022-01-15 05:55:00+00:00 |      42986   |     42887.7 |     0.00028829 |\n",
      "| 102 | 2022-01-15 06:55:00+00:00 | 2022-01-15 07:55:00+00:00 |      42989.6 |     43030.1 |    -0.00293732 |\n",
      "| 103 | 2022-01-15 08:55:00+00:00 | 2022-01-15 09:55:00+00:00 |      42964.3 |     43015.4 |    -0.00318427 |\n",
      "| 104 | 2022-01-15 12:55:00+00:00 | 2022-01-15 13:55:00+00:00 |      42935.7 |     42650.5 |     0.00467648 |\n",
      "| 105 | 2022-01-15 14:55:00+00:00 | 2022-01-15 15:55:00+00:00 |      43334.2 |     43138   |     0.00254133 |\n",
      "| 106 | 2022-01-15 16:55:00+00:00 | 2022-01-15 17:55:00+00:00 |      43421.3 |     43315   |     0.00045167 |\n",
      "| 107 | 2022-01-15 19:55:00+00:00 | 2022-01-15 21:05:00+00:00 |      43485   |     43459.4 |    -0.00140943 |\n",
      "| 108 | 2022-01-16 00:55:00+00:00 | 2022-01-16 02:05:00+00:00 |      43025.9 |     43025   |    -0.00197782 |\n",
      "| 109 | 2022-01-16 07:55:00+00:00 | 2022-01-16 09:00:00+00:00 |      43092.9 |     43229.1 |    -0.0051419  |\n",
      "| 110 | 2022-01-16 09:55:00+00:00 | 2022-01-16 11:05:00+00:00 |      42900   |     43093.5 |    -0.00648019 |\n",
      "| 111 | 2022-01-16 15:55:00+00:00 | 2022-01-16 17:00:00+00:00 |      43340.1 |     43213.5 |     0.00092509 |\n",
      "| 112 | 2022-01-16 17:55:00+00:00 | 2022-01-16 18:55:00+00:00 |      43063.3 |     43079   |    -0.00236126 |\n",
      "| 113 | 2022-01-16 19:55:00+00:00 | 2022-01-16 21:00:00+00:00 |      43046.3 |     42870.1 |     0.00210504 |\n",
      "| 114 | 2022-01-17 03:55:00+00:00 | 2022-01-17 05:15:00+00:00 |      42405.9 |     42695.6 |    -0.00876922 |\n",
      "| 115 | 2022-01-17 05:55:00+00:00 | 2022-01-17 07:00:00+00:00 |      42769.8 |     42519.6 |     0.00387576 |\n",
      "| 116 | 2022-01-17 07:55:00+00:00 | 2022-01-17 08:55:00+00:00 |      42809.1 |     42708.8 |     0.00034647 |\n",
      "| 117 | 2022-01-17 09:55:00+00:00 | 2022-01-17 10:55:00+00:00 |      42781.4 |     42854.2 |    -0.00369223 |\n",
      "| 118 | 2022-01-17 11:55:00+00:00 | 2022-01-17 13:00:00+00:00 |      42617.1 |     42730   |    -0.00463513 |\n",
      "| 119 | 2022-01-17 14:55:00+00:00 | 2022-01-17 16:00:00+00:00 |      42482   |     42615.9 |    -0.00513421 |\n",
      "| 120 | 2022-01-17 16:55:00+00:00 | 2022-01-17 18:00:00+00:00 |      42081.1 |     42209.5 |    -0.00503342 |\n",
      "| 121 | 2022-01-17 18:55:00+00:00 | 2022-01-17 19:55:00+00:00 |      42245.1 |     42130.6 |     0.00071454 |\n",
      "| 122 | 2022-01-17 20:55:00+00:00 | 2022-01-17 21:55:00+00:00 |      41682.2 |     42067   |    -0.0111282  |\n",
      "| 123 | 2022-01-17 22:55:00+00:00 | 2022-01-17 23:55:00+00:00 |      42237.2 |     42155.2 |    -5.622e-05  |\n",
      "| 124 | 2022-01-18 00:55:00+00:00 | 2022-01-18 01:55:00+00:00 |      42220   |     42343.4 |    -0.00490762 |\n",
      "| 125 | 2022-01-18 05:55:00+00:00 | 2022-01-18 07:00:00+00:00 |      42270   |     42137.6 |     0.00113876 |\n",
      "| 126 | 2022-01-18 07:55:00+00:00 | 2022-01-18 09:00:00+00:00 |      41706.2 |     42051.4 |    -0.0101901  |\n",
      "| 127 | 2022-01-18 09:55:00+00:00 | 2022-01-18 10:55:00+00:00 |      41960.8 |     41914.3 |    -0.00089224 |\n",
      "| 128 | 2022-01-18 22:55:00+00:00 | 2022-01-19 00:00:00+00:00 |      42379.8 |     42486   |    -0.00449336 |\n",
      "| 129 | 2022-01-19 01:55:00+00:00 | 2022-01-19 02:55:00+00:00 |      42306.7 |     42384.3 |    -0.00382474 |\n",
      "| 130 | 2022-01-19 05:55:00+00:00 | 2022-01-19 07:25:00+00:00 |      41320.2 |     41675   |    -0.0104935  |\n",
      "| 131 | 2022-01-19 10:55:00+00:00 | 2022-01-19 11:55:00+00:00 |      41951.8 |     41431.7 |     0.0105301  |\n",
      "| 132 | 2022-01-19 19:55:00+00:00 | 2022-01-19 21:00:00+00:00 |      41627.2 |     41782.2 |    -0.00569887 |\n",
      "| 133 | 2022-01-19 21:55:00+00:00 | 2022-01-19 22:55:00+00:00 |      41949.6 |     41732.3 |     0.00319859 |\n",
      "| 134 | 2022-01-19 23:55:00+00:00 | 2022-01-20 00:55:00+00:00 |      41794.6 |     41689.5 |     0.00051822 |\n",
      "| 135 | 2022-01-20 01:55:00+00:00 | 2022-01-20 02:55:00+00:00 |      41992   |     41737.2 |     0.00409466 |\n",
      "| 136 | 2022-01-20 04:55:00+00:00 | 2022-01-20 05:55:00+00:00 |      41854.7 |     41880.9 |    -0.00262353 |\n",
      "| 137 | 2022-01-20 06:55:00+00:00 | 2022-01-20 07:55:00+00:00 |      42065.4 |     42001.5 |    -0.00047919 |\n",
      "| 138 | 2022-01-20 08:55:00+00:00 | 2022-01-20 09:55:00+00:00 |      42037   |     41886.7 |     0.0015826  |\n",
      "| 139 | 2022-01-20 13:55:00+00:00 | 2022-01-20 14:55:00+00:00 |      42900   |     42417.8 |     0.00934644 |\n",
      "| 140 | 2022-01-20 16:55:00+00:00 | 2022-01-20 18:00:00+00:00 |      43024.6 |     43202.2 |    -0.00609931 |\n",
      "| 141 | 2022-01-20 18:55:00+00:00 | 2022-01-20 19:55:00+00:00 |      42926.8 |     43036.6 |    -0.00454376 |\n",
      "| 142 | 2022-01-20 21:55:00+00:00 | 2022-01-20 22:55:00+00:00 |      41140.4 |     41577   |    -0.0124778  |\n",
      "| 143 | 2022-01-20 23:55:00+00:00 | 2022-01-21 01:00:00+00:00 |      40856.3 |     40707.9 |     0.00164142 |\n",
      "| 144 | 2022-01-21 01:55:00+00:00 | 2022-01-21 02:55:00+00:00 |      39821.8 |     39855.3 |    -0.00283511 |\n",
      "| 145 | 2022-01-21 03:55:00+00:00 | 2022-01-21 04:55:00+00:00 |      38837.8 |     38535.5 |     0.00582974 |\n",
      "| 146 | 2022-01-21 06:55:00+00:00 | 2022-01-21 07:55:00+00:00 |      39078.6 |     38664.2 |     0.00869954 |\n",
      "| 147 | 2022-01-21 08:55:00+00:00 | 2022-01-21 09:55:00+00:00 |      39010.1 |     39083.1 |    -0.0038626  |\n",
      "| 148 | 2022-01-21 13:55:00+00:00 | 2022-01-21 15:00:00+00:00 |      38420.3 |     38524.5 |    -0.00469659 |\n",
      "| 149 | 2022-01-21 15:55:00+00:00 | 2022-01-21 16:55:00+00:00 |      38583.7 |     38939.2 |    -0.0111101  |\n",
      "| 150 | 2022-01-21 17:55:00+00:00 | 2022-01-21 18:55:00+00:00 |      38343   |     38310.1 |    -0.00113911 |\n",
      "| 151 | 2022-01-21 21:55:00+00:00 | 2022-01-21 22:55:00+00:00 |      35819.5 |     36607   |    -0.0234673  |\n",
      "| 152 | 2022-01-22 07:55:00+00:00 | 2022-01-22 09:00:00+00:00 |      35605.7 |     35554.4 |    -0.00055634 |\n",
      "| 153 | 2022-01-22 09:55:00+00:00 | 2022-01-22 10:55:00+00:00 |      35441.4 |     34811.7 |     0.0160549  |\n",
      "| 154 | 2022-01-22 17:55:00+00:00 | 2022-01-22 19:00:00+00:00 |      34502.7 |     34656.5 |    -0.00642496 |\n",
      "| 155 | 2022-01-23 00:55:00+00:00 | 2022-01-23 01:55:00+00:00 |      35381.3 |     35300.7 |     0.00028068 |\n",
      "| 156 | 2022-01-23 02:55:00+00:00 | 2022-01-23 03:55:00+00:00 |      35045.5 |     34933   |     0.00121487 |\n",
      "| 157 | 2022-01-23 04:55:00+00:00 | 2022-01-23 05:55:00+00:00 |      35386.7 |     35235.9 |     0.00227288 |\n",
      "| 158 | 2022-01-23 06:55:00+00:00 | 2022-01-23 07:55:00+00:00 |      35590.8 |     35240.2 |     0.00793298 |\n",
      "| 159 | 2022-01-23 08:55:00+00:00 | 2022-01-23 10:00:00+00:00 |      35891.6 |     35626.5 |     0.00543047 |\n",
      "| 160 | 2022-01-23 10:55:00+00:00 | 2022-01-23 12:00:00+00:00 |      35703.1 |     35824.7 |    -0.00538609 |\n",
      "| 161 | 2022-01-23 18:55:00+00:00 | 2022-01-23 20:00:00+00:00 |      34707.6 |     34957.6 |    -0.00913552 |\n",
      "| 162 | 2022-01-23 20:55:00+00:00 | 2022-01-23 21:55:00+00:00 |      35400.1 |     35244   |     0.00242114 |\n",
      "| 163 | 2022-01-23 22:55:00+00:00 | 2022-01-23 23:55:00+00:00 |      36236.1 |     35433.8 |     0.0205999  |\n",
      "| 164 | 2022-01-24 07:55:00+00:00 | 2022-01-24 09:00:00+00:00 |      35001.4 |     34976.5 |    -0.00128894 |\n",
      "| 165 | 2022-01-24 09:55:00+00:00 | 2022-01-24 10:55:00+00:00 |      33547.2 |     34555.1 |    -0.031107   |\n",
      "| 166 | 2022-01-24 11:55:00+00:00 | 2022-01-24 12:55:00+00:00 |      33148.3 |     33787.4 |    -0.0208747  |\n",
      "| 167 | 2022-01-24 13:55:00+00:00 | 2022-01-24 14:55:00+00:00 |      33699.3 |     33428.2 |     0.00609541 |\n",
      "| 168 | 2022-01-24 15:55:00+00:00 | 2022-01-24 16:55:00+00:00 |      34172   |     34066.4 |     0.00109563 |\n",
      "| 169 | 2022-01-24 18:55:00+00:00 | 2022-01-24 19:55:00+00:00 |      35958.1 |     36312.6 |    -0.0117407  |\n",
      "| 170 | 2022-01-24 21:55:00+00:00 | 2022-01-24 22:55:00+00:00 |      36376.1 |     36734.7 |    -0.0117388  |\n",
      "| 171 | 2022-01-24 23:55:00+00:00 | 2022-01-25 01:00:00+00:00 |      36098.4 |     36680   |    -0.0178216  |\n",
      "| 172 | 2022-01-25 01:55:00+00:00 | 2022-01-25 02:55:00+00:00 |      36281.3 |     36123.8 |     0.00235357 |\n",
      "| 173 | 2022-01-25 08:55:00+00:00 | 2022-01-25 09:55:00+00:00 |      36392.4 |     36224.8 |     0.00261804 |\n",
      "| 174 | 2022-01-25 13:55:00+00:00 | 2022-01-25 15:00:00+00:00 |      36338.4 |     36429   |    -0.00448006 |\n",
      "| 175 | 2022-01-25 17:55:00+00:00 | 2022-01-25 18:55:00+00:00 |      37330   |     37172.1 |     0.00224213 |\n",
      "| 176 | 2022-01-25 19:55:00+00:00 | 2022-01-25 20:55:00+00:00 |      36860.9 |     37126.5 |    -0.00913762 |\n",
      "| 177 | 2022-01-26 00:55:00+00:00 | 2022-01-26 01:55:00+00:00 |      36840.2 |     36657.9 |     0.00296425 |\n",
      "| 178 | 2022-01-26 02:55:00+00:00 | 2022-01-26 04:10:00+00:00 |      37327.2 |     36809.8 |     0.0120308  |\n",
      "| 179 | 2022-01-26 05:55:00+00:00 | 2022-01-26 06:55:00+00:00 |      37323.2 |     37689.8 |    -0.0117038  |\n",
      "| 180 | 2022-01-26 14:55:00+00:00 | 2022-01-26 15:55:00+00:00 |      37880   |     38201.8 |    -0.0104046  |\n",
      "| 181 | 2022-01-26 17:55:00+00:00 | 2022-01-26 18:55:00+00:00 |      37789.8 |     38072.5 |    -0.00940926 |\n",
      "| 182 | 2022-01-26 19:55:00+00:00 | 2022-01-26 20:55:00+00:00 |      36972   |     37553   |    -0.0174386  |\n",
      "| 183 | 2022-01-26 21:55:00+00:00 | 2022-01-26 22:55:00+00:00 |      36514.4 |     36411.4 |     0.00082431 |\n",
      "| 184 | 2022-01-26 23:55:00+00:00 | 2022-01-27 00:55:00+00:00 |      37025.8 |     36815.3 |     0.00370885 |\n",
      "| 185 | 2022-01-27 05:55:00+00:00 | 2022-01-27 06:55:00+00:00 |      36121.2 |     36002.9 |     0.00128072 |\n",
      "| 186 | 2022-01-27 07:55:00+00:00 | 2022-01-27 08:55:00+00:00 |      36344.8 |     36167.8 |     0.00288525 |\n",
      "| 187 | 2022-01-27 09:55:00+00:00 | 2022-01-27 11:05:00+00:00 |      36348.7 |     36578   |    -0.008254   |\n",
      "| 188 | 2022-01-27 11:55:00+00:00 | 2022-01-27 12:55:00+00:00 |      36650   |     36631   |    -0.00148008 |\n",
      "| 189 | 2022-01-27 14:55:00+00:00 | 2022-01-27 15:55:00+00:00 |      36578.7 |     36998   |    -0.0133084  |\n",
      "| 190 | 2022-01-27 16:55:00+00:00 | 2022-01-27 17:55:00+00:00 |      36280   |     36507.4 |    -0.0082128  |\n",
      "| 191 | 2022-01-27 18:55:00+00:00 | 2022-01-27 19:55:00+00:00 |      36277.2 |     36170.4 |     0.00095072 |\n",
      "| 192 | 2022-01-27 20:55:00+00:00 | 2022-01-27 21:55:00+00:00 |      36188.5 |     35825   |     0.00812856 |\n",
      "| 193 | 2022-01-27 22:55:00+00:00 | 2022-01-27 23:55:00+00:00 |      37063   |     36892.7 |     0.00260968 |\n",
      "| 194 | 2022-01-28 00:55:00+00:00 | 2022-01-28 01:55:00+00:00 |      36878   |     37136.2 |    -0.00893743 |\n",
      "| 195 | 2022-01-28 03:55:00+00:00 | 2022-01-28 04:55:00+00:00 |      37135.4 |     37312.8 |    -0.00674344 |\n",
      "| 196 | 2022-01-28 05:55:00+00:00 | 2022-01-28 07:05:00+00:00 |      36913.4 |     37219   |    -0.0101919  |\n",
      "| 197 | 2022-01-28 07:55:00+00:00 | 2022-01-28 08:55:00+00:00 |      36590.1 |     36968.8 |    -0.0122213  |\n",
      "| 198 | 2022-01-28 11:55:00+00:00 | 2022-01-28 12:55:00+00:00 |      36183.2 |     36451.8 |    -0.00935301 |\n",
      "| 199 | 2022-01-28 13:55:00+00:00 | 2022-01-28 14:55:00+00:00 |      36717.2 |     36799.6 |    -0.00423241 |\n",
      "| 200 | 2022-01-28 19:55:00+00:00 | 2022-01-28 20:55:00+00:00 |      37737.8 |     37117   |     0.0146938  |\n",
      "| 201 | 2022-01-28 21:55:00+00:00 | 2022-01-28 23:00:00+00:00 |      37741.8 |     37876.9 |    -0.0055577  |\n",
      "| 202 | 2022-01-29 10:55:00+00:00 | 2022-01-29 11:55:00+00:00 |      37722   |     37772.9 |    -0.00334363 |\n",
      "| 203 | 2022-01-29 12:55:00+00:00 | 2022-01-29 13:55:00+00:00 |      38100   |     37790.1 |     0.00618537 |\n",
      "| 204 | 2022-01-29 15:55:00+00:00 | 2022-01-29 16:55:00+00:00 |      37500   |     37501.2 |    -0.0020302  |\n",
      "| 205 | 2022-01-29 17:55:00+00:00 | 2022-01-29 18:55:00+00:00 |      37833.2 |     37657   |     0.00267173 |\n",
      "| 206 | 2022-01-29 19:55:00+00:00 | 2022-01-29 20:55:00+00:00 |      38233.7 |     37878   |     0.00737497 |\n",
      "| 207 | 2022-01-30 00:55:00+00:00 | 2022-01-30 02:00:00+00:00 |      37916   |     37868.7 |    -0.00075197 |\n",
      "| 208 | 2022-01-30 02:55:00+00:00 | 2022-01-30 03:55:00+00:00 |      37888.2 |     37958.3 |    -0.00384239 |\n",
      "| 209 | 2022-01-30 04:55:00+00:00 | 2022-01-30 06:10:00+00:00 |      38110.6 |     38043.2 |    -0.00022882 |\n",
      "| 210 | 2022-01-30 12:55:00+00:00 | 2022-01-30 13:55:00+00:00 |      37969.7 |     37918.6 |    -0.00065413 |\n",
      "| 211 | 2022-01-30 14:55:00+00:00 | 2022-01-30 16:00:00+00:00 |      37953.9 |     37796.9 |     0.00214827 |\n",
      "| 212 | 2022-01-30 16:55:00+00:00 | 2022-01-30 17:55:00+00:00 |      37972.2 |     37866.4 |     0.00078913 |\n",
      "| 213 | 2022-01-30 21:55:00+00:00 | 2022-01-30 22:55:00+00:00 |      37770   |     37730   |    -0.00094022 |\n",
      "| 214 | 2022-01-30 23:55:00+00:00 | 2022-01-31 00:55:00+00:00 |      36931.7 |     37896.4 |    -0.0274028  |\n",
      "| 215 | 2022-01-31 01:55:00+00:00 | 2022-01-31 02:55:00+00:00 |      36952   |     36846.6 |     0.00085462 |\n",
      "| 216 | 2022-01-31 04:55:00+00:00 | 2022-01-31 05:55:00+00:00 |      37018.7 |     36929   |     0.00042505 |\n",
      "| 217 | 2022-01-31 06:55:00+00:00 | 2022-01-31 08:00:00+00:00 |      37125   |     36995.1 |     0.00150761 |\n",
      "| 218 | 2022-01-31 08:55:00+00:00 | 2022-01-31 09:55:00+00:00 |      37225.1 |     37144   |     0.0001813  |\n",
      "| 219 | 2022-01-31 10:55:00+00:00 | 2022-01-31 11:55:00+00:00 |      36988.3 |     37260.6 |    -0.00929086 |\n",
      "| 220 | 2022-01-31 12:55:00+00:00 | 2022-01-31 13:55:00+00:00 |      37270.1 |     37039.1 |     0.00422754 |\n",
      "| 221 | 2022-01-31 15:55:00+00:00 | 2022-01-31 16:55:00+00:00 |      38232   |     37803.1 |     0.00932496 |\n",
      "| 222 | 2022-01-31 20:55:00+00:00 | 2022-01-31 21:55:00+00:00 |      38401.9 |     38525.7 |    -0.00520477 |\n",
      "| 223 | 2022-01-31 22:55:00+00:00 | 2022-01-31 23:55:00+00:00 |      38460.8 |     38447.9 |    -0.00166263 |\n",
      "| 224 | 2022-02-01 00:55:00+00:00 | 2022-02-01 01:55:00+00:00 |      38210.7 |     38325   |    -0.00497313 |\n",
      "| 225 | 2022-02-01 04:55:00+00:00 | 2022-02-01 06:20:00+00:00 |      38287.6 |     38525.1 |    -0.0081505  |\n",
      "| 226 | 2022-02-01 09:55:00+00:00 | 2022-02-01 10:55:00+00:00 |      38233.9 |     38300   |    -0.00372092 |\n",
      "| 227 | 2022-02-01 18:55:00+00:00 | 2022-02-01 19:55:00+00:00 |      38401.9 |     38738.1 |    -0.0106569  |\n",
      "| 228 | 2022-02-01 20:55:00+00:00 | 2022-02-01 21:55:00+00:00 |      38697.4 |     38705.3 |    -0.00220093 |\n",
      "| 229 | 2022-02-01 22:55:00+00:00 | 2022-02-02 00:00:00+00:00 |      38751.1 |     38661   |     0.00032811 |\n",
      "| 230 | 2022-02-02 00:55:00+00:00 | 2022-02-02 01:55:00+00:00 |      38426.8 |     38600.2 |    -0.00648071 |\n",
      "| 231 | 2022-02-02 02:55:00+00:00 | 2022-02-02 03:55:00+00:00 |      38564.8 |     38625.5 |    -0.00356507 |\n",
      "| 232 | 2022-02-02 04:55:00+00:00 | 2022-02-02 05:55:00+00:00 |      38374.1 |     38455.8 |    -0.00411931 |\n",
      "| 233 | 2022-02-02 06:55:00+00:00 | 2022-02-02 07:55:00+00:00 |      38227.3 |     38252.2 |    -0.00264817 |\n",
      "| 234 | 2022-02-02 09:55:00+00:00 | 2022-02-02 10:55:00+00:00 |      38572.2 |     38538.7 |    -0.00113048 |\n",
      "| 235 | 2022-02-02 11:55:00+00:00 | 2022-02-02 12:55:00+00:00 |      38695.4 |     38509.6 |     0.00281583 |\n",
      "| 236 | 2022-02-02 13:55:00+00:00 | 2022-02-02 14:55:00+00:00 |      37626.7 |     38406.9 |    -0.0222723  |\n",
      "| 237 | 2022-02-02 15:55:00+00:00 | 2022-02-02 16:55:00+00:00 |      37299.3 |     37449.7 |    -0.00600603 |\n",
      "| 238 | 2022-02-02 17:55:00+00:00 | 2022-02-02 18:55:00+00:00 |      37431.4 |     37429.1 |    -0.00193694 |\n",
      "| 239 | 2022-02-02 19:55:00+00:00 | 2022-02-02 20:55:00+00:00 |      37545.4 |     37604.9 |    -0.00357575 |\n",
      "| 240 | 2022-02-03 01:55:00+00:00 | 2022-02-03 02:55:00+00:00 |      36940.5 |     36883   |    -0.00044051 |\n",
      "| 241 | 2022-02-03 03:55:00+00:00 | 2022-02-03 05:15:00+00:00 |      36872   |     36843.9 |    -0.00123522 |\n",
      "| 242 | 2022-02-03 06:55:00+00:00 | 2022-02-03 08:00:00+00:00 |      37041.9 |     36927.9 |     0.00108428 |\n",
      "| 243 | 2022-02-03 08:55:00+00:00 | 2022-02-03 09:55:00+00:00 |      36926.9 |     36986.5 |    -0.00360672 |\n",
      "| 244 | 2022-02-03 10:55:00+00:00 | 2022-02-03 11:55:00+00:00 |      36720   |     36441.8 |     0.00562111 |\n",
      "| 245 | 2022-02-03 13:55:00+00:00 | 2022-02-03 14:55:00+00:00 |      36525.4 |     36503.8 |    -0.00140555 |\n",
      "| 246 | 2022-02-03 16:55:00+00:00 | 2022-02-03 17:55:00+00:00 |      36745.9 |     36782.9 |    -0.00300216 |\n",
      "| 247 | 2022-02-03 18:55:00+00:00 | 2022-02-03 19:55:00+00:00 |      36517.2 |     36784.6 |    -0.00925229 |\n",
      "| 248 | 2022-02-04 00:55:00+00:00 | 2022-02-04 01:55:00+00:00 |      37154.4 |     37143.6 |    -0.00170782 |\n",
