regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import os\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras.optimizers import Adam\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.losses import huber_loss\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2015-04-29 13:47:33.000</th>\n",
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       "      <th>2015-04-29 13:51:33.500</th>\n",
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      "text/plain": [
       "                         c001      f000      f001      f002      f003  \\\n",
       "start_time                                                              \n",
       "2015-04-29 13:45:42.500     0  0.391508  0.621605  0.020106  0.133344   \n",
       "2015-04-29 13:46:23.500     0  0.496306  0.539913  0.123895  0.065477   \n",
       "2015-04-29 13:47:33.000     0  0.458761  0.653943  0.103727  0.124588   \n",
       "2015-04-29 13:48:47.500     0  0.399437  0.605034  0.092632  0.030653   \n",
       "2015-04-29 13:51:33.500     0  0.526496  0.478210  0.088870  0.159128   \n",
       "\n",
       "                             f004      f005      f006      f007      f008  \\\n",
       "start_time                                                                  \n",
       "2015-04-29 13:45:42.500 -0.208305  0.060693 -0.036585 -0.042683  0.859854   \n",
       "2015-04-29 13:46:23.500 -0.019297 -0.043244  0.014388 -0.021583  0.937081   \n",
       "2015-04-29 13:47:33.000 -0.092521  0.045566 -0.006711 -0.144295  0.897399   \n",
       "2015-04-29 13:48:47.500  0.015709 -0.090672  0.003012 -0.224398  0.923290   \n",
       "2015-04-29 13:51:33.500 -0.061213 -0.025549 -0.011050 -0.198895  0.922161   \n",
       "\n",
       "                         ...      f149  f150      f151      f152      f153  \\\n",
       "start_time               ...                                                 \n",
       "2015-04-29 13:45:42.500  ... -1.000000  -1.0 -0.003750 -0.545455 -0.234518   \n",
       "2015-04-29 13:46:23.500  ... -0.979798  -1.0 -0.003939 -0.565217 -0.259640   \n",
       "2015-04-29 13:47:33.000  ... -1.000000  -1.0 -0.004375 -0.608696 -0.280561   \n",
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       "2015-04-29 13:51:33.500  ... -1.000000  -1.0 -0.005172 -0.681818 -0.322367   \n",
       "\n",
       "                             f154      f155      f156      f157      f158  \n",
       "start_time                                                                 \n",
       "2015-04-29 13:45:42.500 -0.036463  0.095238  0.191476 -0.097238  0.893617  \n",
       "2015-04-29 13:46:23.500 -0.047200  0.084524  0.163595 -0.073619  0.913043  \n",
       "2015-04-29 13:47:33.000 -0.055301  0.075000  0.139095 -0.053190  0.812500  \n",
       "2015-04-29 13:48:47.500 -0.058812  0.080952  0.120357  0.002143  0.812500  \n",
       "2015-04-29 13:51:33.500 -0.070498  0.080952  0.107619  0.027619  0.851064  \n",
       "\n",
       "[5 rows x 176 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factor_store = pd.HDFStore('/home/data/training_x_150.h5', mode='r')\n",
    "variety = 'RB'\n",
    "factor_df = factor_store.get(variety)\n",
    "factor_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_time\n",
       "2015-04-29 13:45:42.500   -0.042061\n",
       "2015-04-29 13:46:23.500   -0.210305\n",
       "2015-04-29 13:47:33.000   -0.168279\n",
       "2015-04-29 13:48:47.500   -0.252419\n",
       "2015-04-29 13:51:33.500   -0.210482\n",
       "Name: Y_logret, dtype: float64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_store = pd.HDFStore('/home/data/training_y_reg_150.h5', mode='r')\n",
    "y_series = y_store.get(variety)\n",
    "y_series.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa7c34c4978>"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret_y_series = np.exp(y_series) - 1\n",
    "ret_y_series.hist(range=(-1,1), bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    99419.000000\n",
       "mean         0.116054\n",
       "std          3.241139\n",
       "min         -0.999923\n",
       "25%         -0.183089\n",
       "50%          0.000000\n",
       "75%          0.229571\n",
       "max        355.517368\n",
       "Name: Y_logret, dtype: float64"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_y_series.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08375886505374186"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_y_series.quantile(q=0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    99419.000000\n",
       "mean         0.001237\n",
       "std          0.379694\n",
       "min         -9.468834\n",
       "25%         -0.202225\n",
       "50%          0.000000\n",
       "75%          0.206665\n",
       "max          5.876383\n",
       "Name: Y_logret, dtype: float64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_series.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa7cf067e48>"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close('all')\n",
    "y_series.cumsum()\n",
    "y_series.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.3025850929940455"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert (factor_df.index == y_series.index).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "factor_train, factor_test, y_train, y_test = train_test_split(factor_df, y_series, test_size=0.25, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_gpu_option():\n",
    "    os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\" \n",
    "    config = tf.ConfigProto()\n",
    "    config.gpu_options.allow_growth=True\n",
    "    sess = tf.Session(config=config)\n",
    "    keras.backend.tensorflow_backend.set_session(sess)\n",
    "    \n",
    "def create_model(in_shape):\n",
    "    NUM_NEURONS = 128\n",
    "    model = Sequential()\n",
    "    model.add(Dense(NUM_NEURONS, input_shape=input_shape, activation='relu'))\n",
    "    model.add(Dense(NUM_NEURONS, activation='relu'))\n",
    "    model.add(Dense(NUM_NEURONS, activation='relu'))\n",
    "    model.add(Dense(NUM_NEURONS, activation='relu'))\n",
    "    model.add(Dense(NUM_NEURONS, activation='relu'))\n",
    "    model.add(Dense(NUM_NEURONS, activation='relu'))\n",
    "    model.add(Dense(NUM_NEURONS, activation='relu'))\n",
    "    model.add(Dense(1, activation='linear'))  \n",
    "    model.summary()\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_42\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_145 (Dense)            (None, 128)               22656     \n",
      "_________________________________________________________________\n",
      "dense_146 (Dense)            (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_147 (Dense)            (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_148 (Dense)            (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_149 (Dense)            (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_150 (Dense)            (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_151 (Dense)            (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_152 (Dense)            (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 121,857\n",
      "Trainable params: 121,857\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 67107 samples, validate on 7457 samples\n",
      "Epoch 1/10\n",
      "67107/67107 [==============================] - 13s 199us/step - loss: 0.3600 - val_loss: 0.0661\n",
      "Epoch 2/10\n",
      "67107/67107 [==============================] - 12s 186us/step - loss: 0.1515 - val_loss: 0.0661\n",
      "Epoch 3/10\n",
      "67107/67107 [==============================] - 13s 187us/step - loss: 0.1515 - val_loss: 0.0661\n",
      "Epoch 4/10\n",
      "67107/67107 [==============================] - 12s 181us/step - loss: 0.1514 - val_loss: 0.0661\n",
      "Epoch 5/10\n",
      "67107/67107 [==============================] - 12s 185us/step - loss: 0.1514 - val_loss: 0.0661\n",
      "Epoch 6/10\n",
      "67107/67107 [==============================] - 12s 186us/step - loss: 0.1514 - val_loss: 0.0665\n",
      "Epoch 7/10\n",
      "67107/67107 [==============================] - 13s 187us/step - loss: 0.1514 - val_loss: 0.0665\n",
      "Epoch 8/10\n",
      "67107/67107 [==============================] - 13s 187us/step - loss: 0.1514 - val_loss: 0.0662\n",
      "Epoch 9/10\n",
      "67107/67107 [==============================] - 12s 184us/step - loss: 0.1513 - val_loss: 0.0661\n",
      "Epoch 10/10\n",
      "67107/67107 [==============================] - 13s 189us/step - loss: 0.1513 - val_loss: 0.0661\n",
      "24855/24855 [==============================] - 2s 61us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.07380857783570373"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_shape = (factor_df.shape[1],)\n",
    "\n",
    "model = create_model(in_shape=input_shape)\n",