      "| 249 | 2022-02-04 02:55:00+00:00 | 2022-02-04 03:55:00+00:00 |      37318.1 |     37380.1 |    -0.00365412 |\n",
      "| 250 | 2022-02-04 04:55:00+00:00 | 2022-02-04 05:55:00+00:00 |      37550   |     37325.2 |     0.00401163 |\n",
      "| 251 | 2022-02-04 06:55:00+00:00 | 2022-02-04 07:55:00+00:00 |      37861.2 |     37951.1 |    -0.00436158 |\n",
      "| 252 | 2022-02-04 08:55:00+00:00 | 2022-02-04 09:55:00+00:00 |      38075   |     37916.9 |     0.00216437 |\n",
      "| 253 | 2022-02-04 11:55:00+00:00 | 2022-02-04 12:55:00+00:00 |      37963.7 |     37845   |     0.00113221 |\n",
      "| 254 | 2022-02-04 13:55:00+00:00 | 2022-02-04 14:55:00+00:00 |      37854.5 |     37466.3 |     0.00834368 |\n",
      "| 255 | 2022-02-04 15:55:00+00:00 | 2022-02-04 16:55:00+00:00 |      40322.9 |     39504   |     0.0186919  |\n",
      "| 256 | 2022-02-04 17:55:00+00:00 | 2022-02-04 18:55:00+00:00 |      40387.4 |     40465.7 |    -0.00392664 |\n",
      "| 257 | 2022-02-04 20:55:00+00:00 | 2022-02-04 21:55:00+00:00 |      40567.8 |     40510.1 |    -0.00057799 |\n",
      "| 258 | 2022-02-05 00:55:00+00:00 | 2022-02-05 01:55:00+00:00 |      41260.2 |     41524.7 |    -0.0083533  |\n",
      "| 259 | 2022-02-05 02:55:00+00:00 | 2022-02-05 03:55:00+00:00 |      41434.5 |     41420   |    -0.00164742 |\n",
      "| 260 | 2022-02-05 04:55:00+00:00 | 2022-02-05 05:55:00+00:00 |      41600.1 |     41401.7 |     0.00278425 |\n",
      "| 261 | 2022-02-05 06:55:00+00:00 | 2022-02-05 08:05:00+00:00 |      41399.1 |     41511.3 |    -0.004695   |\n",
      "| 262 | 2022-02-05 14:55:00+00:00 | 2022-02-05 16:00:00+00:00 |      41694.4 |     41499.9 |     0.00268108 |\n",
      "| 263 | 2022-02-05 16:55:00+00:00 | 2022-02-05 18:00:00+00:00 |      41576.6 |     41384.5 |     0.00263601 |\n",
      "| 264 | 2022-02-05 22:55:00+00:00 | 2022-02-06 00:00:00+00:00 |      41410.3 |     41584.7 |    -0.00618491 |\n",
      "| 265 | 2022-02-06 04:55:00+00:00 | 2022-02-06 05:55:00+00:00 |      41569.2 |     41512.1 |    -0.00062453 |\n",
      "| 266 | 2022-02-06 06:55:00+00:00 | 2022-02-06 07:55:00+00:00 |      41689.5 |     41455.8 |     0.00362879 |\n",
      "| 267 | 2022-02-06 19:55:00+00:00 | 2022-02-06 20:55:00+00:00 |      41558.2 |     41656.8 |    -0.00436095 |\n",
      "| 268 | 2022-02-06 21:55:00+00:00 | 2022-02-06 23:00:00+00:00 |      41632.6 |     41672.7 |    -0.00296026 |\n",
      "| 269 | 2022-02-06 23:55:00+00:00 | 2022-02-07 00:55:00+00:00 |      41809.9 |     42434.7 |    -0.0166905  |\n",
      "| 270 | 2022-02-07 01:55:00+00:00 | 2022-02-07 03:05:00+00:00 |      42398.3 |     42180.1 |     0.00316425 |\n",
      "| 271 | 2022-02-07 03:55:00+00:00 | 2022-02-07 04:55:00+00:00 |      42770.2 |     42756.8 |    -0.00168616 |\n",
      "| 272 | 2022-02-07 05:55:00+00:00 | 2022-02-07 06:55:00+00:00 |      42750   |     42771.5 |    -0.0025006  |\n",
      "| 273 | 2022-02-07 08:55:00+00:00 | 2022-02-07 09:55:00+00:00 |      42373.7 |     42598.9 |    -0.00727536 |\n",
      "| 274 | 2022-02-07 10:55:00+00:00 | 2022-02-07 11:55:00+00:00 |      42655.7 |     42580.6 |    -0.00023664 |\n",
      "| 275 | 2022-02-07 12:55:00+00:00 | 2022-02-07 13:55:00+00:00 |      42790.2 |     42699.9 |     0.00011136 |\n",
      "| 276 | 2022-02-07 15:55:00+00:00 | 2022-02-07 16:55:00+00:00 |      43645.9 |     43654.1 |    -0.00218387 |\n",
      "| 277 | 2022-02-07 19:55:00+00:00 | 2022-02-07 20:55:00+00:00 |      43966.1 |     44362.6 |    -0.0109167  |\n",
      "| 278 | 2022-02-08 06:55:00+00:00 | 2022-02-08 07:55:00+00:00 |      44850   |     44664.2 |     0.00215407 |\n",
      "| 279 | 2022-02-08 08:55:00+00:00 | 2022-02-08 10:00:00+00:00 |      43891.1 |     44112   |    -0.0069957  |\n",
      "| 280 | 2022-02-08 10:55:00+00:00 | 2022-02-08 11:55:00+00:00 |      43880.5 |     43831.3 |    -0.00087685 |\n",
      "| 281 | 2022-02-08 14:55:00+00:00 | 2022-02-08 15:55:00+00:00 |      43393.1 |     43733.7 |    -0.00976958 |\n",
      "| 282 | 2022-02-08 23:55:00+00:00 | 2022-02-09 00:55:00+00:00 |      44219   |     44089   |     0.00094401 |\n",
      "| 283 | 2022-02-09 01:55:00+00:00 | 2022-02-09 02:55:00+00:00 |      43300.2 |     43943.7 |    -0.0166109  |\n",
      "| 284 | 2022-02-09 03:55:00+00:00 | 2022-02-09 05:15:00+00:00 |      43279.6 |     43359.9 |    -0.00384716 |\n",
      "| 285 | 2022-02-09 05:55:00+00:00 | 2022-02-09 06:55:00+00:00 |      43749.2 |     43586.2 |     0.0017331  |\n",
      "| 286 | 2022-02-09 12:55:00+00:00 | 2022-02-09 14:10:00+00:00 |      43883.4 |     44128.5 |    -0.00754114 |\n",
      "| 287 | 2022-02-09 14:55:00+00:00 | 2022-02-09 15:55:00+00:00 |      43844.9 |     44159.6 |    -0.00911018 |\n",
      "| 288 | 2022-02-09 23:55:00+00:00 | 2022-02-10 00:55:00+00:00 |      44123.5 |     44324.3 |    -0.0065192  |\n",
      "| 289 | 2022-02-10 01:55:00+00:00 | 2022-02-10 03:10:00+00:00 |      43673.4 |     44206.2 |    -0.0140276  |\n",
      "| 290 | 2022-02-10 21:55:00+00:00 | 2022-02-10 22:55:00+00:00 |      43886   |     43912.6 |    -0.00260209 |\n",
      "| 291 | 2022-02-10 23:55:00+00:00 | 2022-02-11 00:55:00+00:00 |      42931.8 |     43441.1 |    -0.0136985  |\n",
      "| 292 | 2022-02-11 01:55:00+00:00 | 2022-02-11 02:55:00+00:00 |      43059.7 |     43090   |    -0.00270001 |\n",
      "| 293 | 2022-02-11 04:55:00+00:00 | 2022-02-11 06:00:00+00:00 |      42904.2 |     43028.9 |    -0.00489189 |\n",
      "| 294 | 2022-02-11 06:55:00+00:00 | 2022-02-11 07:55:00+00:00 |      43333.7 |     43216.8 |     0.0007011  |\n",
      "| 295 | 2022-02-11 08:55:00+00:00 | 2022-02-11 09:55:00+00:00 |      43561.9 |     43296.1 |     0.004127   |\n",
      "| 296 | 2022-02-11 10:55:00+00:00 | 2022-02-11 12:10:00+00:00 |      43293.8 |     43428.6 |    -0.00509483 |\n",
      "| 297 | 2022-02-11 12:55:00+00:00 | 2022-02-11 13:55:00+00:00 |      43761.7 |     43532   |     0.00326872 |\n",
      "| 298 | 2022-02-11 14:55:00+00:00 | 2022-02-11 15:55:00+00:00 |      43741.9 |     43773.8 |    -0.00272712 |\n",
      "| 299 | 2022-02-11 16:55:00+00:00 | 2022-02-11 18:00:00+00:00 |      43405.2 |     43535   |    -0.00497309 |\n",
      "| 300 | 2022-02-11 18:55:00+00:00 | 2022-02-11 19:55:00+00:00 |      42779.4 |     42755   |    -0.00142752 |\n",
      "| 301 | 2022-02-12 03:55:00+00:00 | 2022-02-12 04:55:00+00:00 |      42288   |     42292.8 |    -0.00210985 |\n",
      "| 302 | 2022-02-12 05:55:00+00:00 | 2022-02-12 06:55:00+00:00 |      41988.6 |     42272   |    -0.00868857 |\n",
      "| 303 | 2022-02-12 15:55:00+00:00 | 2022-02-12 16:55:00+00:00 |      42677.6 |     42131   |     0.0109497  |\n",
      "| 304 | 2022-02-13 01:55:00+00:00 | 2022-02-13 02:55:00+00:00 |      42201.9 |     42290   |    -0.00407825 |\n",
      "| 305 | 2022-02-13 03:55:00+00:00 | 2022-02-13 04:55:00+00:00 |      42185.4 |     42273.7 |    -0.00408213 |\n",
      "| 306 | 2022-02-13 05:55:00+00:00 | 2022-02-13 06:55:00+00:00 |      42362   |     42245.4 |     0.00075655 |\n",
      "| 307 | 2022-02-13 07:55:00+00:00 | 2022-02-13 09:00:00+00:00 |      42444.5 |     42375.3 |    -0.00036965 |\n",
      "| 308 | 2022-02-13 09:55:00+00:00 | 2022-02-13 10:55:00+00:00 |      42563.6 |     42334.4 |     0.00340593 |\n",
      "| 309 | 2022-02-13 11:55:00+00:00 | 2022-02-13 13:00:00+00:00 |      42496   |     42498   |    -0.00204497 |\n",
      "| 310 | 2022-02-13 13:55:00+00:00 | 2022-02-13 14:55:00+00:00 |      42293.9 |     42502   |    -0.00688422 |\n",
      "| 311 | 2022-02-13 15:55:00+00:00 | 2022-02-13 16:55:00+00:00 |      42295.3 |     42604.3 |    -0.0092349  |\n",
      "| 312 | 2022-02-13 19:55:00+00:00 | 2022-02-13 20:55:00+00:00 |      42308.4 |     42012.8 |     0.00502365 |\n",
      "| 313 | 2022-02-13 21:55:00+00:00 | 2022-02-13 22:55:00+00:00 |      42329.3 |     42275   |    -0.00071659 |\n",
      "| 314 | 2022-02-13 23:55:00+00:00 | 2022-02-14 00:55:00+00:00 |      42088.1 |     42234.6 |    -0.00545838 |\n",
      "| 315 | 2022-02-14 01:55:00+00:00 | 2022-02-14 03:00:00+00:00 |      41712.9 |     41667.6 |    -0.0009118  |\n",
      "| 316 | 2022-02-14 08:55:00+00:00 | 2022-02-14 09:55:00+00:00 |      42036.2 |     42093   |    -0.00334517 |\n",
      "| 317 | 2022-02-14 10:55:00+00:00 | 2022-02-14 11:55:00+00:00 |      42111.6 |     42239.2 |    -0.00501474 |\n",
      "| 318 | 2022-02-14 15:55:00+00:00 | 2022-02-14 17:00:00+00:00 |      42670   |     42594.4 |    -0.00022643 |\n",
      "| 319 | 2022-02-14 17:55:00+00:00 | 2022-02-14 19:00:00+00:00 |      42289   |     42600   |    -0.00928318 |\n",
      "| 320 | 2022-02-14 19:55:00+00:00 | 2022-02-14 21:00:00+00:00 |      42229.3 |     42020.2 |     0.00296633 |\n",
      "| 321 | 2022-02-14 23:55:00+00:00 | 2022-02-15 00:55:00+00:00 |      42498.5 |     42569.1 |    -0.003652   |\n",
      "| 322 | 2022-02-15 01:55:00+00:00 | 2022-02-15 03:00:00+00:00 |      43556.3 |     43472.7 |    -7.949e-05  |\n",
      "| 323 | 2022-02-15 03:55:00+00:00 | 2022-02-15 04:55:00+00:00 |      43509.8 |     43601.2 |    -0.00408894 |\n",
      "| 324 | 2022-02-15 05:55:00+00:00 | 2022-02-15 06:55:00+00:00 |      43577.8 |     43605.7 |    -0.00263655 |\n",
      "| 325 | 2022-02-15 08:55:00+00:00 | 2022-02-15 09:55:00+00:00 |      44017.7 |     43925.3 |     0.00010114 |\n",
      "| 326 | 2022-02-15 10:55:00+00:00 | 2022-02-15 12:05:00+00:00 |      44233.6 |     44221.4 |    -0.00172289 |\n",
      "| 327 | 2022-02-15 12:55:00+00:00 | 2022-02-15 13:55:00+00:00 |      44282.7 |     44205.4 |    -0.00025284 |\n",
      "| 328 | 2022-02-15 14:55:00+00:00 | 2022-02-15 16:00:00+00:00 |      44213.5 |     44289.6 |    -0.00371326 |\n",
      "| 329 | 2022-02-15 20:55:00+00:00 | 2022-02-15 21:55:00+00:00 |      43986.7 |     44057.3 |    -0.00359862 |\n",
      "| 330 | 2022-02-15 22:55:00+00:00 | 2022-02-16 00:15:00+00:00 |      44385.6 |     44217.8 |     0.00178859 |\n",
      "| 331 | 2022-02-16 00:55:00+00:00 | 2022-02-16 01:55:00+00:00 |      44007.5 |     44153.4 |    -0.00529647 |\n",
      "| 332 | 2022-02-16 05:55:00+00:00 | 2022-02-16 07:00:00+00:00 |      44051.1 |     44056.5 |    -0.00212078 |\n",
      "| 333 | 2022-02-16 08:55:00+00:00 | 2022-02-16 10:00:00+00:00 |      44029.1 |     44175   |    -0.0052944  |\n",
      "| 334 | 2022-02-16 11:55:00+00:00 | 2022-02-16 13:00:00+00:00 |      44021.8 |     44150.3 |    -0.00490428 |\n",
      "| 335 | 2022-02-16 13:55:00+00:00 | 2022-02-16 15:00:00+00:00 |      43443.4 |     43644.2 |    -0.00658782 |\n",
      "| 336 | 2022-02-16 17:55:00+00:00 | 2022-02-16 18:55:00+00:00 |      43798.2 |     43577.7 |     0.00305251 |\n",
      "| 337 | 2022-02-16 20:55:00+00:00 | 2022-02-16 21:55:00+00:00 |      43966.7 |     44139   |    -0.00589288 |\n",
      "| 338 | 2022-02-16 22:55:00+00:00 | 2022-02-17 00:00:00+00:00 |      43941.9 |     44031   |    -0.00401844 |\n",
      "| 339 | 2022-02-17 02:55:00+00:00 | 2022-02-17 04:10:00+00:00 |      43425.8 |     43949.9 |    -0.0138998  |\n",
      "| 340 | 2022-02-17 04:55:00+00:00 | 2022-02-17 05:55:00+00:00 |      43579.3 |     43640.9 |    -0.00340624 |\n",
      "| 341 | 2022-02-17 06:55:00+00:00 | 2022-02-17 07:55:00+00:00 |      43900.8 |     43841.1 |    -0.00063899 |\n",
      "| 342 | 2022-02-17 08:55:00+00:00 | 2022-02-17 09:55:00+00:00 |      43176.8 |     43442.7 |    -0.00810647 |\n",
      "| 343 | 2022-02-17 10:55:00+00:00 | 2022-02-17 11:55:00+00:00 |      43179.6 |     43120.3 |    -0.00062506 |\n",
      "| 344 | 2022-02-17 12:55:00+00:00 | 2022-02-17 14:00:00+00:00 |      42243.5 |     43225.8 |    -0.0246759  |\n",
      "| 345 | 2022-02-17 14:55:00+00:00 | 2022-02-17 15:55:00+00:00 |      41884.6 |     41983.4 |    -0.00434685 |\n",
      "| 346 | 2022-02-17 16:55:00+00:00 | 2022-02-17 17:55:00+00:00 |      41715.7 |     41992.3 |    -0.00857342 |\n",
      "| 347 | 2022-02-17 18:55:00+00:00 | 2022-02-17 19:55:00+00:00 |      41145.8 |     41165   |    -0.00246421 |\n",
      "| 348 | 2022-02-17 20:55:00+00:00 | 2022-02-17 21:55:00+00:00 |      40645.6 |     40987.6 |    -0.0103246  |\n",
      "| 349 | 2022-02-17 22:55:00+00:00 | 2022-02-17 23:55:00+00:00 |      40534.7 |     40675.4 |    -0.00545018 |\n",
      "| 350 | 2022-02-18 03:55:00+00:00 | 2022-02-18 05:00:00+00:00 |      40562.8 |     40677.3 |    -0.00480771 |\n",
      "| 351 | 2022-02-18 05:55:00+00:00 | 2022-02-18 06:55:00+00:00 |      40709.4 |     40657   |    -0.00071076 |\n",
      "| 352 | 2022-02-18 07:55:00+00:00 | 2022-02-18 08:55:00+00:00 |      40804.4 |     40642.8 |     0.00197139 |\n",
      "| 353 | 2022-02-18 09:55:00+00:00 | 2022-02-18 10:55:00+00:00 |      40821.3 |     40884.6 |    -0.00354439 |\n",
      "| 354 | 2022-02-18 11:55:00+00:00 | 2022-02-18 12:55:00+00:00 |      40402.6 |     40256.4 |     0.00162622 |\n",
      "| 355 | 2022-02-18 16:55:00+00:00 | 2022-02-18 17:55:00+00:00 |      40016.6 |     40192.1 |    -0.00635482 |\n",
      "| 356 | 2022-02-18 18:55:00+00:00 | 2022-02-18 19:55:00+00:00 |      40159.6 |     40141.8 |    -0.00155571 |\n",
      "| 357 | 2022-02-18 22:55:00+00:00 | 2022-02-19 00:00:00+00:00 |      39999.2 |     39894.9 |     0.00061114 |\n",
      "| 358 | 2022-02-19 02:55:00+00:00 | 2022-02-19 04:05:00+00:00 |      40125.1 |     40170   |    -0.00311252 |\n",
      "| 359 | 2022-02-19 04:55:00+00:00 | 2022-02-19 05:55:00+00:00 |      40222.9 |     40159.4 |    -0.00041897 |\n",
      "| 360 | 2022-02-19 06:55:00+00:00 | 2022-02-19 08:00:00+00:00 |      40269.8 |     40381.3 |    -0.00475465 |\n",
      "| 361 | 2022-02-19 11:55:00+00:00 | 2022-02-19 13:10:00+00:00 |      39715   |     39760.8 |    -0.00314684 |\n",
      "| 362 | 2022-02-19 14:55:00+00:00 | 2022-02-19 15:55:00+00:00 |      39990.6 |     39928.2 |    -0.00043832 |\n",
      "| 363 | 2022-02-19 16:55:00+00:00 | 2022-02-19 17:55:00+00:00 |      40132.3 |     40068.9 |    -0.00041939 |\n",
      "| 364 | 2022-02-20 11:55:00+00:00 | 2022-02-20 13:00:00+00:00 |      38204.3 |     38338.3 |    -0.00548544 |\n",
      "| 365 | 2022-02-20 13:55:00+00:00 | 2022-02-20 14:55:00+00:00 |      38172.9 |     38258.7 |    -0.00423641 |\n",
      "| 366 | 2022-02-20 19:55:00+00:00 | 2022-02-20 21:00:00+00:00 |      38313.4 |     38411.5 |    -0.00454604 |\n",
      "| 367 | 2022-02-20 23:55:00+00:00 | 2022-02-21 00:55:00+00:00 |      38576.6 |     38444.7 |     0.00142605 |\n",
      "| 368 | 2022-02-21 05:55:00+00:00 | 2022-02-21 06:55:00+00:00 |      39205.2 |     39222.2 |    -0.00243107 |\n",
      "| 369 | 2022-02-21 07:55:00+00:00 | 2022-02-21 08:55:00+00:00 |      39093.6 |     39293.8 |    -0.00708303 |\n",
      "| 370 | 2022-02-21 09:55:00+00:00 | 2022-02-21 11:00:00+00:00 |      38339.6 |     39034.5 |    -0.0197634  |\n",
      "| 371 | 2022-02-21 13:55:00+00:00 | 2022-02-21 14:55:00+00:00 |      38666.4 |     37520.5 |     0.0284811  |\n",
      "| 372 | 2022-02-21 16:55:00+00:00 | 2022-02-21 18:00:00+00:00 |      38516.9 |     38854.2 |    -0.0106595  |\n",
      "| 373 | 2022-02-21 18:55:00+00:00 | 2022-02-21 19:55:00+00:00 |      38135.4 |     37912.2 |     0.00387779 |\n",
      "| 374 | 2022-02-21 20:55:00+00:00 | 2022-02-21 22:05:00+00:00 |      37058.5 |     38250   |    -0.0330858  |\n",
      "| 375 | 2022-02-21 22:55:00+00:00 | 2022-02-21 23:55:00+00:00 |      37042   |     37505.9 |    -0.0143412  |\n",
      "| 376 | 2022-02-22 00:55:00+00:00 | 2022-02-22 01:55:00+00:00 |      37256.5 |     37250   |    -0.00182412 |\n",
      "| 377 | 2022-02-22 10:55:00+00:00 | 2022-02-22 12:10:00+00:00 |      37406.8 |     37637.3 |    -0.00811001 |\n",
      "| 378 | 2022-02-22 12:55:00+00:00 | 2022-02-22 13:55:00+00:00 |      37615.3 |     37597.2 |    -0.00151967 |\n",
      "| 379 | 2022-02-22 18:55:00+00:00 | 2022-02-22 19:55:00+00:00 |      38011.3 |     37776.4 |     0.00420589 |\n",
      "| 380 | 2022-02-22 20:55:00+00:00 | 2022-02-22 21:55:00+00:00 |      37936.9 |     37896.9 |    -0.00094514 |\n",
      "| 381 | 2022-02-22 22:55:00+00:00 | 2022-02-23 00:00:00+00:00 |      38277.7 |     37917.3 |     0.0074871  |\n",
      "| 382 | 2022-02-23 02:55:00+00:00 | 2022-02-23 03:55:00+00:00 |      37720.6 |     37619.9 |     0.0006729  |\n",
      "| 383 | 2022-02-23 04:55:00+00:00 | 2022-02-23 05:55:00+00:00 |      37930   |     38009.9 |    -0.00409589 |\n",
      "| 384 | 2022-02-23 06:55:00+00:00 | 2022-02-23 07:55:00+00:00 |      38122.1 |     37971.6 |     0.00195784 |\n",
      "| 385 | 2022-02-23 08:55:00+00:00 | 2022-02-23 09:55:00+00:00 |      38854.7 |     38610   |     0.00432552 |\n",
      "| 386 | 2022-02-23 10:55:00+00:00 | 2022-02-23 12:00:00+00:00 |      38804.6 |     38836.7 |    -0.00282469 |\n",
      "| 387 | 2022-02-23 13:55:00+00:00 | 2022-02-23 15:10:00+00:00 |      38623   |     38983.1 |    -0.0112174  |\n",
      "| 388 | 2022-02-23 15:55:00+00:00 | 2022-02-23 16:55:00+00:00 |      38660   |     38523.6 |     0.00153535 |\n",
      "| 389 | 2022-02-23 17:55:00+00:00 | 2022-02-23 19:00:00+00:00 |      37789.9 |     38166.7 |    -0.0118492  |\n",
      "| 390 | 2022-02-23 22:55:00+00:00 | 2022-02-24 00:00:00+00:00 |      37278.2 |     37599   |    -0.0105118  |\n",
      "| 391 | 2022-02-24 03:55:00+00:00 | 2022-02-24 04:55:00+00:00 |      35031.4 |     35230.2 |    -0.00762904 |\n",
      "| 392 | 2022-02-24 06:55:00+00:00 | 2022-02-24 07:55:00+00:00 |      35313.1 |     34922.2 |     0.00917394 |\n",
      "| 393 | 2022-02-24 08:55:00+00:00 | 2022-02-24 09:55:00+00:00 |      35421.6 |     35442.7 |    -0.0025913  |\n",
      "| 394 | 2022-02-24 10:55:00+00:00 | 2022-02-24 11:55:00+00:00 |      35406.8 |     35192.9 |     0.00406835 |\n",
      "| 395 | 2022-02-24 12:55:00+00:00 | 2022-02-24 13:55:00+00:00 |      35543.8 |     35314.9 |     0.0044713  |\n",
      "| 396 | 2022-02-24 16:55:00+00:00 | 2022-02-24 17:55:00+00:00 |      35829.8 |     36038   |    -0.00776259 |\n",
      "| 397 | 2022-02-24 18:55:00+00:00 | 2022-02-24 19:55:00+00:00 |      37200   |     36428.5 |     0.0191385  |\n",
      "| 398 | 2022-02-25 04:55:00+00:00 | 2022-02-25 06:05:00+00:00 |      38453.4 |     38739.6 |    -0.00937231 |\n",
      "| 399 | 2022-02-25 06:55:00+00:00 | 2022-02-25 08:05:00+00:00 |      38466.2 |     38646.6 |    -0.0066543  |\n",
      "| 400 | 2022-02-25 08:55:00+00:00 | 2022-02-25 09:55:00+00:00 |      38307.4 |     38360   |    -0.00336752 |\n",
      "| 401 | 2022-02-25 14:55:00+00:00 | 2022-02-25 15:55:00+00:00 |      39396.2 |     39285.1 |     0.00082515 |\n",
      "| 402 | 2022-02-25 16:55:00+00:00 | 2022-02-25 18:00:00+00:00 |      38908.5 |     39292.5 |    -0.0117521  |\n",
      "| 403 | 2022-02-25 18:55:00+00:00 | 2022-02-25 20:00:00+00:00 |      38669   |     38670.6 |    -0.00203981 |\n",
      "| 404 | 2022-02-25 23:55:00+00:00 | 2022-02-26 00:55:00+00:00 |      39789.9 |     39179.8 |     0.0135424  |\n",
      "| 405 | 2022-02-26 05:55:00+00:00 | 2022-02-26 07:00:00+00:00 |      39144.6 |     39112.2 |    -0.00117306 |\n",
      "| 406 | 2022-02-26 08:55:00+00:00 | 2022-02-26 10:00:00+00:00 |      38759.8 |     38815.4 |    -0.00342962 |\n",
      "| 407 | 2022-02-26 13:55:00+00:00 | 2022-02-26 14:55:00+00:00 |      39137   |     39110   |    -0.00130902 |\n",
      "| 408 | 2022-02-26 15:55:00+00:00 | 2022-02-26 16:55:00+00:00 |      39150.9 |     39260   |    -0.00477161 |\n",
      "| 409 | 2022-02-26 17:55:00+00:00 | 2022-02-26 18:55:00+00:00 |      39056.8 |     39259   |    -0.00713737 |\n",
      "| 410 | 2022-02-26 19:55:00+00:00 | 2022-02-26 20:55:00+00:00 |      39371.1 |     39268.3 |     0.00061389 |\n",
      "| 411 | 2022-02-26 21:55:00+00:00 | 2022-02-26 23:10:00+00:00 |      39014.3 |     39396.4 |    -0.011678   |\n",
      "| 412 | 2022-02-27 00:55:00+00:00 | 2022-02-27 02:05:00+00:00 |      38343.3 |     38767.5 |    -0.0129183  |\n",
      "| 413 | 2022-02-27 07:55:00+00:00 | 2022-02-27 08:55:00+00:00 |      38826   |     38774.8 |    -0.00067839 |\n",
      "| 414 | 2022-02-27 09:55:00+00:00 | 2022-02-27 10:55:00+00:00 |      39372.3 |     39047.6 |     0.0063019  |\n",
      "| 415 | 2022-02-27 22:55:00+00:00 | 2022-02-27 23:55:00+00:00 |      37670.1 |     37707.9 |    -0.00300029 |\n",
      "| 416 | 2022-02-28 00:55:00+00:00 | 2022-02-28 02:05:00+00:00 |      38050.2 |     37575.4 |     0.0106137  |\n",
      "| 417 | 2022-02-28 02:55:00+00:00 | 2022-02-28 04:00:00+00:00 |      37802.5 |     37614   |     0.00300368 |\n",
      "| 418 | 2022-02-28 10:55:00+00:00 | 2022-02-28 11:55:00+00:00 |      38353.8 |     38316.9 |    -0.00103951 |\n",
      "| 419 | 2022-02-28 13:55:00+00:00 | 2022-02-28 14:55:00+00:00 |      39563.1 |     38003.5 |     0.0389565  |\n",
      "| 420 | 2022-02-28 16:55:00+00:00 | 2022-02-28 17:55:00+00:00 |      40982.3 |     40957.6 |    -0.00139688 |\n",
      "| 421 | 2022-02-28 18:55:00+00:00 | 2022-02-28 19:55:00+00:00 |      41261.2 |     41180.3 |    -3.618e-05  |\n",
      "| 422 | 2022-02-28 20:55:00+00:00 | 2022-02-28 21:55:00+00:00 |      41618.7 |     41845.2 |    -0.00739999 |\n",
      "| 423 | 2022-02-28 22:55:00+00:00 | 2022-03-01 00:00:00+00:00 |      43193.5 |     43405.8 |    -0.00687859 |\n",
      "| 424 | 2022-03-01 08:55:00+00:00 | 2022-03-01 10:00:00+00:00 |      43288.4 |     43443.7 |    -0.00556698 |\n",
      "| 425 | 2022-03-01 10:55:00+00:00 | 2022-03-01 11:55:00+00:00 |      43525.3 |     43451.8 |    -0.00030985 |\n",
      "| 426 | 2022-03-01 15:55:00+00:00 | 2022-03-01 16:55:00+00:00 |      43341.4 |     43432   |    -0.00408031 |\n",
      "| 427 | 2022-03-01 21:55:00+00:00 | 2022-03-01 22:55:00+00:00 |      44122.3 |     43836.4 |     0.00450934 |\n",
      "| 428 | 2022-03-02 00:55:00+00:00 | 2022-03-02 01:55:00+00:00 |      43955.6 |     44210   |    -0.00774131 |\n",
      "| 429 | 2022-03-02 17:55:00+00:00 | 2022-03-02 18:55:00+00:00 |      43594.7 |     43828.6 |    -0.00732222 |\n",
      "| 430 | 2022-03-02 19:55:00+00:00 | 2022-03-02 21:10:00+00:00 |      43766   |     43738.6 |    -0.00137235 |\n",
      "| 431 | 2022-03-03 11:55:00+00:00 | 2022-03-03 12:55:00+00:00 |      43498.4 |     43426   |    -0.00033413 |\n",
      "| 432 | 2022-03-03 18:55:00+00:00 | 2022-03-03 19:55:00+00:00 |      42172.8 |     42344   |    -0.00603159 |\n",
      "| 433 | 2022-03-03 20:55:00+00:00 | 2022-03-03 21:55:00+00:00 |      42004.4 |     41866.7 |     0.00128444 |\n",
      "| 434 | 2022-03-03 22:55:00+00:00 | 2022-03-04 00:00:00+00:00 |      42459.3 |     42513.3 |    -0.00326425 |\n",
      "| 435 | 2022-03-04 01:55:00+00:00 | 2022-03-04 03:00:00+00:00 |      41420.9 |     41392   |    -0.00129927 |\n",
      "| 436 | 2022-03-04 03:55:00+00:00 | 2022-03-04 04:55:00+00:00 |      41336.2 |     41447.5 |    -0.00467772 |\n",
      "| 437 | 2022-03-04 06:55:00+00:00 | 2022-03-04 07:55:00+00:00 |      41373.9 |     41374.9 |    -0.00202188 |\n",
      "| 438 | 2022-03-04 08:55:00+00:00 | 2022-03-04 09:55:00+00:00 |      41590.7 |     41552.6 |    -0.00108244 |\n",
      "| 439 | 2022-03-04 10:55:00+00:00 | 2022-03-04 11:55:00+00:00 |      41748.5 |     41722.1 |    -0.00136723 |\n",
      "| 440 | 2022-03-04 13:55:00+00:00 | 2022-03-04 14:55:00+00:00 |      40688   |     41322.3 |    -0.0173174  |\n",
      "| 441 | 2022-03-04 15:55:00+00:00 | 2022-03-04 17:00:00+00:00 |      40759.1 |     40683.1 |    -0.0001329  |\n",
      "| 442 | 2022-03-04 18:55:00+00:00 | 2022-03-04 19:55:00+00:00 |      39849.3 |     40647.4 |    -0.0215952  |\n",
      "| 443 | 2022-03-04 23:55:00+00:00 | 2022-03-05 00:55:00+00:00 |      38864.3 |     39130.1 |    -0.00877791 |\n",
      "| 444 | 2022-03-05 01:55:00+00:00 | 2022-03-05 02:55:00+00:00 |      38899.5 |     38987.2 |    -0.00424526 |\n",
      "| 445 | 2022-03-05 03:55:00+00:00 | 2022-03-05 04:55:00+00:00 |      38845.9 |     39039.5 |    -0.00694743 |\n",
      "| 446 | 2022-03-05 06:55:00+00:00 | 2022-03-05 08:00:00+00:00 |      39020.7 |     38976.8 |    -0.00087189 |\n",
      "| 447 | 2022-03-05 10:55:00+00:00 | 2022-03-05 11:55:00+00:00 |      39128.6 |     39088.6 |    -0.00097673 |\n",
      "| 448 | 2022-03-05 12:55:00+00:00 | 2022-03-05 13:55:00+00:00 |      38987.9 |     39127.1 |    -0.00554751 |\n",
      "| 449 | 2022-03-05 15:55:00+00:00 | 2022-03-05 16:55:00+00:00 |      39402.7 |     39130   |     0.00495766 |\n",
      "| 450 | 2022-03-05 20:55:00+00:00 | 2022-03-05 22:10:00+00:00 |      39310.4 |     39555.6 |    -0.00818549 |\n",
      "| 451 | 2022-03-05 23:55:00+00:00 | 2022-03-06 00:55:00+00:00 |      39538   |     39374.2 |     0.0021525  |\n",
      "| 452 | 2022-03-06 01:55:00+00:00 | 2022-03-06 02:55:00+00:00 |      39480.7 |     39452.7 |    -0.0012892  |\n",
      "| 453 | 2022-03-06 06:55:00+00:00 | 2022-03-06 08:00:00+00:00 |      39378.9 |     39442.9 |    -0.00361938 |\n",
      "| 454 | 2022-03-06 08:55:00+00:00 | 2022-03-06 10:15:00+00:00 |      38356.6 |     39163.3 |    -0.0225577  |\n",
      "| 455 | 2022-03-06 10:55:00+00:00 | 2022-03-06 11:55:00+00:00 |      38329.3 |     38242.5 |     0.00026719 |\n",
      "| 456 | 2022-03-06 19:55:00+00:00 | 2022-03-06 21:00:00+00:00 |      38837.8 |     38664.8 |     0.00246742 |\n",
      "| 457 | 2022-03-06 21:55:00+00:00 | 2022-03-06 22:55:00+00:00 |      38991.2 |     38992.6 |    -0.00203307 |\n",
      "| 458 | 2022-03-07 02:55:00+00:00 | 2022-03-07 03:55:00+00:00 |      37885.5 |     37787.1 |     0.00060033 |\n",
      "| 459 | 2022-03-07 04:55:00+00:00 | 2022-03-07 05:55:00+00:00 |      38087.3 |     37928   |     0.0021942  |\n",
      "| 460 | 2022-03-07 06:55:00+00:00 | 2022-03-07 08:00:00+00:00 |      37863   |     38123.9 |    -0.00882728 |\n",
      "| 461 | 2022-03-07 08:55:00+00:00 | 2022-03-07 10:00:00+00:00 |      38144.1 |     38144.7 |    -0.00201292 |\n",
      "| 462 | 2022-03-07 10:55:00+00:00 | 2022-03-07 11:55:00+00:00 |      38339.2 |     38141.3 |     0.00317787 |\n",
      "| 463 | 2022-03-07 12:55:00+00:00 | 2022-03-07 14:00:00+00:00 |      39001.5 |     38556.2 |     0.00952674 |\n",
      "| 464 | 2022-03-07 17:55:00+00:00 | 2022-03-07 18:55:00+00:00 |      38045.1 |     38850   |    -0.0226758  |\n",
      "| 465 | 2022-03-07 19:55:00+00:00 | 2022-03-07 20:55:00+00:00 |      37617.6 |     37359.8 |     0.00488975 |\n",
      "| 466 | 2022-03-07 22:55:00+00:00 | 2022-03-07 23:55:00+00:00 |      37988   |     38221.8 |    -0.00810245 |\n",
      "| 467 | 2022-03-08 00:55:00+00:00 | 2022-03-08 01:55:00+00:00 |      38383.1 |     38281.8 |     0.00064209 |\n",
      "| 468 | 2022-03-08 06:55:00+00:00 | 2022-03-08 08:00:00+00:00 |      38503.3 |     38240.5 |     0.00485847 |\n",
      "| 469 | 2022-03-08 08:55:00+00:00 | 2022-03-08 09:55:00+00:00 |      38923   |     38786.7 |     0.00150829 |\n",
      "| 470 | 2022-03-08 15:55:00+00:00 | 2022-03-08 16:55:00+00:00 |      38368   |     38563.8 |    -0.00706696 |\n",
      "| 471 | 2022-03-09 04:55:00+00:00 | 2022-03-09 05:55:00+00:00 |      41388.6 |     41557.8 |    -0.00606083 |\n",
      "| 472 | 2022-03-09 06:55:00+00:00 | 2022-03-09 07:55:00+00:00 |      41731.3 |     41519.1 |     0.00310316 |\n",
      "| 473 | 2022-03-09 08:55:00+00:00 | 2022-03-09 09:55:00+00:00 |      42076.4 |     42294.6 |    -0.00714698 |\n",
      "| 474 | 2022-03-09 10:55:00+00:00 | 2022-03-09 11:55:00+00:00 |      42148   |     42125   |    -0.00145286 |\n",
      "| 475 | 2022-03-09 13:55:00+00:00 | 2022-03-09 14:55:00+00:00 |      42150.1 |     41805.4 |     0.00623134 |\n",
      "| 476 | 2022-03-09 15:55:00+00:00 | 2022-03-09 16:55:00+00:00 |      42253.6 |     42472.2 |    -0.00713508 |\n",
      "| 477 | 2022-03-09 19:55:00+00:00 | 2022-03-09 21:00:00+00:00 |      41843.5 |     42210   |    -0.0106627  |\n",
      "| 478 | 2022-03-09 21:55:00+00:00 | 2022-03-09 22:55:00+00:00 |      41863.8 |     41860.4 |    -0.00191527 |\n",
      "| 479 | 2022-03-09 23:55:00+00:00 | 2022-03-10 01:00:00+00:00 |      41830.5 |     41973.3 |    -0.00539479 |\n",
      "| 480 | 2022-03-10 01:55:00+00:00 | 2022-03-10 03:05:00+00:00 |      40771.5 |     41033.5 |    -0.00837197 |\n",
      "| 481 | 2022-03-10 05:55:00+00:00 | 2022-03-10 06:55:00+00:00 |      39274.7 |     39228   |    -0.00081067 |\n",
      "| 482 | 2022-03-10 11:55:00+00:00 | 2022-03-10 12:55:00+00:00 |      39271.9 |     39080.9 |     0.00287902 |\n",
      "| 483 | 2022-03-10 15:55:00+00:00 | 2022-03-10 16:55:00+00:00 |      39081.2 |     39226.8 |    -0.00570132 |\n",
      "| 484 | 2022-03-11 01:55:00+00:00 | 2022-03-11 02:55:00+00:00 |      38364   |     38689.5 |    -0.0103948  |\n",
      "| 485 | 2022-03-11 03:55:00+00:00 | 2022-03-11 04:55:00+00:00 |      38484.1 |     38626.1 |    -0.0056659  |\n",
      "| 486 | 2022-03-11 06:55:00+00:00 | 2022-03-11 07:55:00+00:00 |      39169.8 |     38873   |     0.00562084 |\n",
      "| 487 | 2022-03-11 10:55:00+00:00 | 2022-03-11 12:00:00+00:00 |      39844   |     39043.6 |     0.0184633  |\n",
      "| 488 | 2022-03-11 12:55:00+00:00 | 2022-03-11 13:55:00+00:00 |      39210.3 |     39795.3 |    -0.0166669  |\n",
      "| 489 | 2022-03-11 15:55:00+00:00 | 2022-03-11 16:55:00+00:00 |      38750   |     39121.9 |    -0.0114839  |\n",
      "| 490 | 2022-03-12 00:55:00+00:00 | 2022-03-12 02:00:00+00:00 |      39185   |     39090   |     0.00042718 |\n",
      "| 491 | 2022-03-12 08:55:00+00:00 | 2022-03-12 09:55:00+00:00 |      39172.6 |     39127.2 |    -0.00084077 |\n",
      "| 492 | 2022-03-12 10:55:00+00:00 | 2022-03-12 11:55:00+00:00 |      39064.1 |     39189.5 |    -0.00519043 |\n",
      "| 493 | 2022-03-12 15:55:00+00:00 | 2022-03-12 16:55:00+00:00 |      39129.8 |     39120   |    -0.00174697 |\n",
      "| 494 | 2022-03-12 18:55:00+00:00 | 2022-03-12 19:55:00+00:00 |      39019.3 |     39087.8 |    -0.0037462  |\n",
      "| 495 | 2022-03-13 02:55:00+00:00 | 2022-03-13 03:55:00+00:00 |      39178.2 |     39171.6 |    -0.00182985 |\n",
      "| 496 | 2022-03-13 05:55:00+00:00 | 2022-03-13 06:55:00+00:00 |      39146.1 |     39132.6 |    -0.0016532  |\n",
      "| 497 | 2022-03-13 07:55:00+00:00 | 2022-03-13 08:55:00+00:00 |      39074.1 |     39117.6 |    -0.00310628 |\n",
      "| 498 | 2022-03-13 18:55:00+00:00 | 2022-03-13 19:55:00+00:00 |      38903   |     38996.9 |    -0.00440082 |\n",
      "| 499 | 2022-03-13 20:55:00+00:00 | 2022-03-13 22:00:00+00:00 |      38737.8 |     38690.3 |    -0.00077353 |\n",
      "| 500 | 2022-03-13 22:55:00+00:00 | 2022-03-13 23:55:00+00:00 |      37833.1 |     37871.2 |    -0.00300124 |\n",
      "| 501 | 2022-03-14 02:55:00+00:00 | 2022-03-14 03:55:00+00:00 |      38137.2 |     38066   |    -0.00013183 |\n",
      "| 502 | 2022-03-14 04:55:00+00:00 | 2022-03-14 05:55:00+00:00 |      38462.7 |     38494   |    -0.00280923 |\n",
      "| 503 | 2022-03-14 12:55:00+00:00 | 2022-03-14 13:55:00+00:00 |      38828   |     38808.8 |    -0.00150349 |\n",
      "| 504 | 2022-03-14 15:55:00+00:00 | 2022-03-14 17:10:00+00:00 |      38729.1 |     38855.4 |    -0.00523997 |\n",
      "| 505 | 2022-03-14 17:55:00+00:00 | 2022-03-14 18:55:00+00:00 |      38620.1 |     38696   |    -0.00395553 |\n",
      "| 506 | 2022-03-14 19:55:00+00:00 | 2022-03-14 21:05:00+00:00 |      38723.3 |     38797   |    -0.00389152 |\n",
      "| 507 | 2022-03-15 01:55:00+00:00 | 2022-03-15 02:55:00+00:00 |      39008   |     38852.9 |     0.00198549 |\n",
      "| 508 | 2022-03-15 04:55:00+00:00 | 2022-03-15 05:55:00+00:00 |      38771   |     38750.5 |    -0.00147055 |\n",
      "| 509 | 2022-03-15 06:55:00+00:00 | 2022-03-15 07:55:00+00:00 |      38412.4 |     38784.7 |    -0.0115774  |\n",
      "| 510 | 2022-03-15 08:55:00+00:00 | 2022-03-15 09:55:00+00:00 |      38571.5 |     38396   |     0.00256339 |\n",
      "| 511 | 2022-03-15 11:55:00+00:00 | 2022-03-15 12:55:00+00:00 |      38826.8 |     38615.6 |     0.00346062 |\n",
      "| 512 | 2022-03-15 14:55:00+00:00 | 2022-03-15 16:05:00+00:00 |      39017.1 |     39121.3 |    -0.0046567  |\n",
      "| 513 | 2022-03-15 16:55:00+00:00 | 2022-03-15 17:55:00+00:00 |      39264.2 |     39194   |    -0.00021176 |\n",
      "| 514 | 2022-03-15 19:55:00+00:00 | 2022-03-15 20:55:00+00:00 |      39449   |     39626.1 |    -0.0064586  |\n",
      "| 515 | 2022-03-15 22:55:00+00:00 | 2022-03-15 23:55:00+00:00 |      39335   |     39442   |    -0.00470492 |\n",
      "| 516 | 2022-03-16 00:55:00+00:00 | 2022-03-16 02:00:00+00:00 |      39473   |     39189.8 |     0.00521367 |\n",
      "| 517 | 2022-03-16 02:55:00+00:00 | 2022-03-16 04:00:00+00:00 |      39020.6 |     40882.9 |    -0.0474586  |\n",
      "| 518 | 2022-03-16 04:55:00+00:00 | 2022-03-16 05:55:00+00:00 |      39271.8 |     39162.8 |     0.00077867 |\n",
      "| 519 | 2022-03-16 06:55:00+00:00 | 2022-03-16 08:00:00+00:00 |      39563.6 |     39544.8 |    -0.00152304 |\n",
      "| 520 | 2022-03-16 08:55:00+00:00 | 2022-03-16 09:55:00+00:00 |      40467.2 |     40280.8 |     0.00261803 |\n",
      "| 521 | 2022-03-16 10:55:00+00:00 | 2022-03-16 11:55:00+00:00 |      40511   |     40363.7 |     0.00164328 |\n",
      "| 522 | 2022-03-16 12:55:00+00:00 | 2022-03-16 13:55:00+00:00 |      40500   |     40398.7 |     0.00050474 |\n",
      "| 523 | 2022-03-16 14:55:00+00:00 | 2022-03-16 15:55:00+00:00 |      40598   |     40720   |    -0.00498784 |\n",
      "| 524 | 2022-03-16 16:55:00+00:00 | 2022-03-16 18:00:00+00:00 |      40378   |     40290.2 |     0.00017733 |\n",
      "| 525 | 2022-03-16 18:55:00+00:00 | 2022-03-16 19:55:00+00:00 |      40782.4 |     39846.3 |     0.0214478  |\n",
      "| 526 | 2022-03-16 20:55:00+00:00 | 2022-03-16 22:00:00+00:00 |      41149.2 |     41269.3 |    -0.0049021  |\n",
      "| 527 | 2022-03-16 22:55:00+00:00 | 2022-03-16 23:55:00+00:00 |      41102   |     40873.5 |     0.00358223 |\n",
      "| 528 | 2022-03-17 00:55:00+00:00 | 2022-03-17 01:55:00+00:00 |      41107   |     40939.7 |     0.00208058 |\n",
      "| 529 | 2022-03-17 02:55:00+00:00 | 2022-03-17 03:55:00+00:00 |      41016   |     41089   |    -0.0037706  |\n",
      "| 530 | 2022-03-17 04:55:00+00:00 | 2022-03-17 06:00:00+00:00 |      41052   |     41071.7 |    -0.00247572 |\n",
      "| 531 | 2022-03-17 06:55:00+00:00 | 2022-03-17 07:55:00+00:00 |      40691.7 |     40717.2 |    -0.00262253 |\n",
      "| 532 | 2022-03-17 08:55:00+00:00 | 2022-03-17 09:55:00+00:00 |      40803.7 |     40824.5 |    -0.00250722 |\n",
      "| 533 | 2022-03-17 10:55:00+00:00 | 2022-03-17 11:55:00+00:00 |      40691   |     40737.8 |    -0.00314452 |\n",
      "| 534 | 2022-03-17 12:55:00+00:00 | 2022-03-17 13:55:00+00:00 |      40925   |     41042   |    -0.00484207 |\n",
      "| 535 | 2022-03-17 19:55:00+00:00 | 2022-03-17 20:55:00+00:00 |      40706.1 |     40860.4 |    -0.00576576 |\n",
      "| 536 | 2022-03-17 22:55:00+00:00 | 2022-03-17 23:55:00+00:00 |      40954.6 |     40946   |    -0.0017879  |\n",
      "| 537 | 2022-03-18 00:55:00+00:00 | 2022-03-18 01:55:00+00:00 |      40494.2 |     40690   |    -0.00679891 |\n",
      "| 538 | 2022-03-18 02:55:00+00:00 | 2022-03-18 03:55:00+00:00 |      40523   |     40508   |    -0.00162844 |\n",
      "| 539 | 2022-03-18 06:55:00+00:00 | 2022-03-18 08:00:00+00:00 |      40561.8 |     40740   |    -0.00636408 |\n",
      "| 540 | 2022-03-18 10:55:00+00:00 | 2022-03-18 11:55:00+00:00 |      40376.6 |     40486.4 |    -0.00470436 |\n",
      "| 541 | 2022-03-18 12:55:00+00:00 | 2022-03-18 13:55:00+00:00 |      40573.5 |     40461.3 |     0.00077071 |\n",
      "| 542 | 2022-03-18 23:55:00+00:00 | 2022-03-19 01:00:00+00:00 |      41894.1 |     41753.2 |     0.00136985 |\n",