    "set_gpu_option()\n",
    "model.compile(optimizer=Adam(lr=1e-3),loss=huber_loss)\n",
    "model.fit(x=factor_train, y=y_train, epochs=10, verbose=1, validation_split=0.1)\n",
    "model.evaluate(x=factor_test, y=y_test)\n",
    "          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_time\n",
       "2017-06-26 09:28:27.500    1.060412\n",
       "2017-06-26 09:30:07.000    1.060412\n",
       "2017-06-26 09:32:50.000    1.060412\n",
       "2017-06-26 09:35:15.000    1.060412\n",
       "2017-06-26 09:40:52.000    1.060412\n",
       "2017-06-26 09:44:25.500    1.060412\n",
       "2017-06-26 09:48:15.500    1.060412\n",
       "2017-06-26 09:50:42.000    1.060412\n",
       "2017-06-26 09:55:42.500    1.060412\n",
       "2017-06-26 09:58:18.500    1.060412\n",
       "2017-06-26 10:00:26.500    1.060412\n",
       "2017-06-26 10:05:13.000    1.060412\n",
       "2017-06-26 10:07:45.000    1.060412\n",
       "2017-06-26 10:30:09.000    1.060412\n",
       "2017-06-26 10:32:03.500    1.060412\n",
       "2017-06-26 10:34:42.500    1.060412\n",
       "2017-06-26 10:36:59.500    1.060412\n",
       "2017-06-26 10:42:23.000    1.060412\n",
       "2017-06-26 10:50:29.500    1.060412\n",
       "2017-06-26 10:56:30.000    1.060412\n",
       "2017-06-26 11:00:12.500    1.060412\n",
       "2017-06-26 11:01:17.000    1.060412\n",
       "2017-06-26 11:02:24.000    1.060412\n",
       "2017-06-26 11:07:02.000    1.060412\n",
       "2017-06-26 11:08:03.000    1.060412\n",
       "2017-06-26 11:09:02.000    1.060412\n",
       "2017-06-26 11:09:31.000    1.060412\n",
       "2017-06-26 11:10:13.000    1.060412\n",
       "2017-06-26 11:10:54.000    1.060412\n",
       "2017-06-26 11:11:55.500    1.060412\n",
       "                             ...   \n",
       "2018-03-29 11:08:10.500    1.060412\n",
       "2018-03-29 11:12:00.000    1.060412\n",
       "2018-03-29 11:16:06.000    1.060412\n",
       "2018-03-29 11:20:56.000    1.060412\n",
       "2018-03-29 11:22:11.000    1.060412\n",
       "2018-03-29 11:24:59.000    1.060412\n",
       "2018-03-29 11:28:20.000    1.060412\n",
       "2018-03-29 13:30:16.000    1.060412\n",
       "2018-03-29 13:31:39.000    1.060412\n",
       "2018-03-29 13:34:27.000    1.060412\n",
       "2018-03-29 13:36:40.500    1.060412\n",
       "2018-03-29 13:39:05.500    1.060412\n",
       "2018-03-29 13:42:47.000    1.060412\n",
       "2018-03-29 13:43:40.000    1.060412\n",
       "2018-03-29 13:44:25.000    1.060412\n",
       "2018-03-29 13:46:04.000    1.060412\n",
       "2018-03-29 13:46:48.500    1.060412\n",
       "2018-03-29 13:48:57.500    1.060412\n",
       "2018-03-29 13:51:37.500    1.060412\n",
       "2018-03-29 13:55:03.500    1.060412\n",
       "2018-03-29 13:58:39.000    1.060412\n",
       "2018-03-29 14:01:12.000    1.060412\n",
       "2018-03-29 14:06:21.500    1.060412\n",
       "2018-03-29 14:11:54.500    1.060412\n",
       "2018-03-29 14:17:02.000    1.060412\n",
       "2018-03-29 14:24:33.500    1.060412\n",
       "2018-03-29 14:32:16.500    1.060412\n",
       "2018-03-29 14:39:03.000    1.060412\n",
       "2018-03-29 14:41:07.000    1.060412\n",
       "2018-03-29 14:43:37.500    1.060412\n",
       "Name: predict_y_test, Length: 24855, dtype: float32"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_y_test = model.predict(x=factor_test)\n",
    "predict_y_test_series = pd.Series(data=predict_y_test.flatten(), index=y_test.index, name='predict_y_test')\n",
    "predict_y_test_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "2017-06-26 09:48:15.500   -0.128370\n",
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       "2017-06-26 09:55:42.500    0.353074\n",
       "2017-06-26 09:58:18.500    0.192616\n",
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       "2017-06-26 11:10:13.000    0.032149\n",
       "2017-06-26 11:10:54.000   -0.128493\n",
       "2017-06-26 11:11:55.500   -0.032129\n",
       "                             ...   \n",
       "2018-03-29 11:08:10.500    0.289018\n",
       "2018-03-29 11:12:00.000    0.347323\n",
       "2018-03-29 11:16:06.000    0.057837\n",
       "2018-03-29 11:20:56.000   -0.173611\n",
       "2018-03-29 11:22:11.000   -0.057737\n",
       "2018-03-29 11:24:59.000    0.086668\n",
       "2018-03-29 11:28:20.000    0.144196\n",
       "2018-03-29 13:30:16.000    0.173210\n",
       "2018-03-29 13:31:39.000    0.375127\n",