      "| 543 | 2022-03-19 01:55:00+00:00 | 2022-03-19 02:55:00+00:00 |      41745.4 |     41759   |    -0.00232422 |\n",
      "| 544 | 2022-03-19 03:55:00+00:00 | 2022-03-19 05:00:00+00:00 |      41799.6 |     41740.3 |    -0.00057991 |\n",
      "| 545 | 2022-03-19 05:55:00+00:00 | 2022-03-19 06:55:00+00:00 |      41747.5 |     41746.7 |    -0.00197768 |\n",
      "| 546 | 2022-03-19 11:55:00+00:00 | 2022-03-19 12:55:00+00:00 |      41795.8 |     41721.8 |    -0.00022837 |\n",
      "| 547 | 2022-03-19 13:55:00+00:00 | 2022-03-19 14:55:00+00:00 |      41806.9 |     41771.7 |    -0.00115773 |\n",
      "| 548 | 2022-03-19 15:55:00+00:00 | 2022-03-19 16:55:00+00:00 |      42049.9 |     42010.1 |    -0.00105179 |\n",
      "| 549 | 2022-03-19 17:55:00+00:00 | 2022-03-19 18:55:00+00:00 |      41993.3 |     41843   |     0.00158706 |\n",
      "| 550 | 2022-03-19 19:55:00+00:00 | 2022-03-19 20:55:00+00:00 |      42284   |     41993   |     0.00491764 |\n",
      "| 551 | 2022-03-19 22:55:00+00:00 | 2022-03-19 23:55:00+00:00 |      42166.2 |     41939   |     0.00340904 |\n",
      "| 552 | 2022-03-20 00:55:00+00:00 | 2022-03-20 01:55:00+00:00 |      41951   |     42038   |    -0.00406319 |\n",
      "| 553 | 2022-03-20 02:55:00+00:00 | 2022-03-20 04:05:00+00:00 |      41942   |     41807   |     0.00122419 |\n",
      "| 554 | 2022-03-20 04:55:00+00:00 | 2022-03-20 05:55:00+00:00 |      41889.6 |     41858   |    -0.00124386 |\n",
      "| 555 | 2022-03-20 06:55:00+00:00 | 2022-03-20 07:55:00+00:00 |      41814   |     41932.7 |    -0.00482212 |\n",
      "| 556 | 2022-03-20 08:55:00+00:00 | 2022-03-20 09:55:00+00:00 |      41751.4 |     41887   |    -0.00522977 |\n",
      "| 557 | 2022-03-20 10:55:00+00:00 | 2022-03-20 11:55:00+00:00 |      41666.4 |     41771.8 |    -0.00451571 |\n",
      "| 558 | 2022-03-20 12:55:00+00:00 | 2022-03-20 14:00:00+00:00 |      41529.5 |     41660   |    -0.0051252  |\n",
      "| 559 | 2022-03-20 14:55:00+00:00 | 2022-03-20 15:55:00+00:00 |      41340.3 |     41426.7 |    -0.00407969 |\n",
      "| 560 | 2022-03-20 16:55:00+00:00 | 2022-03-20 17:55:00+00:00 |      41039   |     41311.5 |    -0.00858033 |\n",
      "| 561 | 2022-03-20 18:55:00+00:00 | 2022-03-20 19:55:00+00:00 |      41344.4 |     41169.1 |     0.00225057 |\n",
      "| 562 | 2022-03-20 20:55:00+00:00 | 2022-03-20 21:55:00+00:00 |      41318.5 |     41390   |    -0.00372226 |\n",
      "| 563 | 2022-03-20 22:55:00+00:00 | 2022-03-21 00:00:00+00:00 |      41216.5 |     41317.1 |    -0.0044282  |\n",
      "| 564 | 2022-03-21 01:55:00+00:00 | 2022-03-21 02:55:00+00:00 |      40667.6 |     41206.6 |    -0.0150518  |\n",
      "| 565 | 2022-03-21 03:55:00+00:00 | 2022-03-21 04:55:00+00:00 |      40874   |     40861   |    -0.00168097 |\n",
      "| 566 | 2022-03-21 06:55:00+00:00 | 2022-03-21 07:55:00+00:00 |      41091   |     40882.4 |     0.00309449 |\n",
      "| 567 | 2022-03-21 10:55:00+00:00 | 2022-03-21 12:00:00+00:00 |      41287.8 |     41304.9 |    -0.00240972 |\n",
      "| 568 | 2022-03-21 13:55:00+00:00 | 2022-03-21 14:55:00+00:00 |      40920.9 |     41208.1 |    -0.00895406 |\n",
      "| 569 | 2022-03-21 15:55:00+00:00 | 2022-03-21 17:00:00+00:00 |      40807.9 |     41158.7 |    -0.0105031  |\n",
      "| 570 | 2022-03-21 17:55:00+00:00 | 2022-03-21 18:55:00+00:00 |      40982   |     41011.2 |    -0.00270882 |\n",
      "| 571 | 2022-03-21 20:55:00+00:00 | 2022-03-21 21:55:00+00:00 |      41270.7 |     41224.5 |    -0.00087979 |\n",
      "| 572 | 2022-03-21 22:55:00+00:00 | 2022-03-22 00:05:00+00:00 |      41002.3 |     41182.2 |    -0.00635864 |\n",
      "| 573 | 2022-03-22 04:55:00+00:00 | 2022-03-22 05:55:00+00:00 |      42871.3 |     42979.8 |    -0.00451601 |\n",
      "| 574 | 2022-03-22 06:55:00+00:00 | 2022-03-22 08:00:00+00:00 |      42234.8 |     42140.8 |     0.00023029 |\n",
      "| 575 | 2022-03-22 08:55:00+00:00 | 2022-03-22 09:55:00+00:00 |      42470.4 |     42550.2 |    -0.00386945 |\n",
      "| 576 | 2022-03-22 10:55:00+00:00 | 2022-03-22 11:55:00+00:00 |      42937.5 |     42870   |    -0.00042569 |\n",
      "| 577 | 2022-03-22 19:55:00+00:00 | 2022-03-22 20:55:00+00:00 |      42573.8 |     42498.7 |    -0.00023301 |\n",
      "| 578 | 2022-03-22 21:55:00+00:00 | 2022-03-22 22:55:00+00:00 |      42524   |     42837.2 |    -0.00929433 |\n",
      "| 579 | 2022-03-22 23:55:00+00:00 | 2022-03-23 01:00:00+00:00 |      42398   |     42370.5 |    -0.00134956 |\n",
      "| 580 | 2022-03-23 04:55:00+00:00 | 2022-03-23 05:55:00+00:00 |      42032   |     41918   |     0.00071616 |\n",
      "| 581 | 2022-03-23 06:55:00+00:00 | 2022-03-23 07:55:00+00:00 |      42109.6 |     42225.8 |    -0.00474414 |\n",
      "| 582 | 2022-03-23 08:55:00+00:00 | 2022-03-23 09:55:00+00:00 |      42149   |     42257   |    -0.00454869 |\n",
      "| 583 | 2022-03-23 12:55:00+00:00 | 2022-03-23 13:55:00+00:00 |      42017.3 |     42288.9 |    -0.00840671 |\n",
      "| 584 | 2022-03-23 19:55:00+00:00 | 2022-03-23 21:00:00+00:00 |      42332.4 |     42300   |    -0.00123357 |\n",
      "| 585 | 2022-03-23 21:55:00+00:00 | 2022-03-23 22:55:00+00:00 |      42488.7 |     42380   |     0.00056058 |\n",
      "| 586 | 2022-03-23 23:55:00+00:00 | 2022-03-24 00:55:00+00:00 |      43094   |     42866   |     0.00331027 |\n",
      "| 587 | 2022-03-24 01:55:00+00:00 | 2022-03-24 02:55:00+00:00 |      42919.9 |     42752.5 |     0.00190856 |\n",
      "| 588 | 2022-03-24 04:55:00+00:00 | 2022-03-24 05:55:00+00:00 |      42985.1 |     42934.3 |    -0.0008167  |\n",
      "| 589 | 2022-03-24 06:55:00+00:00 | 2022-03-24 07:55:00+00:00 |      43084.2 |     43128.1 |    -0.0030141  |\n",
      "| 590 | 2022-03-24 08:55:00+00:00 | 2022-03-24 09:55:00+00:00 |      43016.7 |     43061   |    -0.00302402 |\n",
      "| 591 | 2022-03-24 10:55:00+00:00 | 2022-03-24 11:55:00+00:00 |      42943.9 |     43045   |    -0.00434225 |\n",
      "| 592 | 2022-03-24 12:55:00+00:00 | 2022-03-24 13:55:00+00:00 |      42944   |     42824   |     0.0007988  |\n",
      "| 593 | 2022-03-24 14:55:00+00:00 | 2022-03-24 15:55:00+00:00 |      43859.5 |     43359.9 |     0.00950115 |\n",
      "| 594 | 2022-03-24 17:55:00+00:00 | 2022-03-24 18:55:00+00:00 |      43962.3 |     43926.4 |    -0.00118213 |\n",
      "| 595 | 2022-03-24 20:55:00+00:00 | 2022-03-24 21:55:00+00:00 |      44025.2 |     43896.2 |     0.00093624 |\n",
      "| 596 | 2022-03-25 04:55:00+00:00 | 2022-03-25 06:00:00+00:00 |      44075.7 |     44000.7 |    -0.00029507 |\n",
      "| 597 | 2022-03-25 07:55:00+00:00 | 2022-03-25 08:55:00+00:00 |      43981.2 |     43920   |    -0.00060689 |\n",
      "| 598 | 2022-03-25 11:55:00+00:00 | 2022-03-25 12:55:00+00:00 |      44822.9 |     44612.9 |     0.00269975 |\n",
      "| 599 | 2022-03-25 14:55:00+00:00 | 2022-03-25 15:55:00+00:00 |      44245.5 |     44909.8 |    -0.0167597  |\n",
      "| 600 | 2022-03-25 22:55:00+00:00 | 2022-03-26 00:05:00+00:00 |      44340.1 |     44378.5 |    -0.00286268 |\n",
      "| 601 | 2022-03-26 04:55:00+00:00 | 2022-03-26 05:55:00+00:00 |      44497.8 |     44435   |    -0.00058798 |\n",
      "| 602 | 2022-03-26 06:55:00+00:00 | 2022-03-26 07:55:00+00:00 |      44525.2 |     44407.2 |     0.00065234 |\n",
      "| 603 | 2022-03-26 08:55:00+00:00 | 2022-03-26 09:55:00+00:00 |      44465.1 |     44457.4 |    -0.00182492 |\n",
      "| 604 | 2022-03-26 16:55:00+00:00 | 2022-03-26 17:55:00+00:00 |      44391.9 |     44292.9 |     0.00023265 |\n",
      "| 605 | 2022-03-26 20:55:00+00:00 | 2022-03-26 21:55:00+00:00 |      44445   |     44477   |    -0.00271671 |\n",
      "| 606 | 2022-03-26 22:55:00+00:00 | 2022-03-27 00:00:00+00:00 |      44488   |     44394   |     0.00011517 |\n",
      "| 607 | 2022-03-27 00:55:00+00:00 | 2022-03-27 01:55:00+00:00 |      44701   |     44668.8 |    -0.00127903 |\n",
      "| 608 | 2022-03-27 02:55:00+00:00 | 2022-03-27 03:55:00+00:00 |      44675   |     44669.8 |    -0.00188182 |\n",
      "| 609 | 2022-03-27 08:55:00+00:00 | 2022-03-27 09:55:00+00:00 |      44581.1 |     44633.3 |    -0.0031652  |\n",
      "| 610 | 2022-03-27 10:55:00+00:00 | 2022-03-27 11:55:00+00:00 |      44541.5 |     44559.3 |    -0.00239779 |\n",
      "| 611 | 2022-03-27 12:55:00+00:00 | 2022-03-27 13:55:00+00:00 |      44590.5 |     44472.2 |     0.00065835 |\n",
      "| 612 | 2022-03-27 15:55:00+00:00 | 2022-03-27 16:55:00+00:00 |      44915   |     44826.9 |    -3.704e-05  |\n",
      "| 613 | 2022-03-27 18:55:00+00:00 | 2022-03-27 19:55:00+00:00 |      44830.9 |     44859.2 |    -0.00262872 |\n",
      "| 614 | 2022-03-27 20:55:00+00:00 | 2022-03-27 22:05:00+00:00 |      46552.1 |     45979.8 |     0.0104221  |\n",
      "| 615 | 2022-03-27 22:55:00+00:00 | 2022-03-27 23:55:00+00:00 |      46826.8 |     46636.2 |     0.00208122 |\n",
      "| 616 | 2022-03-28 00:55:00+00:00 | 2022-03-28 01:55:00+00:00 |      46813.6 |     46927.8 |    -0.00442709 |\n",
      "| 617 | 2022-03-28 06:55:00+00:00 | 2022-03-28 07:55:00+00:00 |      46974.7 |     46954   |    -0.00155845 |\n",
      "| 618 | 2022-03-28 08:55:00+00:00 | 2022-03-28 09:55:00+00:00 |      47136.3 |     47027.9 |     0.00030262 |\n",
      "| 619 | 2022-03-28 10:55:00+00:00 | 2022-03-28 12:00:00+00:00 |      47243   |     47194.2 |    -0.00096668 |\n",
      "| 620 | 2022-03-28 17:55:00+00:00 | 2022-03-28 18:55:00+00:00 |      47970.9 |     47589.5 |     0.00599952 |\n",
      "| 621 | 2022-03-28 22:55:00+00:00 | 2022-03-28 23:55:00+00:00 |      47178.7 |     47572   |    -0.0102489  |\n",
      "| 622 | 2022-03-29 00:55:00+00:00 | 2022-03-29 02:00:00+00:00 |      47561.9 |     47409.9 |     0.00120252 |\n",
      "| 623 | 2022-03-29 02:55:00+00:00 | 2022-03-29 03:55:00+00:00 |      47321.8 |     47450.2 |    -0.0046988  |\n",
      "| 624 | 2022-03-29 06:55:00+00:00 | 2022-03-29 07:55:00+00:00 |      47405   |     47542.1 |    -0.00487579 |\n",
      "| 625 | 2022-03-29 08:55:00+00:00 | 2022-03-29 09:55:00+00:00 |      47592.1 |     47572   |    -0.00157528 |\n",
      "| 626 | 2022-03-29 12:55:00+00:00 | 2022-03-29 13:55:00+00:00 |      47623.3 |     47854.8 |    -0.00682464 |\n",
      "| 627 | 2022-03-29 14:55:00+00:00 | 2022-03-29 15:55:00+00:00 |      47516   |     47810   |    -0.00813526 |\n",
      "| 628 | 2022-03-29 16:55:00+00:00 | 2022-03-29 17:55:00+00:00 |      47419.1 |     47450   |    -0.0026477  |\n",
      "| 629 | 2022-03-29 18:55:00+00:00 | 2022-03-29 20:00:00+00:00 |      47680   |     47500   |     0.00178369 |\n",
      "| 630 | 2022-03-29 20:55:00+00:00 | 2022-03-29 21:55:00+00:00 |      47242   |     47477.8 |    -0.00695482 |\n",
      "| 631 | 2022-03-30 00:55:00+00:00 | 2022-03-30 01:55:00+00:00 |      46779.5 |     47159   |    -0.0100285  |\n",
      "| 632 | 2022-03-30 04:55:00+00:00 | 2022-03-30 05:55:00+00:00 |      47430.5 |     47200.8 |     0.00285808 |\n",
      "| 633 | 2022-03-30 06:55:00+00:00 | 2022-03-30 07:55:00+00:00 |      47446   |     47350   |     2.583e-05  |\n",
      "| 634 | 2022-03-30 08:55:00+00:00 | 2022-03-30 09:55:00+00:00 |      47257   |     47425.2 |    -0.00553776 |\n",
      "| 635 | 2022-03-30 14:55:00+00:00 | 2022-03-30 15:55:00+00:00 |      47135.7 |     47201.6 |    -0.00339135 |\n",
      "| 636 | 2022-03-30 16:55:00+00:00 | 2022-03-30 17:55:00+00:00 |      47590.8 |     47287.7 |     0.00439974 |\n",
      "| 637 | 2022-03-30 18:55:00+00:00 | 2022-03-30 19:55:00+00:00 |      46961.2 |     47323.9 |    -0.00964753 |\n",
      "| 638 | 2022-03-30 20:55:00+00:00 | 2022-03-30 21:55:00+00:00 |      46990.5 |     47192.5 |    -0.00627127 |\n",
      "| 639 | 2022-03-31 00:55:00+00:00 | 2022-03-31 01:55:00+00:00 |      47385.1 |     47241.8 |     0.00102905 |\n",
      "| 640 | 2022-03-31 02:55:00+00:00 | 2022-03-31 04:00:00+00:00 |      47083.9 |     47184   |    -0.00411503 |\n",
      "| 641 | 2022-03-31 04:55:00+00:00 | 2022-03-31 05:55:00+00:00 |      47077.1 |     47026.6 |    -0.00092629 |\n",
      "| 642 | 2022-03-31 06:55:00+00:00 | 2022-03-31 07:55:00+00:00 |      47160.8 |     47140.8 |    -0.00157501 |\n",
      "| 643 | 2022-03-31 08:55:00+00:00 | 2022-03-31 09:55:00+00:00 |      47246   |     47259   |    -0.00227148 |\n",
      "| 644 | 2022-03-31 10:55:00+00:00 | 2022-03-31 11:55:00+00:00 |      47122.5 |     47224.8 |    -0.00416033 |\n",
      "| 645 | 2022-03-31 15:55:00+00:00 | 2022-03-31 17:00:00+00:00 |      45753   |     46462.4 |    -0.0172351  |\n",
      "| 646 | 2022-03-31 18:55:00+00:00 | 2022-03-31 19:55:00+00:00 |      45625.3 |     45905.1 |    -0.00808057 |\n",
      "| 647 | 2022-03-31 20:55:00+00:00 | 2022-03-31 21:55:00+00:00 |      45775   |     45757   |    -0.00160541 |\n",
      "| 648 | 2022-03-31 22:55:00+00:00 | 2022-03-31 23:55:00+00:00 |      45471.5 |     45793.1 |    -0.00900621 |\n",
      "| 649 | 2022-04-01 00:55:00+00:00 | 2022-04-01 02:00:00+00:00 |      44903.3 |     45576.2 |    -0.0167319  |\n",
      "| 650 | 2022-04-01 02:55:00+00:00 | 2022-04-01 03:55:00+00:00 |      44509.6 |     44397   |     0.00053224 |\n",
      "| 651 | 2022-04-01 04:55:00+00:00 | 2022-04-01 05:55:00+00:00 |      44648.1 |     44684.7 |    -0.00281566 |\n",
      "| 652 | 2022-04-01 06:55:00+00:00 | 2022-04-01 08:00:00+00:00 |      45042   |     44830.7 |     0.00270721 |\n",
      "| 653 | 2022-04-01 08:55:00+00:00 | 2022-04-01 10:00:00+00:00 |      45105.4 |     45155.7 |    -0.00310837 |\n",
      "| 654 | 2022-04-01 11:55:00+00:00 | 2022-04-01 12:55:00+00:00 |      45200   |     45081.9 |     0.00061711 |\n",
      "| 655 | 2022-04-01 13:55:00+00:00 | 2022-04-01 14:55:00+00:00 |      45953   |     45474.4 |     0.00850557 |\n",
      "| 656 | 2022-04-01 17:55:00+00:00 | 2022-04-01 18:55:00+00:00 |      46311.5 |     46474.3 |    -0.0054953  |\n",
      "| 657 | 2022-04-01 19:55:00+00:00 | 2022-04-01 20:55:00+00:00 |      46119   |     46287.2 |    -0.00562329 |\n",
      "| 658 | 2022-04-01 21:55:00+00:00 | 2022-04-01 22:55:00+00:00 |      46250   |     46305.6 |    -0.00319589 |\n",
      "| 659 | 2022-04-01 23:55:00+00:00 | 2022-04-02 00:55:00+00:00 |      46894.9 |     46264.4 |     0.011603   |\n",
      "| 660 | 2022-04-02 01:55:00+00:00 | 2022-04-02 02:55:00+00:00 |      46483.7 |     46766   |    -0.00802131 |\n",
      "| 661 | 2022-04-02 09:55:00+00:00 | 2022-04-02 10:55:00+00:00 |      46613.6 |     46688.4 |    -0.00359563 |\n",
      "| 662 | 2022-04-02 11:55:00+00:00 | 2022-04-02 13:05:00+00:00 |      46717.5 |     46590   |     0.00073295 |\n",
      "| 663 | 2022-04-02 13:55:00+00:00 | 2022-04-02 14:55:00+00:00 |      46622.2 |     46777   |    -0.00529964 |\n",
      "| 664 | 2022-04-02 15:55:00+00:00 | 2022-04-02 16:55:00+00:00 |      46320.4 |     46180.3 |     0.00102948 |\n",
      "| 665 | 2022-04-02 23:55:00+00:00 | 2022-04-03 00:55:00+00:00 |      46017.5 |     45842.6 |     0.0018096  |\n",
      "| 666 | 2022-04-03 02:55:00+00:00 | 2022-04-03 03:55:00+00:00 |      45924.5 |     45780   |     0.00115187 |\n",
      "| 667 | 2022-04-03 06:55:00+00:00 | 2022-04-03 07:55:00+00:00 |      46346.4 |     46430   |    -0.0037941  |\n",
      "| 668 | 2022-04-03 10:55:00+00:00 | 2022-04-03 12:10:00+00:00 |      46196.2 |     46150   |    -0.0009987  |\n",
      "| 669 | 2022-04-03 12:55:00+00:00 | 2022-04-03 14:00:00+00:00 |      46437   |     46410.9 |    -0.00143719 |\n",
      "| 670 | 2022-04-03 15:55:00+00:00 | 2022-04-03 16:55:00+00:00 |      46290   |     46428.8 |    -0.00498112 |\n",
      "| 671 | 2022-04-03 17:55:00+00:00 | 2022-04-03 18:55:00+00:00 |      46583.8 |     46350   |     0.00303658 |\n",
      "| 672 | 2022-04-03 19:55:00+00:00 | 2022-04-03 21:00:00+00:00 |      46369.8 |     46445.8 |    -0.00363062 |\n",
      "| 673 | 2022-04-03 21:55:00+00:00 | 2022-04-03 22:55:00+00:00 |      46865.9 |     46717   |     0.00118333 |\n",
      "| 674 | 2022-04-03 23:55:00+00:00 | 2022-04-04 00:55:00+00:00 |      45941.5 |     46321.6 |    -0.0101866  |\n",
      "| 675 | 2022-04-04 03:55:00+00:00 | 2022-04-04 05:10:00+00:00 |      46039.2 |     46069   |    -0.00264335 |\n",
      "| 676 | 2022-04-04 05:55:00+00:00 | 2022-04-04 07:00:00+00:00 |      46203.6 |     46209.5 |    -0.00212586 |\n",
      "| 677 | 2022-04-04 10:55:00+00:00 | 2022-04-04 11:55:00+00:00 |      46152.4 |     46000.7 |     0.00129427 |\n",
      "| 678 | 2022-04-04 13:55:00+00:00 | 2022-04-04 14:55:00+00:00 |      45930.9 |     46166.3 |    -0.00708569 |\n",
      "| 679 | 2022-04-04 16:55:00+00:00 | 2022-04-04 17:55:00+00:00 |      45344.7 |     45751   |    -0.0108609  |\n",
      "| 680 | 2022-04-04 18:55:00+00:00 | 2022-04-04 19:55:00+00:00 |      45856.8 |     45497.7 |     0.00587827 |\n",
      "| 681 | 2022-04-04 20:55:00+00:00 | 2022-04-04 21:55:00+00:00 |      46521   |     46265.5 |     0.00351344 |\n",
      "| 682 | 2022-04-05 01:55:00+00:00 | 2022-04-05 02:55:00+00:00 |      46610.7 |     46636   |    -0.00253963 |\n",
      "| 683 | 2022-04-05 04:55:00+00:00 | 2022-04-05 05:55:00+00:00 |      46733.8 |     46790.4 |    -0.00320502 |\n",
      "| 684 | 2022-04-05 13:55:00+00:00 | 2022-04-05 14:55:00+00:00 |      45872   |     46469.3 |    -0.0148273  |\n",
      "| 685 | 2022-04-05 19:55:00+00:00 | 2022-04-05 20:55:00+00:00 |      45710   |     45943.9 |    -0.00707796 |\n",
      "| 686 | 2022-04-05 21:55:00+00:00 | 2022-04-05 23:00:00+00:00 |      45846.5 |     45892.9 |    -0.00300703 |\n",
      "| 687 | 2022-04-06 00:55:00+00:00 | 2022-04-06 02:00:00+00:00 |      45153.5 |     45089.3 |    -0.00057656 |\n",
      "| 688 | 2022-04-06 02:55:00+00:00 | 2022-04-06 04:00:00+00:00 |      45219.2 |     45269.5 |    -0.00310735 |\n",
      "| 689 | 2022-04-06 04:55:00+00:00 | 2022-04-06 05:55:00+00:00 |      45360.7 |     45336.7 |    -0.00146969 |\n",
      "| 690 | 2022-04-06 11:55:00+00:00 | 2022-04-06 12:55:00+00:00 |      44813   |     44910   |    -0.00415423 |\n",
      "| 691 | 2022-04-06 13:55:00+00:00 | 2022-04-06 15:15:00+00:00 |      44077.7 |     44267.7 |    -0.00628037 |\n",
      "| 692 | 2022-04-06 15:55:00+00:00 | 2022-04-06 16:55:00+00:00 |      43736.3 |     44190   |    -0.012245   |\n",
      "| 693 | 2022-04-06 17:55:00+00:00 | 2022-04-06 18:55:00+00:00 |      43767.5 |     43918.4 |    -0.00542637 |\n",
      "| 694 | 2022-04-06 19:55:00+00:00 | 2022-04-06 20:55:00+00:00 |      43919.5 |     43820   |     0.0002672  |\n",
      "| 695 | 2022-04-06 21:55:00+00:00 | 2022-04-06 22:55:00+00:00 |      43625.7 |     43902   |    -0.0082774  |\n",
      "| 696 | 2022-04-07 00:55:00+00:00 | 2022-04-07 01:55:00+00:00 |      43326.9 |     42947.2 |     0.00682706 |\n",
      "| 697 | 2022-04-07 06:55:00+00:00 | 2022-04-07 07:55:00+00:00 |      43490.9 |     43416.2 |    -0.0002818  |\n",
      "| 698 | 2022-04-07 08:55:00+00:00 | 2022-04-07 09:55:00+00:00 |      43365.6 |     43448.5 |    -0.00390334 |\n",
      "| 699 | 2022-04-07 10:55:00+00:00 | 2022-04-07 11:55:00+00:00 |      43750.5 |     43426   |     0.00545954 |\n",
      "| 700 | 2022-04-07 19:55:00+00:00 | 2022-04-07 21:00:00+00:00 |      43561.9 |     43426.3 |     0.00111852 |\n",
      "| 701 | 2022-04-07 22:55:00+00:00 | 2022-04-08 00:00:00+00:00 |      43458.2 |     43596.6 |    -0.00516622 |\n",
      "| 702 | 2022-04-08 04:55:00+00:00 | 2022-04-08 05:55:00+00:00 |      43656.1 |     43536.8 |     0.00073696 |\n",
      "| 703 | 2022-04-08 08:55:00+00:00 | 2022-04-08 10:00:00+00:00 |      43542.5 |     43805.9 |    -0.00799864 |\n",
      "| 704 | 2022-04-08 10:55:00+00:00 | 2022-04-08 12:05:00+00:00 |      43301.9 |     43575.4 |    -0.00826103 |\n",
      "| 705 | 2022-04-08 12:55:00+00:00 | 2022-04-08 13:55:00+00:00 |      42922.8 |     43216.1 |    -0.00877288 |\n",
      "| 706 | 2022-04-08 15:55:00+00:00 | 2022-04-08 16:55:00+00:00 |      43481.8 |     43667.2 |    -0.00623664 |\n",
      "| 707 | 2022-04-08 18:55:00+00:00 | 2022-04-08 19:55:00+00:00 |      42677   |     42893.1 |    -0.00702512 |\n"
     ]
    }
   ],
   "source": [
    "# print(get_backtest_repo().tail(3))\n",
    "# print(report.backtest_data['trades'])\n",
    "print(report.trades[['open_date', 'close_date', 'close_rate', 'open_rate', 'profit_ratio']].to_markdown())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result for strategy SagesGymCNN2 #14 | Hyperopt: False | Hyperopt id: \n",
      "========================================================== BACKTESTING REPORT =========================================================\n",
      "|     Pair |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit USDT |   Tot Profit % |   Avg Duration |   Win  Draw  Loss  Win% |\n",
      "|----------+--------+----------------+----------------+-------------------+----------------+----------------+-------------------------|\n",
      "| BTC/USDT |    748 |          -0.28 |        -212.58 |          -881.062 |         -88.11 |        1:01:00 |   241     0   507  32.2 |\n",
      "|    TOTAL |    748 |          -0.28 |        -212.58 |          -881.062 |         -88.11 |        1:01:00 |   241     0   507  32.2 |\n",
      "========================================================== BUY TAG STATS ===========================================================\n",
      "|   TAG |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit USDT |   Tot Profit % |   Avg Duration |   Win  Draw  Loss  Win% |\n",
      "|-------+--------+----------------+----------------+-------------------+----------------+----------------+-------------------------|\n",
      "| TOTAL |    748 |          -0.28 |        -212.58 |          -881.062 |         -88.11 |        1:01:00 |   241     0   507  32.2 |\n",
      "===================================================== SELL REASON STATS =====================================================\n",
      "|   Sell Reason |   Sells |   Win  Draws  Loss  Win% |   Avg Profit % |   Cum Profit % |   Tot Profit USDT |   Tot Profit % |\n",
      "|---------------+---------+--------------------------+----------------+----------------+-------------------+----------------|\n",
      "|   sell_signal |     748 |    241     0   507  32.2 |          -0.28 |        -212.58 |          -881.062 |        -212.58 |\n",
      "====================================================== LEFT OPEN TRADES REPORT ======================================================\n",
      "|   Pair |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit USDT |   Tot Profit % |   Avg Duration |   Win  Draw  Loss  Win% |\n",
      "|--------+--------+----------------+----------------+-------------------+----------------+----------------+-------------------------|\n",
      "|  TOTAL |      0 |           0.00 |           0.00 |             0.000 |           0.00 |           0:00 |     0     0     0     0 |\n",
      "=============== SUMMARY METRICS ================\n",
      "| Metric                 | Value               |\n",
      "|------------------------+---------------------|\n",
      "| Backtesting from       | 2022-01-01 00:00:00 |\n",
      "| Backtesting to         | 2022-04-08 22:00:00 |\n",
      "| Max open trades        | 1                   |\n",
      "|                        |                     |\n",
      "| Total/Daily Avg Trades | 748 / 7.71          |\n",
      "| Starting balance       | 1000 USDT           |\n",
      "| Final balance          | 118.938 USDT        |\n",
      "| Absolute profit        | -881.062 USDT       |\n",
      "| Total profit %         | -88.11%             |\n",
      "| Trades per day         | 7.71                |\n",
      "| Avg. daily profit %    | -0.91%              |\n",
      "| Avg. stake amount      | 394.771 USDT        |\n",
      "| Total trade volume     | 295288.43 USDT      |\n",
      "|                        |                     |\n",
      "| Best Pair              | BTC/USDT -212.58%   |\n",
      "| Worst Pair             | BTC/USDT -212.58%   |\n",
      "| Best trade             | BTC/USDT 3.26%      |\n",
      "| Worst trade            | BTC/USDT -4.68%     |\n",
      "| Best day               | 3.186 USDT          |\n",
      "| Worst day              | -53.368 USDT        |\n",
      "| Days win/draw/lose     | 10 / 1 / 87         |\n",
      "| Avg. Duration Winners  | 1:01:00             |\n",
      "| Avg. Duration Loser    | 1:02:00             |\n",
      "| Rejected Buy signals   | 0                   |\n",
      "| Entry/Exit Timeouts    | 79 / 0              |\n",
      "|                        |                     |\n",
      "| Min balance            | 118.938 USDT        |\n",
      "| Max balance            | 1007.805 USDT       |\n",
      "| Drawdown (Account)     | 88.20%              |\n",
      "| Drawdown               | 888.868 USDT        |\n",
      "| Drawdown high          | 7.805 USDT          |\n",
      "| Drawdown low           | -881.062 USDT       |\n",
      "| Drawdown Start         | 2022-01-01 08:00:00 |\n",
      "| Drawdown End           | 2022-04-08 22:00:00 |\n",
      "| Market change          | -12.71%             |\n",
      "================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(report.report_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-08 22:42:54.290162: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-08 22:42:54.312673: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-08 22:42:54.312797: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-08 22:42:54.313404: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-04-08 22:42:54.313728: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-08 22:42:54.313841: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-08 22:42:54.313930: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-08 22:42:54.831471: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-08 22:42:54.831609: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-08 22:42:54.831710: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-08 22:42:54.831788: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6233 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1070 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "from keras.models import load_model\n",
    "\n",
    "model = load_model('tuner/20220408181048/best_model.h5')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-08 22:43:01.559525: I tensorflow/stream_executor/cuda/cuda_dnn.cc:368] Loaded cuDNN version 8302\n",
      "2022-04-08 22:43:01.832578: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2022-04-08 22:43:01.833041: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2022-04-08 22:43:01.833062: W tensorflow/stream_executor/gpu/asm_compiler.cc:80] Couldn't get ptxas version string: INTERNAL: Couldn't invoke ptxas --version\n",
      "2022-04-08 22:43:01.833546: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2022-04-08 22:43:01.833640: W tensorflow/stream_executor/gpu/redzone_allocator.cc:314] INTERNAL: Failed to launch ptxas\n",
      "Relying on driver to perform ptx compilation. \n",
      "Modify $PATH to customize ptxas location.\n",
      "This message will be only logged once.\n",
      "2022-04-08 22:43:02.330453: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.36GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
      "2022-04-08 22:43:02.525024: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.22GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
      "2022-04-08 22:43:20.445434: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.63GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('SHORT', array([0.383921, 0.616079], dtype=float32)),\n",
      " ('LONG', array([0.37017214, 0.62982786], dtype=float32))]\n"
     ]
    }
   ],
   "source": [
    "predict = model.predict(test_generator)\n",
    "clear_session()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.383921   0.616079  ]\n",
      " [0.37017214 0.62982786]\n",
      " [0.37109217 0.62890786]\n",
      " ...\n",
      " [0.46331537 0.53668463]\n",
      " [0.438827   0.56117296]\n",
      " [0.43346682 0.56653315]]\n"
     ]
    }
   ],
   "source": [
    "print(predict)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [],
   "source": [
    "dt = report.trades['open_date'].dt.strftime(\"%Y%m%d%H%M%S\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on DatetimeProperties in module pandas.core.indexes.accessors object:\n",
      "\n",
      "class DatetimeProperties(Properties)\n",
      " |  DatetimeProperties(data: 'Series', orig)\n",
      " |  \n",
      " |  Accessor object for datetimelike properties of the Series values.\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  >>> seconds_series = pd.Series(pd.date_range(\"2000-01-01\", periods=3, freq=\"s\"))\n",
      " |  >>> seconds_series\n",
      " |  0   2000-01-01 00:00:00\n",
      " |  1   2000-01-01 00:00:01\n",
      " |  2   2000-01-01 00:00:02\n",
      " |  dtype: datetime64[ns]\n",
      " |  >>> seconds_series.dt.second\n",
      " |  0    0\n",
      " |  1    1\n",
      " |  2    2\n",
      " |  dtype: int64\n",
      " |  \n",
      " |  >>> hours_series = pd.Series(pd.date_range(\"2000-01-01\", periods=3, freq=\"h\"))\n",
      " |  >>> hours_series\n",
      " |  0   2000-01-01 00:00:00\n",
      " |  1   2000-01-01 01:00:00\n",
      " |  2   2000-01-01 02:00:00\n",
      " |  dtype: datetime64[ns]\n",
      " |  >>> hours_series.dt.hour\n",
      " |  0    0\n",
      " |  1    1\n",
      " |  2    2\n",
      " |  dtype: int64\n",
      " |  \n",
      " |  >>> quarters_series = pd.Series(pd.date_range(\"2000-01-01\", periods=3, freq=\"q\"))\n",
      " |  >>> quarters_series\n",
      " |  0   2000-03-31\n",
      " |  1   2000-06-30\n",
      " |  2   2000-09-30\n",
      " |  dtype: datetime64[ns]\n",
      " |  >>> quarters_series.dt.quarter\n",
      " |  0    1\n",
      " |  1    2\n",
      " |  2    3\n",
      " |  dtype: int64\n",
      " |  \n",
      " |  Returns a Series indexed like the original Series.\n",
      " |  Raises TypeError if the Series does not contain datetimelike values.\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      DatetimeProperties\n",
      " |      Properties\n",
      " |      pandas.core.accessor.PandasDelegate\n",
      " |      pandas.core.base.PandasObject\n",
      " |      pandas.core.accessor.DirNamesMixin\n",
      " |      pandas.core.base.NoNewAttributesMixin\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  ceil(self, *args, **kwargs)\n",
      " |      Perform ceil operation on the data to the specified `freq`.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      freq : str or Offset\n",
      " |          The frequency level to ceil the index to. Must be a fixed\n",
      " |          frequency like 'S' (second) not 'ME' (month end). See\n",
      " |          :ref:`frequency aliases <timeseries.offset_aliases>` for\n",
      " |          a list of possible `freq` values.\n",
      " |      ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'\n",
      " |          Only relevant for DatetimeIndex:\n",
      " |      \n",
      " |          - 'infer' will attempt to infer fall dst-transition hours based on\n",
      " |            order\n",
      " |          - bool-ndarray where True signifies a DST time, False designates\n",
      " |            a non-DST time (note that this flag is only applicable for\n",
      " |            ambiguous times)\n",
      " |          - 'NaT' will return NaT where there are ambiguous times\n",
      " |          - 'raise' will raise an AmbiguousTimeError if there are ambiguous\n",
      " |            times.\n",
      " |      \n",
      " |      nonexistent : 'shift_forward', 'shift_backward', 'NaT', timedelta, default 'raise'\n",
      " |          A nonexistent time does not exist in a particular timezone\n",
      " |          where clocks moved forward due to DST.\n",
      " |      \n",
      " |          - 'shift_forward' will shift the nonexistent time forward to the\n",
      " |            closest existing time\n",
      " |          - 'shift_backward' will shift the nonexistent time backward to the\n",
      " |            closest existing time\n",
      " |          - 'NaT' will return NaT where there are nonexistent times\n",
      " |          - timedelta objects will shift nonexistent times by the timedelta\n",
      " |          - 'raise' will raise an NonExistentTimeError if there are\n",
      " |            nonexistent times.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      DatetimeIndex, TimedeltaIndex, or Series\n",
      " |          Index of the same type for a DatetimeIndex or TimedeltaIndex,\n",
      " |          or a Series with the same index for a Series.\n",
      " |      \n",
      " |      Raises\n",
      " |      ------\n",
      " |      ValueError if the `freq` cannot be converted.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      If the timestamps have a timezone, ceiling will take place relative to the\n",
      " |      local (\"wall\") time and re-localized to the same timezone. When ceiling\n",
      " |      near daylight savings time, use ``nonexistent`` and ``ambiguous`` to\n",
      " |      control the re-localization behavior.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      **DatetimeIndex**\n",
      " |      \n",
      " |      >>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min')\n",
      " |      >>> rng\n",
      " |      DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00',\n",
      " |                     '2018-01-01 12:01:00'],\n",
      " |                    dtype='datetime64[ns]', freq='T')\n",
      " |      >>> rng.ceil('H')\n",
      " |      DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',\n",
      " |                     '2018-01-01 13:00:00'],\n",
      " |                    dtype='datetime64[ns]', freq=None)\n",
      " |      \n",
      " |      **Series**\n",
      " |      \n",
      " |      >>> pd.Series(rng).dt.ceil(\"H\")\n",
      " |      0   2018-01-01 12:00:00\n",
      " |      1   2018-01-01 12:00:00\n",
      " |      2   2018-01-01 13:00:00\n",
      " |      dtype: datetime64[ns]\n",
      " |      \n",
      " |      When rounding near a daylight savings time transition, use ``ambiguous`` or\n",
      " |      ``nonexistent`` to control how the timestamp should be re-localized.\n",
      " |      \n",
      " |      >>> rng_tz = pd.DatetimeIndex([\"2021-10-31 01:30:00\"], tz=\"Europe/Amsterdam\")\n",
      " |      \n",
      " |      >>> rng_tz.ceil(\"H\", ambiguous=False)\n",
      " |      DatetimeIndex(['2021-10-31 02:00:00+01:00'],\n",
      " |                    dtype='datetime64[ns, Europe/Amsterdam]', freq=None)\n",
      " |      \n",
      " |      >>> rng_tz.ceil(\"H\", ambiguous=True)\n",
      " |      DatetimeIndex(['2021-10-31 02:00:00+02:00'],\n",
      " |                    dtype='datetime64[ns, Europe/Amsterdam]', freq=None)\n",
      " |  \n",
      " |  day_name(self, *args, **kwargs)\n",
      " |      Return the day names of the DateTimeIndex with specified locale.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      locale : str, optional\n",
      " |          Locale determining the language in which to return the day name.\n",
      " |          Default is English locale.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      Index\n",
      " |          Index of day names.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> idx = pd.date_range(start='2018-01-01', freq='D', periods=3)\n",
      " |      >>> idx\n",
      " |      DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'],\n",
      " |                    dtype='datetime64[ns]', freq='D')\n",
      " |      >>> idx.day_name()\n",