       "2018-03-29 13:34:27.000    0.404625\n",
       "2018-03-29 13:36:40.500    0.144279\n",
       "2018-03-29 13:39:05.500    0.173110\n",
       "2018-03-29 13:42:47.000    0.028814\n",
       "2018-03-29 13:43:40.000    0.086493\n",
       "2018-03-29 13:44:25.000   -0.230681\n",
       "2018-03-29 13:46:04.000   -0.028848\n",
       "2018-03-29 13:46:48.500   -0.202049\n",
       "2018-03-29 13:48:57.500   -0.144238\n",
       "2018-03-29 13:51:37.500   -0.230747\n",
       "2018-03-29 13:55:03.500   -0.173060\n",
       "2018-03-29 13:58:39.000    0.115407\n",
       "2018-03-29 14:01:12.000    0.201758\n",
       "2018-03-29 14:06:21.500    0.086643\n",
       "2018-03-29 14:11:54.500    0.115407\n",
       "2018-03-29 14:17:02.000    0.288351\n",
       "2018-03-29 14:24:33.500    0.288268\n",
       "2018-03-29 14:32:16.500    0.144071\n",
       "2018-03-29 14:39:03.000    0.028789\n",
       "2018-03-29 14:41:07.000    0.374586\n",
       "2018-03-29 14:43:37.500    0.316684\n",
       "Name: Y_logret, Length: 24855, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "factor_train, factor_test, y_train, y_test = train_test_split(factor_df, y_series, test_size=0.25, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2017-06-26 09:28:27.500</th>\n",
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       "      <th>2017-06-26 09:30:07.000</th>\n",
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       "    <tr>\n",
       "      <th>2017-06-26 09:32:50.000</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-26 09:35:15.000</th>\n",
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       "    <tr>\n",
       "      <th>2017-06-26 09:40:52.000</th>\n",
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       "      <th>2017-06-26 09:44:25.500</th>\n",
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       "    <tr>\n",
       "      <th>2017-06-26 09:48:15.500</th>\n",
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       "      <td>0.071458</td>\n",
       "      <td>0.059889</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.472624</td>\n",
       "      <td>1.054753</td>\n",
       "      <td>0.821429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 09:50:42.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.512648</td>\n",
       "      <td>0.502701</td>\n",
       "      <td>0.044784</td>\n",
       "      <td>0.025876</td>\n",
       "      <td>-0.095700</td>\n",
       "      <td>0.030078</td>\n",
       "      <td>-0.003328</td>\n",
       "      <td>-0.103993</td>\n",
       "      <td>0.963818</td>\n",
       "      <td>...</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.696970</td>\n",
       "      <td>0.003784</td>\n",
       "      <td>1.217391</td>\n",
       "      <td>0.190461</td>\n",
       "      <td>0.116396</td>\n",
       "      <td>0.680702</td>\n",
       "      <td>0.512529</td>\n",
       "      <td>1.017048</td>\n",
       "      <td>0.812500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 09:55:42.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.448473</td>\n",
       "      <td>0.556214</td>\n",
       "      <td>0.019288</td>\n",
       "      <td>0.066020</td>\n",
       "      <td>-0.136553</td>\n",
       "      <td>0.078886</td>\n",
       "      <td>-0.022436</td>\n",
       "      <td>0.008013</td>\n",
       "      <td>0.977124</td>\n",
       "      <td>...</td>\n",
       "      <td>0.070707</td>\n",
       "      <td>0.656566</td>\n",
       "      <td>0.001944</td>\n",
       "      <td>0.482759</td>\n",
       "      <td>0.232157</td>\n",
       "      <td>0.131424</td>\n",
       "      <td>0.686952</td>\n",
       "      <td>0.551670</td>\n",
       "      <td>0.957515</td>\n",
       "      <td>0.796460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 09:58:18.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.497761</td>\n",
       "      <td>0.528533</td>\n",
       "      <td>0.045520</td>\n",
       "      <td>0.025196</td>\n",
       "      <td>0.001383</td>\n",
       "      <td>-0.065603</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.961195</td>\n",
       "      <td>...</td>\n",
       "      <td>0.393939</td>\n",
       "      <td>0.676768</td>\n",
       "      <td>0.001622</td>\n",
       "      <td>0.387097</td>\n",
       "      <td>0.299764</td>\n",
       "      <td>0.158656</td>\n",
       "      <td>0.702083</td>\n",
       "      <td>0.588977</td>\n",
       "      <td>0.928297</td>\n",
       "      <td>0.836364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 10:00:26.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.390627</td>\n",
       "      <td>0.620302</td>\n",
       "      <td>0.025758</td>\n",
       "      <td>0.108302</td>\n",
       "      <td>-0.117430</td>\n",
       "      <td>0.006364</td>\n",
       "      <td>-0.012216</td>\n",
       "      <td>-0.064572</td>\n",
       "      <td>0.872153</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.090909</td>\n",
       "      <td>0.595960</td>\n",
       "      <td>0.002286</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.312833</td>\n",
       "      <td>0.157258</td>\n",
       "      <td>0.670833</td>\n",
       "      <td>0.615928</td>\n",
       "      <td>0.780643</td>\n",
       "      <td>0.853211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 10:05:13.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.499013</td>\n",
       "      <td>0.526048</td>\n",
       "      <td>0.041826</td>\n",
       "      <td>0.048233</td>\n",
       "      <td>-0.040531</td>\n",
       "      <td>-0.022581</td>\n",
       "      <td>0.006579</td>\n",
       "      <td>-0.074013</td>\n",
       "      <td>0.958761</td>\n",
       "      <td>...</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.001781</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>0.329078</td>\n",
       "      <td>0.157517</td>\n",
       "      <td>0.645833</td>\n",
       "      <td>0.634196</td>\n",
       "      <td>0.669108</td>\n",
       "      <td>0.871560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 10:07:45.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.511669</td>\n",
       "      <td>0.497110</td>\n",
       "      <td>0.071646</td>\n",
       "      <td>0.030075</td>\n",
       "      <td>0.028619</td>\n",
       "      <td>-0.091377</td>\n",
       "      <td>0.004499</td>\n",
       "      <td>-0.169854</td>\n",
       "      <td>0.927673</td>\n",
       "      <td>...</td>\n",
       "      <td>0.595960</td>\n",
       "      <td>0.595960</td>\n",
       "      <td>0.001892</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.367862</td>\n",
       "      <td>0.169034</td>\n",
       "      <td>0.653472</td>\n",
       "      <td>0.648695</td>\n",
       "      <td>0.663026</td>\n",
       "      <td>0.942857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 10:30:09.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.706094</td>\n",
       "      <td>0.323853</td>\n",
       "      <td>0.037216</td>\n",
       "      <td>0.047758</td>\n",
       "      <td>0.084516</td>\n",
       "      <td>-0.103375</td>\n",
       "      <td>0.017467</td>\n",
       "      <td>0.034934</td>\n",
       "      <td>1.031603</td>\n",
       "      <td>...</td>\n",
       "      <td>0.747475</td>\n",
       "      <td>0.696970</td>\n",
       "      <td>0.002593</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.429099</td>\n",
       "      <td>0.191201</td>\n",
       "      <td>0.670833</td>\n",
       "      <td>0.660910</td>\n",
       "      <td>0.690680</td>\n",
       "      <td>0.915094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 10:32:03.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.652999</td>\n",
       "      <td>0.351288</td>\n",
       "      <td>0.072727</td>\n",
       "      <td>0.039289</td>\n",
       "      <td>-0.008102</td>\n",
       "      <td>-0.099348</td>\n",
       "      <td>0.009434</td>\n",
       "      <td>0.025157</td>\n",
       "      <td>0.896875</td>\n",
       "      <td>...</td>\n",
       "      <td>0.858586</td>\n",
       "      <td>0.717172</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.448469</td>\n",
       "      <td>0.191325</td>\n",
       "      <td>0.678125</td>\n",
       "      <td>0.669468</td>\n",
       "      <td>0.695439</td>\n",
       "      <td>0.951923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 10:34:42.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.530319</td>\n",
       "      <td>0.470023</td>\n",
       "      <td>0.029784</td>\n",
       "      <td>0.094017</td>\n",
       "      <td>-0.006248</td>\n",
       "      <td>-0.018508</td>\n",
       "      <td>-0.014599</td>\n",
       "      <td>-0.009124</td>\n",
       "      <td>0.967121</td>\n",
       "      <td>...</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.717172</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.451749</td>\n",