      " |      Index(['Monday', 'Tuesday', 'Wednesday'], dtype='object')\n",
      " |  \n",
      " |  floor(self, *args, **kwargs)\n",
      " |      Perform floor operation on the data to the specified `freq`.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      freq : str or Offset\n",
      " |          The frequency level to floor the index to. Must be a fixed\n",
      " |          frequency like 'S' (second) not 'ME' (month end). See\n",
      " |          :ref:`frequency aliases <timeseries.offset_aliases>` for\n",
      " |          a list of possible `freq` values.\n",
      " |      ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'\n",
      " |          Only relevant for DatetimeIndex:\n",
      " |      \n",
      " |          - 'infer' will attempt to infer fall dst-transition hours based on\n",
      " |            order\n",
      " |          - bool-ndarray where True signifies a DST time, False designates\n",
      " |            a non-DST time (note that this flag is only applicable for\n",
      " |            ambiguous times)\n",
      " |          - 'NaT' will return NaT where there are ambiguous times\n",
      " |          - 'raise' will raise an AmbiguousTimeError if there are ambiguous\n",
      " |            times.\n",
      " |      \n",
      " |      nonexistent : 'shift_forward', 'shift_backward', 'NaT', timedelta, default 'raise'\n",
      " |          A nonexistent time does not exist in a particular timezone\n",
      " |          where clocks moved forward due to DST.\n",
      " |      \n",
      " |          - 'shift_forward' will shift the nonexistent time forward to the\n",
      " |            closest existing time\n",
      " |          - 'shift_backward' will shift the nonexistent time backward to the\n",
      " |            closest existing time\n",
      " |          - 'NaT' will return NaT where there are nonexistent times\n",
      " |          - timedelta objects will shift nonexistent times by the timedelta\n",
      " |          - 'raise' will raise an NonExistentTimeError if there are\n",
      " |            nonexistent times.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      DatetimeIndex, TimedeltaIndex, or Series\n",
      " |          Index of the same type for a DatetimeIndex or TimedeltaIndex,\n",
      " |          or a Series with the same index for a Series.\n",
      " |      \n",
      " |      Raises\n",
      " |      ------\n",
      " |      ValueError if the `freq` cannot be converted.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      If the timestamps have a timezone, flooring will take place relative to the\n",
      " |      local (\"wall\") time and re-localized to the same timezone. When flooring\n",
      " |      near daylight savings time, use ``nonexistent`` and ``ambiguous`` to\n",
      " |      control the re-localization behavior.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      **DatetimeIndex**\n",
      " |      \n",
      " |      >>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min')\n",
      " |      >>> rng\n",
      " |      DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00',\n",
      " |                     '2018-01-01 12:01:00'],\n",
      " |                    dtype='datetime64[ns]', freq='T')\n",
      " |      >>> rng.floor('H')\n",
      " |      DatetimeIndex(['2018-01-01 11:00:00', '2018-01-01 12:00:00',\n",
      " |                     '2018-01-01 12:00:00'],\n",
      " |                    dtype='datetime64[ns]', freq=None)\n",
      " |      \n",
      " |      **Series**\n",
      " |      \n",
      " |      >>> pd.Series(rng).dt.floor(\"H\")\n",
      " |      0   2018-01-01 11:00:00\n",
      " |      1   2018-01-01 12:00:00\n",
      " |      2   2018-01-01 12:00:00\n",
      " |      dtype: datetime64[ns]\n",
      " |      \n",
      " |      When rounding near a daylight savings time transition, use ``ambiguous`` or\n",
      " |      ``nonexistent`` to control how the timestamp should be re-localized.\n",
      " |      \n",
      " |      >>> rng_tz = pd.DatetimeIndex([\"2021-10-31 03:30:00\"], tz=\"Europe/Amsterdam\")\n",
      " |      \n",
      " |      >>> rng_tz.floor(\"2H\", ambiguous=False)\n",
      " |      DatetimeIndex(['2021-10-31 02:00:00+01:00'],\n",
      " |                   dtype='datetime64[ns, Europe/Amsterdam]', freq=None)\n",
      " |      \n",
      " |      >>> rng_tz.floor(\"2H\", ambiguous=True)\n",
      " |      DatetimeIndex(['2021-10-31 02:00:00+02:00'],\n",
      " |                    dtype='datetime64[ns, Europe/Amsterdam]', freq=None)\n",
      " |  \n",
      " |  isocalendar(self)\n",
      " |      Returns a DataFrame with the year, week, and day calculated according to\n",
      " |      the ISO 8601 standard.\n",
      " |      \n",
      " |      .. versionadded:: 1.1.0\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      DataFrame\n",
      " |          with columns year, week and day\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      Timestamp.isocalendar : Function return a 3-tuple containing ISO year,\n",
      " |          week number, and weekday for the given Timestamp object.\n",
      " |      datetime.date.isocalendar : Return a named tuple object with\n",
      " |          three components: year, week and weekday.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> ser = pd.to_datetime(pd.Series([\"2010-01-01\", pd.NaT]))\n",
      " |      >>> ser.dt.isocalendar()\n",
      " |         year  week  day\n",
      " |      0  2009    53     5\n",
      " |      1  <NA>  <NA>  <NA>\n",
      " |      >>> ser.dt.isocalendar().week\n",
      " |      0      53\n",
      " |      1    <NA>\n",
      " |      Name: week, dtype: UInt32\n",
      " |  \n",
      " |  month_name(self, *args, **kwargs)\n",
      " |      Return the month names of the DateTimeIndex with specified locale.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      locale : str, optional\n",
      " |          Locale determining the language in which to return the month name.\n",
      " |          Default is English locale.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      Index\n",
      " |          Index of month names.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> idx = pd.date_range(start='2018-01', freq='M', periods=3)\n",
      " |      >>> idx\n",
      " |      DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31'],\n",
      " |                    dtype='datetime64[ns]', freq='M')\n",
      " |      >>> idx.month_name()\n",
      " |      Index(['January', 'February', 'March'], dtype='object')\n",
      " |  \n",
      " |  normalize(self, *args, **kwargs)\n",
      " |      Convert times to midnight.\n",
      " |      \n",
      " |      The time component of the date-time is converted to midnight i.e.\n",
      " |      00:00:00. This is useful in cases, when the time does not matter.\n",
      " |      Length is unaltered. The timezones are unaffected.\n",
      " |      \n",
      " |      This method is available on Series with datetime values under\n",
      " |      the ``.dt`` accessor, and directly on Datetime Array/Index.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      DatetimeArray, DatetimeIndex or Series\n",
      " |          The same type as the original data. Series will have the same\n",
      " |          name and index. DatetimeIndex will have the same name.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      floor : Floor the datetimes to the specified freq.\n",
      " |      ceil : Ceil the datetimes to the specified freq.\n",
      " |      round : Round the datetimes to the specified freq.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> idx = pd.date_range(start='2014-08-01 10:00', freq='H',\n",
      " |      ...                     periods=3, tz='Asia/Calcutta')\n",
      " |      >>> idx\n",
      " |      DatetimeIndex(['2014-08-01 10:00:00+05:30',\n",
      " |                     '2014-08-01 11:00:00+05:30',\n",
      " |                     '2014-08-01 12:00:00+05:30'],\n",
      " |                      dtype='datetime64[ns, Asia/Calcutta]', freq='H')\n",
      " |      >>> idx.normalize()\n",
      " |      DatetimeIndex(['2014-08-01 00:00:00+05:30',\n",
      " |                     '2014-08-01 00:00:00+05:30',\n",
      " |                     '2014-08-01 00:00:00+05:30'],\n",
      " |                     dtype='datetime64[ns, Asia/Calcutta]', freq=None)\n",
      " |  \n",
      " |  round(self, *args, **kwargs)\n",
      " |      Perform round operation on the data to the specified `freq`.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      freq : str or Offset\n",
      " |          The frequency level to round the index to. Must be a fixed\n",
      " |          frequency like 'S' (second) not 'ME' (month end). See\n",
      " |          :ref:`frequency aliases <timeseries.offset_aliases>` for\n",
      " |          a list of possible `freq` values.\n",
      " |      ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'\n",
      " |          Only relevant for DatetimeIndex:\n",
      " |      \n",
      " |          - 'infer' will attempt to infer fall dst-transition hours based on\n",
      " |            order\n",
      " |          - bool-ndarray where True signifies a DST time, False designates\n",
      " |            a non-DST time (note that this flag is only applicable for\n",
      " |            ambiguous times)\n",
      " |          - 'NaT' will return NaT where there are ambiguous times\n",
      " |          - 'raise' will raise an AmbiguousTimeError if there are ambiguous\n",
      " |            times.\n",
      " |      \n",
      " |      nonexistent : 'shift_forward', 'shift_backward', 'NaT', timedelta, default 'raise'\n",
      " |          A nonexistent time does not exist in a particular timezone\n",
      " |          where clocks moved forward due to DST.\n",
      " |      \n",
      " |          - 'shift_forward' will shift the nonexistent time forward to the\n",
      " |            closest existing time\n",
      " |          - 'shift_backward' will shift the nonexistent time backward to the\n",
      " |            closest existing time\n",
      " |          - 'NaT' will return NaT where there are nonexistent times\n",
      " |          - timedelta objects will shift nonexistent times by the timedelta\n",
      " |          - 'raise' will raise an NonExistentTimeError if there are\n",
      " |            nonexistent times.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      DatetimeIndex, TimedeltaIndex, or Series\n",
      " |          Index of the same type for a DatetimeIndex or TimedeltaIndex,\n",
      " |          or a Series with the same index for a Series.\n",
      " |      \n",
      " |      Raises\n",
      " |      ------\n",
      " |      ValueError if the `freq` cannot be converted.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      If the timestamps have a timezone, rounding will take place relative to the\n",
      " |      local (\"wall\") time and re-localized to the same timezone. When rounding\n",
      " |      near daylight savings time, use ``nonexistent`` and ``ambiguous`` to\n",
      " |      control the re-localization behavior.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      **DatetimeIndex**\n",
      " |      \n",
      " |      >>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min')\n",
      " |      >>> rng\n",
      " |      DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00',\n",
      " |                     '2018-01-01 12:01:00'],\n",
      " |                    dtype='datetime64[ns]', freq='T')\n",
      " |      >>> rng.round('H')\n",
      " |      DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',\n",
      " |                     '2018-01-01 12:00:00'],\n",
      " |                    dtype='datetime64[ns]', freq=None)\n",
      " |      \n",
      " |      **Series**\n",
      " |      \n",
      " |      >>> pd.Series(rng).dt.round(\"H\")\n",
      " |      0   2018-01-01 12:00:00\n",
      " |      1   2018-01-01 12:00:00\n",
      " |      2   2018-01-01 12:00:00\n",
      " |      dtype: datetime64[ns]\n",
      " |      \n",
      " |      When rounding near a daylight savings time transition, use ``ambiguous`` or\n",
      " |      ``nonexistent`` to control how the timestamp should be re-localized.\n",
      " |      \n",
      " |      >>> rng_tz = pd.DatetimeIndex([\"2021-10-31 03:30:00\"], tz=\"Europe/Amsterdam\")\n",
      " |      \n",
      " |      >>> rng_tz.floor(\"2H\", ambiguous=False)\n",
      " |      DatetimeIndex(['2021-10-31 02:00:00+01:00'],\n",
      " |                    dtype='datetime64[ns, Europe/Amsterdam]', freq=None)\n",
      " |      \n",
      " |      >>> rng_tz.floor(\"2H\", ambiguous=True)\n",
      " |      DatetimeIndex(['2021-10-31 02:00:00+02:00'],\n",
      " |                    dtype='datetime64[ns, Europe/Amsterdam]', freq=None)\n",
      " |  \n",
      " |  strftime(self, *args, **kwargs)\n",
      " |      Convert to Index using specified date_format.\n",
      " |      \n",
      " |      Return an Index of formatted strings specified by date_format, which\n",
      " |      supports the same string format as the python standard library. Details\n",
      " |      of the string format can be found in `python string format\n",
      " |      doc <https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior>`__.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      date_format : str\n",
      " |          Date format string (e.g. \"%Y-%m-%d\").\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      ndarray[object]\n",
      " |          NumPy ndarray of formatted strings.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      to_datetime : Convert the given argument to datetime.\n",
      " |      DatetimeIndex.normalize : Return DatetimeIndex with times to midnight.\n",
      " |      DatetimeIndex.round : Round the DatetimeIndex to the specified freq.\n",
      " |      DatetimeIndex.floor : Floor the DatetimeIndex to the specified freq.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> rng = pd.date_range(pd.Timestamp(\"2018-03-10 09:00\"),\n",
      " |      ...                     periods=3, freq='s')\n",
      " |      >>> rng.strftime('%B %d, %Y, %r')\n",
      " |      Index(['March 10, 2018, 09:00:00 AM', 'March 10, 2018, 09:00:01 AM',\n",
      " |             'March 10, 2018, 09:00:02 AM'],\n",
      " |            dtype='object')\n",
      " |  \n",
      " |  to_period(self, *args, **kwargs)\n",
      " |      Cast to PeriodArray/Index at a particular frequency.\n",
      " |      \n",
      " |      Converts DatetimeArray/Index to PeriodArray/Index.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      freq : str or Offset, optional\n",
      " |          One of pandas' :ref:`offset strings <timeseries.offset_aliases>`\n",
      " |          or an Offset object. Will be inferred by default.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      PeriodArray/Index\n",
      " |      \n",
      " |      Raises\n",
      " |      ------\n",
      " |      ValueError\n",
      " |          When converting a DatetimeArray/Index with non-regular values,\n",
      " |          so that a frequency cannot be inferred.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      PeriodIndex: Immutable ndarray holding ordinal values.\n",
      " |      DatetimeIndex.to_pydatetime: Return DatetimeIndex as object.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> df = pd.DataFrame({\"y\": [1, 2, 3]},\n",
      " |      ...                   index=pd.to_datetime([\"2000-03-31 00:00:00\",\n",
      " |      ...                                         \"2000-05-31 00:00:00\",\n",
      " |      ...                                         \"2000-08-31 00:00:00\"]))\n",
      " |      >>> df.index.to_period(\"M\")\n",
      " |      PeriodIndex(['2000-03', '2000-05', '2000-08'],\n",
      " |                  dtype='period[M]')\n",
      " |      \n",
      " |      Infer the daily frequency\n",
      " |      \n",
      " |      >>> idx = pd.date_range(\"2017-01-01\", periods=2)\n",
      " |      >>> idx.to_period()\n",
      " |      PeriodIndex(['2017-01-01', '2017-01-02'],\n",
      " |                  dtype='period[D]')\n",
      " |  \n",
      " |  to_pydatetime(self) -> 'np.ndarray'\n",
      " |      Return the data as an array of :class:`datetime.datetime` objects.\n",
      " |      \n",
      " |      Timezone information is retained if present.\n",
      " |      \n",
      " |      .. warning::\n",
      " |      \n",
      " |         Python's datetime uses microsecond resolution, which is lower than\n",
      " |         pandas (nanosecond). The values are truncated.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      numpy.ndarray\n",
      " |          Object dtype array containing native Python datetime objects.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      datetime.datetime : Standard library value for a datetime.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> s = pd.Series(pd.date_range('20180310', periods=2))\n",
      " |      >>> s\n",
      " |      0   2018-03-10\n",
      " |      1   2018-03-11\n",
      " |      dtype: datetime64[ns]\n",
      " |      \n",
      " |      >>> s.dt.to_pydatetime()\n",
      " |      array([datetime.datetime(2018, 3, 10, 0, 0),\n",
      " |             datetime.datetime(2018, 3, 11, 0, 0)], dtype=object)\n",
      " |      \n",
      " |      pandas' nanosecond precision is truncated to microseconds.\n",
      " |      \n",
      " |      >>> s = pd.Series(pd.date_range('20180310', periods=2, freq='ns'))\n",