       "      <td>0.183399</td>\n",
       "      <td>0.673264</td>\n",
       "      <td>0.672876</td>\n",
       "      <td>0.674039</td>\n",
       "      <td>0.925926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 10:36:59.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.529754</td>\n",
       "      <td>0.474854</td>\n",
       "      <td>0.040195</td>\n",
       "      <td>0.087625</td>\n",
       "      <td>-0.025943</td>\n",
       "      <td>-0.022692</td>\n",
       "      <td>0.003091</td>\n",
       "      <td>-0.116692</td>\n",
       "      <td>0.934929</td>\n",
       "      <td>...</td>\n",
       "      <td>0.797980</td>\n",
       "      <td>0.757576</td>\n",
       "      <td>0.001282</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.412854</td>\n",
       "      <td>0.154782</td>\n",
       "      <td>0.685417</td>\n",
       "      <td>0.674751</td>\n",
       "      <td>0.706747</td>\n",
       "      <td>1.095745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 10:42:23.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.561922</td>\n",
       "      <td>0.441630</td>\n",
       "      <td>0.037123</td>\n",
       "      <td>0.051994</td>\n",
       "      <td>-0.037616</td>\n",
       "      <td>0.019279</td>\n",
       "      <td>-0.003083</td>\n",
       "      <td>-0.204008</td>\n",
       "      <td>0.854043</td>\n",
       "      <td>...</td>\n",
       "      <td>0.737374</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.000811</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.361070</td>\n",
       "      <td>0.121151</td>\n",
       "      <td>0.696875</td>\n",
       "      <td>0.676369</td>\n",
       "      <td>0.737887</td>\n",
       "      <td>1.107527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 10:50:29.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.530555</td>\n",
       "      <td>0.469531</td>\n",
       "      <td>0.071444</td>\n",
       "      <td>0.056124</td>\n",
       "      <td>-0.066095</td>\n",
       "      <td>0.011362</td>\n",
       "      <td>-0.002774</td>\n",
       "      <td>-0.162968</td>\n",
       "      <td>0.940344</td>\n",
       "      <td>...</td>\n",
       "      <td>0.484848</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.000526</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.361057</td>\n",
       "      <td>0.115087</td>\n",
       "      <td>0.709375</td>\n",
       "      <td>0.678611</td>\n",
       "      <td>0.770903</td>\n",
       "      <td>0.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 10:56:30.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.521486</td>\n",
       "      <td>0.482222</td>\n",
       "      <td>0.089390</td>\n",
       "      <td>0.067446</td>\n",
       "      <td>-0.013764</td>\n",
       "      <td>-0.055318</td>\n",
       "      <td>0.006742</td>\n",
       "      <td>-0.162921</td>\n",
       "      <td>0.989176</td>\n",
       "      <td>...</td>\n",
       "      <td>0.898990</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.000526</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.344832</td>\n",
       "      <td>0.101220</td>\n",
       "      <td>0.728125</td>\n",
       "      <td>0.681215</td>\n",
       "      <td>0.821944</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 11:00:12.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.588676</td>\n",
       "      <td>0.434642</td>\n",
       "      <td>0.084963</td>\n",
       "      <td>0.057525</td>\n",
       "      <td>0.044938</td>\n",
       "      <td>-0.112877</td>\n",
       "      <td>0.038760</td>\n",
       "      <td>0.054264</td>\n",
       "      <td>0.960118</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.898990</td>\n",
       "      <td>0.000649</td>\n",
       "      <td>0.138889</td>\n",
       "      <td>0.322078</td>\n",
       "      <td>0.084782</td>\n",
       "      <td>0.784375</td>\n",
       "      <td>0.692569</td>\n",
       "      <td>0.967986</td>\n",
       "      <td>1.064516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 11:01:17.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.734661</td>\n",
       "      <td>0.336664</td>\n",
       "      <td>0.030809</td>\n",
       "      <td>0.058337</td>\n",
       "      <td>-0.023221</td>\n",
       "      <td>-0.131106</td>\n",
       "      <td>-0.007463</td>\n",