      " |      >>> s\n",
      " |      0   2018-03-10 00:00:00.000000000\n",
      " |      1   2018-03-10 00:00:00.000000001\n",
      " |      dtype: datetime64[ns]\n",
      " |      \n",
      " |      >>> s.dt.to_pydatetime()\n",
      " |      array([datetime.datetime(2018, 3, 10, 0, 0),\n",
      " |             datetime.datetime(2018, 3, 10, 0, 0)], dtype=object)\n",
      " |  \n",
      " |  tz_convert(self, *args, **kwargs)\n",
      " |      Convert tz-aware Datetime Array/Index from one time zone to another.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      tz : str, pytz.timezone, dateutil.tz.tzfile or None\n",
      " |          Time zone for time. Corresponding timestamps would be converted\n",
      " |          to this time zone of the Datetime Array/Index. A `tz` of None will\n",
      " |          convert to UTC and remove the timezone information.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      Array or Index\n",
      " |      \n",
      " |      Raises\n",
      " |      ------\n",
      " |      TypeError\n",
      " |          If Datetime Array/Index is tz-naive.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      DatetimeIndex.tz : A timezone that has a variable offset from UTC.\n",
      " |      DatetimeIndex.tz_localize : Localize tz-naive DatetimeIndex to a\n",
      " |          given time zone, or remove timezone from a tz-aware DatetimeIndex.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      With the `tz` parameter, we can change the DatetimeIndex\n",
      " |      to other time zones:\n",
      " |      \n",
      " |      >>> dti = pd.date_range(start='2014-08-01 09:00',\n",
      " |      ...                     freq='H', periods=3, tz='Europe/Berlin')\n",
      " |      \n",
      " |      >>> dti\n",
      " |      DatetimeIndex(['2014-08-01 09:00:00+02:00',\n",
      " |                     '2014-08-01 10:00:00+02:00',\n",
      " |                     '2014-08-01 11:00:00+02:00'],\n",
      " |                    dtype='datetime64[ns, Europe/Berlin]', freq='H')\n",
      " |      \n",
      " |      >>> dti.tz_convert('US/Central')\n",
      " |      DatetimeIndex(['2014-08-01 02:00:00-05:00',\n",
      " |                     '2014-08-01 03:00:00-05:00',\n",
      " |                     '2014-08-01 04:00:00-05:00'],\n",
      " |                    dtype='datetime64[ns, US/Central]', freq='H')\n",
      " |      \n",
      " |      With the ``tz=None``, we can remove the timezone (after converting\n",
      " |      to UTC if necessary):\n",
      " |      \n",
      " |      >>> dti = pd.date_range(start='2014-08-01 09:00', freq='H',\n",
      " |      ...                     periods=3, tz='Europe/Berlin')\n",
      " |      \n",
      " |      >>> dti\n",
      " |      DatetimeIndex(['2014-08-01 09:00:00+02:00',\n",
      " |                     '2014-08-01 10:00:00+02:00',\n",
      " |                     '2014-08-01 11:00:00+02:00'],\n",
      " |                      dtype='datetime64[ns, Europe/Berlin]', freq='H')\n",
      " |      \n",
      " |      >>> dti.tz_convert(None)\n",
      " |      DatetimeIndex(['2014-08-01 07:00:00',\n",
      " |                     '2014-08-01 08:00:00',\n",
      " |                     '2014-08-01 09:00:00'],\n",
      " |                      dtype='datetime64[ns]', freq='H')\n",
      " |  \n",
      " |  tz_localize(self, *args, **kwargs)\n",
      " |      Localize tz-naive Datetime Array/Index to tz-aware\n",
      " |      Datetime Array/Index.\n",
      " |      \n",
      " |      This method takes a time zone (tz) naive Datetime Array/Index object\n",
      " |      and makes this time zone aware. It does not move the time to another\n",
      " |      time zone.\n",
      " |      \n",
      " |      This method can also be used to do the inverse -- to create a time\n",
      " |      zone unaware object from an aware object. To that end, pass `tz=None`.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      tz : str, pytz.timezone, dateutil.tz.tzfile or None\n",
      " |          Time zone to convert timestamps to. Passing ``None`` will\n",
      " |          remove the time zone information preserving local time.\n",
      " |      ambiguous : 'infer', 'NaT', bool array, default 'raise'\n",
      " |          When clocks moved backward due to DST, ambiguous times may arise.\n",
      " |          For example in Central European Time (UTC+01), when going from\n",
      " |          03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at\n",
      " |          00:30:00 UTC and at 01:30:00 UTC. In such a situation, the\n",
      " |          `ambiguous` parameter dictates how ambiguous times should be\n",
      " |          handled.\n",
      " |      \n",
      " |          - 'infer' will attempt to infer fall dst-transition hours based on\n",
      " |            order\n",
      " |          - bool-ndarray where True signifies a DST time, False signifies a\n",
      " |            non-DST time (note that this flag is only applicable for\n",
      " |            ambiguous times)\n",
      " |          - 'NaT' will return NaT where there are ambiguous times\n",
      " |          - 'raise' will raise an AmbiguousTimeError if there are ambiguous\n",
      " |            times.\n",
      " |      \n",
      " |      nonexistent : 'shift_forward', 'shift_backward, 'NaT', timedelta, default 'raise'\n",
      " |          A nonexistent time does not exist in a particular timezone\n",
      " |          where clocks moved forward due to DST.\n",
      " |      \n",
      " |          - 'shift_forward' will shift the nonexistent time forward to the\n",
      " |            closest existing time\n",
      " |          - 'shift_backward' will shift the nonexistent time backward to the\n",
      " |            closest existing time\n",
      " |          - 'NaT' will return NaT where there are nonexistent times\n",
      " |          - timedelta objects will shift nonexistent times by the timedelta\n",
      " |          - 'raise' will raise an NonExistentTimeError if there are\n",
      " |            nonexistent times.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      Same type as self\n",
      " |          Array/Index converted to the specified time zone.\n",
      " |      \n",
      " |      Raises\n",
      " |      ------\n",
      " |      TypeError\n",
      " |          If the Datetime Array/Index is tz-aware and tz is not None.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      DatetimeIndex.tz_convert : Convert tz-aware DatetimeIndex from\n",
      " |          one time zone to another.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> tz_naive = pd.date_range('2018-03-01 09:00', periods=3)\n",
      " |      >>> tz_naive\n",
      " |      DatetimeIndex(['2018-03-01 09:00:00', '2018-03-02 09:00:00',\n",
      " |                     '2018-03-03 09:00:00'],\n",
      " |                    dtype='datetime64[ns]', freq='D')\n",
      " |      \n",
      " |      Localize DatetimeIndex in US/Eastern time zone:\n",
      " |      \n",
      " |      >>> tz_aware = tz_naive.tz_localize(tz='US/Eastern')\n",
      " |      >>> tz_aware\n",
      " |      DatetimeIndex(['2018-03-01 09:00:00-05:00',\n",
      " |                     '2018-03-02 09:00:00-05:00',\n",
      " |                     '2018-03-03 09:00:00-05:00'],\n",
      " |                    dtype='datetime64[ns, US/Eastern]', freq=None)\n",
      " |      \n",
      " |      With the ``tz=None``, we can remove the time zone information\n",
      " |      while keeping the local time (not converted to UTC):\n",
      " |      \n",
      " |      >>> tz_aware.tz_localize(None)\n",
      " |      DatetimeIndex(['2018-03-01 09:00:00', '2018-03-02 09:00:00',\n",
      " |                     '2018-03-03 09:00:00'],\n",
      " |                    dtype='datetime64[ns]', freq=None)\n",
      " |      \n",
      " |      Be careful with DST changes. When there is sequential data, pandas can\n",
      " |      infer the DST time:\n",
      " |      \n",
      " |      >>> s = pd.to_datetime(pd.Series(['2018-10-28 01:30:00',\n",
      " |      ...                               '2018-10-28 02:00:00',\n",
      " |      ...                               '2018-10-28 02:30:00',\n",
      " |      ...                               '2018-10-28 02:00:00',\n",
      " |      ...                               '2018-10-28 02:30:00',\n",
      " |      ...                               '2018-10-28 03:00:00',\n",
      " |      ...                               '2018-10-28 03:30:00']))\n",
      " |      >>> s.dt.tz_localize('CET', ambiguous='infer')\n",
      " |      0   2018-10-28 01:30:00+02:00\n",
      " |      1   2018-10-28 02:00:00+02:00\n",
      " |      2   2018-10-28 02:30:00+02:00\n",
      " |      3   2018-10-28 02:00:00+01:00\n",
      " |      4   2018-10-28 02:30:00+01:00\n",
      " |      5   2018-10-28 03:00:00+01:00\n",
      " |      6   2018-10-28 03:30:00+01:00\n",
      " |      dtype: datetime64[ns, CET]\n",
      " |      \n",
      " |      In some cases, inferring the DST is impossible. In such cases, you can\n",
      " |      pass an ndarray to the ambiguous parameter to set the DST explicitly\n",
      " |      \n",
      " |      >>> s = pd.to_datetime(pd.Series(['2018-10-28 01:20:00',\n",
      " |      ...                               '2018-10-28 02:36:00',\n",
      " |      ...                               '2018-10-28 03:46:00']))\n",
      " |      >>> s.dt.tz_localize('CET', ambiguous=np.array([True, True, False]))\n",
      " |      0   2018-10-28 01:20:00+02:00\n",
      " |      1   2018-10-28 02:36:00+02:00\n",
      " |      2   2018-10-28 03:46:00+01:00\n",
      " |      dtype: datetime64[ns, CET]\n",
      " |      \n",
      " |      If the DST transition causes nonexistent times, you can shift these\n",
      " |      dates forward or backwards with a timedelta object or `'shift_forward'`\n",
      " |      or `'shift_backwards'`.\n",
      " |      \n",
      " |      >>> s = pd.to_datetime(pd.Series(['2015-03-29 02:30:00',\n",
      " |      ...                               '2015-03-29 03:30:00']))\n",
      " |      >>> s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_forward')\n",
      " |      0   2015-03-29 03:00:00+02:00\n",
      " |      1   2015-03-29 03:30:00+02:00\n",
      " |      dtype: datetime64[ns, Europe/Warsaw]\n",
      " |      \n",
      " |      >>> s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_backward')\n",
      " |      0   2015-03-29 01:59:59.999999999+01:00\n",
      " |      1   2015-03-29 03:30:00+02:00\n",
      " |      dtype: datetime64[ns, Europe/Warsaw]\n",
      " |      \n",
      " |      >>> s.dt.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1H'))\n",
      " |      0   2015-03-29 03:30:00+02:00\n",
      " |      1   2015-03-29 03:30:00+02:00\n",
      " |      dtype: datetime64[ns, Europe/Warsaw]\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Readonly properties defined here:\n",
      " |  \n",
      " |  freq\n",
      " |  \n",
      " |  week\n",
      " |      The week ordinal of the year.\n",
      " |      \n",
      " |      .. deprecated:: 1.1.0\n",
      " |      \n",
      " |      Series.dt.weekofyear and Series.dt.week have been deprecated.\n",
      " |      Please use Series.dt.isocalendar().week instead.\n",
      " |  \n",
      " |  weekofyear\n",
      " |      The week ordinal of the year.\n",
      " |      \n",
      " |      .. deprecated:: 1.1.0\n",
      " |      \n",
      " |      Series.dt.weekofyear and Series.dt.week have been deprecated.\n",
      " |      Please use Series.dt.isocalendar().week instead.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  date\n",
      " |      Returns numpy array of python :class:`datetime.date` objects.\n",
      " |      \n",
      " |      Namely, the date part of Timestamps without time and\n",
      " |      timezone information.\n",
      " |  \n",
      " |  day\n",
      " |      The day of the datetime.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> datetime_series = pd.Series(\n",
      " |      ...     pd.date_range(\"2000-01-01\", periods=3, freq=\"D\")\n",
      " |      ... )\n",
      " |      >>> datetime_series\n",
      " |      0   2000-01-01\n",
      " |      1   2000-01-02\n",
      " |      2   2000-01-03\n",
      " |      dtype: datetime64[ns]\n",
      " |      >>> datetime_series.dt.day\n",
      " |      0    1\n",
      " |      1    2\n",
      " |      2    3\n",
      " |      dtype: int64\n",
      " |  \n",
      " |  day_of_week\n",
      " |      The day of the week with Monday=0, Sunday=6.\n",
      " |      \n",
      " |      Return the day of the week. It is assumed the week starts on\n",
      " |      Monday, which is denoted by 0 and ends on Sunday which is denoted\n",
      " |      by 6. This method is available on both Series with datetime\n",
      " |      values (using the `dt` accessor) or DatetimeIndex.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      Series or Index\n",
      " |          Containing integers indicating the day number.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      Series.dt.dayofweek : Alias.\n",
      " |      Series.dt.weekday : Alias.\n",
      " |      Series.dt.day_name : Returns the name of the day of the week.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> s = pd.date_range('2016-12-31', '2017-01-08', freq='D').to_series()\n",
      " |      >>> s.dt.dayofweek\n",
      " |      2016-12-31    5\n",
      " |      2017-01-01    6\n",
      " |      2017-01-02    0\n",
      " |      2017-01-03    1\n",
      " |      2017-01-04    2\n",
      " |      2017-01-05    3\n",
      " |      2017-01-06    4\n",
      " |      2017-01-07    5\n",
      " |      2017-01-08    6\n",
      " |      Freq: D, dtype: int64\n",
      " |  \n",
      " |  day_of_year\n",
      " |      The ordinal day of the year.\n",
      " |  \n",
      " |  dayofweek\n",
      " |      The day of the week with Monday=0, Sunday=6.\n",
      " |      \n",
      " |      Return the day of the week. It is assumed the week starts on\n",
      " |      Monday, which is denoted by 0 and ends on Sunday which is denoted\n",
      " |      by 6. This method is available on both Series with datetime\n",
      " |      values (using the `dt` accessor) or DatetimeIndex.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      Series or Index\n",
      " |          Containing integers indicating the day number.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      Series.dt.dayofweek : Alias.\n",
      " |      Series.dt.weekday : Alias.\n",
      " |      Series.dt.day_name : Returns the name of the day of the week.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> s = pd.date_range('2016-12-31', '2017-01-08', freq='D').to_series()\n",
      " |      >>> s.dt.dayofweek\n",
      " |      2016-12-31    5\n",
      " |      2017-01-01    6\n",
      " |      2017-01-02    0\n",
      " |      2017-01-03    1\n",
      " |      2017-01-04    2\n",
      " |      2017-01-05    3\n",
      " |      2017-01-06    4\n",
      " |      2017-01-07    5\n",
      " |      2017-01-08    6\n",
      " |      Freq: D, dtype: int64\n",
      " |  \n",
      " |  dayofyear\n",
      " |      The ordinal day of the year.\n",
      " |  \n",
      " |  days_in_month\n",
      " |      The number of days in the month.\n",
      " |  \n",
      " |  daysinmonth\n",
      " |      The number of days in the month.\n",
      " |  \n",
      " |  hour\n",
      " |      The hours of the datetime.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> datetime_series = pd.Series(\n",
      " |      ...     pd.date_range(\"2000-01-01\", periods=3, freq=\"h\")\n",
      " |      ... )\n",
      " |      >>> datetime_series\n",
      " |      0   2000-01-01 00:00:00\n",
      " |      1   2000-01-01 01:00:00\n",
      " |      2   2000-01-01 02:00:00\n",
      " |      dtype: datetime64[ns]\n",
      " |      >>> datetime_series.dt.hour\n",
      " |      0    0\n",
      " |      1    1\n",
      " |      2    2\n",
      " |      dtype: int64\n",
      " |  \n",
      " |  is_leap_year\n",
      " |      Boolean indicator if the date belongs to a leap year.\n",
      " |      \n",
      " |      A leap year is a year, which has 366 days (instead of 365) including\n",
      " |      29th of February as an intercalary day.\n",
      " |      Leap years are years which are multiples of four with the exception\n",
      " |      of years divisible by 100 but not by 400.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      Series or ndarray\n",
      " |           Booleans indicating if dates belong to a leap year.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      This method is available on Series with datetime values under\n",
      " |      the ``.dt`` accessor, and directly on DatetimeIndex.\n",
      " |      \n",
      " |      >>> idx = pd.date_range(\"2012-01-01\", \"2015-01-01\", freq=\"Y\")\n",
      " |      >>> idx\n",
      " |      DatetimeIndex(['2012-12-31', '2013-12-31', '2014-12-31'],\n",
      " |                    dtype='datetime64[ns]', freq='A-DEC')\n",
      " |      >>> idx.is_leap_year\n",
      " |      array([ True, False, False])\n",
      " |      \n",
      " |      >>> dates_series = pd.Series(idx)\n",
      " |      >>> dates_series\n",
      " |      0   2012-12-31\n",
      " |      1   2013-12-31\n",
      " |      2   2014-12-31\n",
      " |      dtype: datetime64[ns]\n",
      " |      >>> dates_series.dt.is_leap_year\n",
      " |      0     True\n",
      " |      1    False\n",
      " |      2    False\n",
      " |      dtype: bool\n",
      " |  \n",
      " |  is_month_end\n",
      " |      Indicates whether the date is the last day of the month.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      Series or array\n",
      " |          For Series, returns a Series with boolean values.\n",
      " |          For DatetimeIndex, returns a boolean array.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      is_month_start : Return a boolean indicating whether the date\n",
      " |          is the first day of the month.\n",
      " |      is_month_end : Return a boolean indicating whether the date\n",
      " |          is the last day of the month.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      This method is available on Series with datetime values under\n",
      " |      the ``.dt`` accessor, and directly on DatetimeIndex.\n",
      " |      \n",
      " |      >>> s = pd.Series(pd.date_range(\"2018-02-27\", periods=3))\n",
      " |      >>> s\n",
      " |      0   2018-02-27\n",
      " |      1   2018-02-28\n",
      " |      2   2018-03-01\n",
      " |      dtype: datetime64[ns]\n",
      " |      >>> s.dt.is_month_start\n",
      " |      0    False\n",
      " |      1    False\n",
      " |      2    True\n",
      " |      dtype: bool\n",
      " |      >>> s.dt.is_month_end\n",
      " |      0    False\n",
      " |      1    True\n",
      " |      2    False\n",
      " |      dtype: bool\n",
      " |      \n",
      " |      >>> idx = pd.date_range(\"2018-02-27\", periods=3)\n",
      " |      >>> idx.is_month_start\n",
      " |      array([False, False, True])\n",
      " |      >>> idx.is_month_end\n",
      " |      array([False, True, False])\n",
      " |  \n",
      " |  is_month_start\n",
      " |      Indicates whether the date is the first day of the month.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      Series or array\n",
      " |          For Series, returns a Series with boolean values.\n",
      " |          For DatetimeIndex, returns a boolean array.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      is_month_start : Return a boolean indicating whether the date\n",
      " |          is the first day of the month.\n",
      " |      is_month_end : Return a boolean indicating whether the date\n",
      " |          is the last day of the month.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      This method is available on Series with datetime values under\n",
      " |      the ``.dt`` accessor, and directly on DatetimeIndex.\n",
      " |      \n",
      " |      >>> s = pd.Series(pd.date_range(\"2018-02-27\", periods=3))\n",
      " |      >>> s\n",
      " |      0   2018-02-27\n",
      " |      1   2018-02-28\n",
      " |      2   2018-03-01\n",
      " |      dtype: datetime64[ns]\n",
      " |      >>> s.dt.is_month_start\n",
      " |      0    False\n",
      " |      1    False\n",
      " |      2    True\n",
      " |      dtype: bool\n",
      " |      >>> s.dt.is_month_end\n",
      " |      0    False\n",
      " |      1    True\n",
      " |      2    False\n",
      " |      dtype: bool\n",
      " |      \n",
      " |      >>> idx = pd.date_range(\"2018-02-27\", periods=3)\n",
      " |      >>> idx.is_month_start\n",
      " |      array([False, False, True])\n",
      " |      >>> idx.is_month_end\n",
      " |      array([False, True, False])\n",
      " |  \n",
      " |  is_quarter_end\n",
      " |      Indicator for whether the date is the last day of a quarter.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      is_quarter_end : Series or DatetimeIndex\n",
      " |          The same type as the original data with boolean values. Series will\n",
      " |          have the same name and index. DatetimeIndex will have the same\n",
      " |          name.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      quarter : Return the quarter of the date.\n",
      " |      is_quarter_start : Similar property indicating the quarter start.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      This method is available on Series with datetime values under\n",
      " |      the ``.dt`` accessor, and directly on DatetimeIndex.\n",
      " |      \n",
      " |      >>> df = pd.DataFrame({'dates': pd.date_range(\"2017-03-30\",\n",
      " |      ...                    periods=4)})\n",
      " |      >>> df.assign(quarter=df.dates.dt.quarter,\n",
      " |      ...           is_quarter_end=df.dates.dt.is_quarter_end)\n",
      " |             dates  quarter    is_quarter_end\n",
      " |      0 2017-03-30        1             False\n",
      " |      1 2017-03-31        1              True\n",
      " |      2 2017-04-01        2             False\n",
      " |      3 2017-04-02        2             False\n",
      " |      \n",
      " |      >>> idx = pd.date_range('2017-03-30', periods=4)\n",
      " |      >>> idx\n",
      " |      DatetimeIndex(['2017-03-30', '2017-03-31', '2017-04-01', '2017-04-02'],\n",
      " |                    dtype='datetime64[ns]', freq='D')\n",
      " |      \n",
      " |      >>> idx.is_quarter_end\n",
      " |      array([False,  True, False, False])\n",
      " |  \n",
      " |  is_quarter_start\n",
      " |      Indicator for whether the date is the first day of a quarter.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      is_quarter_start : Series or DatetimeIndex\n",
      " |          The same type as the original data with boolean values. Series will\n",
      " |          have the same name and index. DatetimeIndex will have the same\n",
      " |          name.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      quarter : Return the quarter of the date.\n",
      " |      is_quarter_end : Similar property for indicating the quarter start.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      This method is available on Series with datetime values under\n",
      " |      the ``.dt`` accessor, and directly on DatetimeIndex.\n",
      " |      \n",
      " |      >>> df = pd.DataFrame({'dates': pd.date_range(\"2017-03-30\",\n",
      " |      ...                   periods=4)})\n",
      " |      >>> df.assign(quarter=df.dates.dt.quarter,\n",
      " |      ...           is_quarter_start=df.dates.dt.is_quarter_start)\n",
      " |             dates  quarter  is_quarter_start\n",
      " |      0 2017-03-30        1             False\n",
      " |      1 2017-03-31        1             False\n",
      " |      2 2017-04-01        2              True\n",
      " |      3 2017-04-02        2             False\n",
      " |      \n",
      " |      >>> idx = pd.date_range('2017-03-30', periods=4)\n",
      " |      >>> idx\n",
      " |      DatetimeIndex(['2017-03-30', '2017-03-31', '2017-04-01', '2017-04-02'],\n",
      " |                    dtype='datetime64[ns]', freq='D')\n",
      " |      \n",
      " |      >>> idx.is_quarter_start\n",
      " |      array([False, False,  True, False])\n",
      " |  \n",
      " |  is_year_end\n",
      " |      Indicate whether the date is the last day of the year.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      Series or DatetimeIndex\n",
      " |          The same type as the original data with boolean values. Series will\n",
      " |          have the same name and index. DatetimeIndex will have the same\n",
      " |          name.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      is_year_start : Similar property indicating the start of the year.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      This method is available on Series with datetime values under\n",
      " |      the ``.dt`` accessor, and directly on DatetimeIndex.\n",
      " |      \n",
      " |      >>> dates = pd.Series(pd.date_range(\"2017-12-30\", periods=3))\n",
      " |      >>> dates\n",
      " |      0   2017-12-30\n",
      " |      1   2017-12-31\n",
      " |      2   2018-01-01\n",
      " |      dtype: datetime64[ns]\n",
      " |      \n",
      " |      >>> dates.dt.is_year_end\n",
      " |      0    False\n",
      " |      1     True\n",
      " |      2    False\n",
      " |      dtype: bool\n",
      " |      \n",
      " |      >>> idx = pd.date_range(\"2017-12-30\", periods=3)\n",
      " |      >>> idx\n",
      " |      DatetimeIndex(['2017-12-30', '2017-12-31', '2018-01-01'],\n",
      " |                    dtype='datetime64[ns]', freq='D')\n",
      " |      \n",
      " |      >>> idx.is_year_end\n",
      " |      array([False,  True, False])\n",
      " |  \n",
      " |  is_year_start\n",
      " |      Indicate whether the date is the first day of a year.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      Series or DatetimeIndex\n",
      " |          The same type as the original data with boolean values. Series will\n",
      " |          have the same name and index. DatetimeIndex will have the same\n",
      " |          name.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      is_year_end : Similar property indicating the last day of the year.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      This method is available on Series with datetime values under\n",
      " |      the ``.dt`` accessor, and directly on DatetimeIndex.\n",
      " |      \n",
      " |      >>> dates = pd.Series(pd.date_range(\"2017-12-30\", periods=3))\n",
      " |      >>> dates\n",
      " |      0   2017-12-30\n",
      " |      1   2017-12-31\n",
      " |      2   2018-01-01\n",
      " |      dtype: datetime64[ns]\n",
      " |      \n",
      " |      >>> dates.dt.is_year_start\n",
      " |      0    False\n",
      " |      1    False\n",
      " |      2    True\n",
      " |      dtype: bool\n",
      " |      \n",
      " |      >>> idx = pd.date_range(\"2017-12-30\", periods=3)\n",
      " |      >>> idx\n",
      " |      DatetimeIndex(['2017-12-30', '2017-12-31', '2018-01-01'],\n",
      " |                    dtype='datetime64[ns]', freq='D')\n",
      " |      \n",
      " |      >>> idx.is_year_start\n",
      " |      array([False, False,  True])\n",
      " |  \n",
      " |  microsecond\n",
      " |      The microseconds of the datetime.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> datetime_series = pd.Series(\n",
      " |      ...     pd.date_range(\"2000-01-01\", periods=3, freq=\"us\")\n",
      " |      ... )\n",
      " |      >>> datetime_series\n",
      " |      0   2000-01-01 00:00:00.000000\n",
      " |      1   2000-01-01 00:00:00.000001\n",
      " |      2   2000-01-01 00:00:00.000002\n",
      " |      dtype: datetime64[ns]\n",
      " |      >>> datetime_series.dt.microsecond\n",
      " |      0       0\n",
      " |      1       1\n",
      " |      2       2\n",
      " |      dtype: int64\n",
      " |  \n",
      " |  minute\n",
      " |      The minutes of the datetime.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> datetime_series = pd.Series(\n",
      " |      ...     pd.date_range(\"2000-01-01\", periods=3, freq=\"T\")\n",
      " |      ... )\n",
      " |      >>> datetime_series\n",
      " |      0   2000-01-01 00:00:00\n",
      " |      1   2000-01-01 00:01:00\n",
      " |      2   2000-01-01 00:02:00\n",
      " |      dtype: datetime64[ns]\n",
      " |      >>> datetime_series.dt.minute\n",
      " |      0    0\n",
      " |      1    1\n",
      " |      2    2\n",
      " |      dtype: int64\n",
      " |  \n",
      " |  month\n",
      " |      The month as January=1, December=12.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> datetime_series = pd.Series(\n",
      " |      ...     pd.date_range(\"2000-01-01\", periods=3, freq=\"M\")\n",
      " |      ... )\n",
      " |      >>> datetime_series\n",
      " |      0   2000-01-31\n",
      " |      1   2000-02-29\n",
      " |      2   2000-03-31\n",
      " |      dtype: datetime64[ns]\n",
      " |      >>> datetime_series.dt.month\n",
      " |      0    1\n",
      " |      1    2\n",
      " |      2    3\n",
      " |      dtype: int64\n",
      " |  \n",
      " |  nanosecond\n",
      " |      The nanoseconds of the datetime.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> datetime_series = pd.Series(\n",
      " |      ...     pd.date_range(\"2000-01-01\", periods=3, freq=\"ns\")\n",
      " |      ... )\n",
      " |      >>> datetime_series\n",
      " |      0   2000-01-01 00:00:00.000000000\n",
      " |      1   2000-01-01 00:00:00.000000001\n",
      " |      2   2000-01-01 00:00:00.000000002\n",
      " |      dtype: datetime64[ns]\n",
      " |      >>> datetime_series.dt.nanosecond\n",
      " |      0       0\n",
      " |      1       1\n",
      " |      2       2\n",
      " |      dtype: int64\n",
      " |  \n",
      " |  quarter\n",
      " |      The quarter of the date.\n",
      " |  \n",
      " |  second\n",
      " |      The seconds of the datetime.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> datetime_series = pd.Series(\n",
      " |      ...     pd.date_range(\"2000-01-01\", periods=3, freq=\"s\")\n",
      " |      ... )\n",
      " |      >>> datetime_series\n",
      " |      0   2000-01-01 00:00:00\n",
      " |      1   2000-01-01 00:00:01\n",
      " |      2   2000-01-01 00:00:02\n",
      " |      dtype: datetime64[ns]\n",
      " |      >>> datetime_series.dt.second\n",
      " |      0    0\n",
      " |      1    1\n",
      " |      2    2\n",
      " |      dtype: int64\n",
      " |  \n",
      " |  time\n",
      " |      Returns numpy array of :class:`datetime.time` objects.\n",
      " |      \n",
      " |      The time part of the Timestamps.\n",
      " |  \n",
      " |  timetz\n",
      " |      Returns numpy array of :class:`datetime.time` objects with timezone\n",
      " |      information.\n",
      " |      \n",
      " |      The time part of the Timestamps.\n",
      " |  \n",
      " |  tz\n",
      " |      Return the timezone.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      datetime.tzinfo, pytz.tzinfo.BaseTZInfo, dateutil.tz.tz.tzfile, or None\n",
      " |          Returns None when the array is tz-naive.\n",
      " |  \n",
      " |  weekday\n",
      " |      The day of the week with Monday=0, Sunday=6.\n",
      " |      \n",
      " |      Return the day of the week. It is assumed the week starts on\n",
      " |      Monday, which is denoted by 0 and ends on Sunday which is denoted\n",
      " |      by 6. This method is available on both Series with datetime\n",
      " |      values (using the `dt` accessor) or DatetimeIndex.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      Series or Index\n",
      " |          Containing integers indicating the day number.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      Series.dt.dayofweek : Alias.\n",
      " |      Series.dt.weekday : Alias.\n",
      " |      Series.dt.day_name : Returns the name of the day of the week.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> s = pd.date_range('2016-12-31', '2017-01-08', freq='D').to_series()\n",
      " |      >>> s.dt.dayofweek\n",
      " |      2016-12-31    5\n",
      " |      2017-01-01    6\n",
      " |      2017-01-02    0\n",
      " |      2017-01-03    1\n",
      " |      2017-01-04    2\n",
      " |      2017-01-05    3\n",
      " |      2017-01-06    4\n",
      " |      2017-01-07    5\n",
      " |      2017-01-08    6\n",
      " |      Freq: D, dtype: int64\n",
      " |  \n",
      " |  year\n",
      " |      The year of the datetime.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> datetime_series = pd.Series(\n",
      " |      ...     pd.date_range(\"2000-01-01\", periods=3, freq=\"Y\")\n",
      " |      ... )\n",
      " |      >>> datetime_series\n",
      " |      0   2000-12-31\n",
      " |      1   2001-12-31\n",
      " |      2   2002-12-31\n",
      " |      dtype: datetime64[ns]\n",
      " |      >>> datetime_series.dt.year\n",
      " |      0    2000\n",
      " |      1    2001\n",
      " |      2    2002\n",
      " |      dtype: int64\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from Properties:\n",
      " |  \n",
      " |  __init__(self, data: 'Series', orig)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from pandas.core.accessor.PandasDelegate:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from pandas.core.base.PandasObject:\n",
      " |  \n",
      " |  __repr__(self) -> 'str'\n",
      " |      Return a string representation for a particular object.\n",
      " |  \n",
      " |  __sizeof__(self) -> 'int'\n",
      " |      Generates the total memory usage for an object that returns\n",
      " |      either a value or Series of values\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes inherited from pandas.core.base.PandasObject:\n",
      " |  \n",
      " |  __annotations__ = {'_cache': 'dict[str, Any]'}\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from pandas.core.accessor.DirNamesMixin:\n",
      " |  \n",
      " |  __dir__(self) -> 'list[str]'\n",
      " |      Provide method name lookup and completion.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      Only provide 'public' methods.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from pandas.core.base.NoNewAttributesMixin:\n",
      " |  \n",
      " |  __setattr__(self, key: 'str', value)\n",
      " |      Implement setattr(self, name, value).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(dt)\n"
   ],
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      "text/plain": "48"
     },
     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "import sys\n",
    "from writer import figure_cache\n",
    "\n",
    "sys.getsizeof(figure_cache)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
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