       "      <td>0.033582</td>\n",
       "      <td>0.928092</td>\n",
       "      <td>...</td>\n",
       "      <td>0.939394</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000526</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.296178</td>\n",
       "      <td>0.067593</td>\n",
       "      <td>0.824030</td>\n",
       "      <td>0.710389</td>\n",
       "      <td>1.051312</td>\n",
       "      <td>0.989583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 11:02:24.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.534962</td>\n",
       "      <td>0.471044</td>\n",
       "      <td>0.059242</td>\n",
       "      <td>0.046201</td>\n",
       "      <td>-0.015422</td>\n",
       "      <td>-0.019733</td>\n",
       "      <td>-0.005396</td>\n",
       "      <td>-0.138489</td>\n",
       "      <td>0.909334</td>\n",
       "      <td>...</td>\n",
       "      <td>0.898990</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.260588</td>\n",
       "      <td>0.046418</td>\n",
       "      <td>0.834591</td>\n",
       "      <td>0.728501</td>\n",
       "      <td>1.046770</td>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 11:07:02.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.409582</td>\n",
       "      <td>0.590504</td>\n",
       "      <td>0.012198</td>\n",
       "      <td>0.102315</td>\n",
       "      <td>-0.192776</td>\n",
       "      <td>0.060214</td>\n",
       "      <td>-0.098361</td>\n",
       "      <td>-0.053279</td>\n",
       "      <td>0.930550</td>\n",
       "      <td>...</td>\n",
       "      <td>0.252525</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>-0.000361</td>\n",
       "      <td>-0.069767</td>\n",
       "      <td>0.176875</td>\n",
       "      <td>0.002241</td>\n",
       "      <td>0.785483</td>\n",
       "      <td>0.739966</td>\n",
       "      <td>0.876518</td>\n",
       "      <td>0.756757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 11:08:03.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.481112</td>\n",
       "      <td>0.544028</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.025555</td>\n",
       "      <td>0.018975</td>\n",
       "      <td>0.003891</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>1.033411</td>\n",
       "      <td>...</td>\n",
       "      <td>0.494949</td>\n",
       "      <td>0.959596</td>\n",
       "      <td>-0.002414</td>\n",
       "      <td>-0.388889</td>\n",
       "      <td>0.083769</td>\n",
       "      <td>-0.044424</td>\n",
       "      <td>0.709144</td>\n",
       "      <td>0.743068</td>\n",
       "      <td>0.641297</td>\n",
       "      <td>0.718182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 11:09:02.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.526817</td>\n",
       "      <td>0.480030</td>\n",
       "      <td>0.020102</td>\n",
       "      <td>0.177471</td>\n",
       "      <td>-0.135775</td>\n",
       "      <td>0.035156</td>\n",
       "      <td>-0.103448</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>0.951960</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>-0.003258</td>\n",
       "      <td>-0.491525</td>\n",
       "      <td>-0.028484</td>\n",
       "      <td>-0.098329</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.738531</td>\n",
       "      <td>0.406620</td>\n",
       "      <td>0.689655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 11:09:31.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.431009</td>\n",
       "      <td>0.608259</td>\n",
       "      <td>0.040292</td>\n",
       "      <td>0.056025</td>\n",
       "      <td>-0.055851</td>\n",
       "      <td>-0.052735</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.136905</td>\n",
       "      <td>1.011037</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.090909</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>-0.003895</td>\n",
       "      <td>-0.560606</td>\n",
       "      <td>-0.115171</td>\n",
       "      <td>-0.136756</td>\n",
       "      <td>0.557805</td>\n",
       "      <td>0.725770</td>\n",
       "      <td>0.221875</td>\n",
       "      <td>0.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 11:10:13.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.544787</td>\n",
       "      <td>0.476346</td>\n",
       "      <td>0.087031</td>\n",
       "      <td>0.045773</td>\n",
       "      <td>0.021374</td>\n",
       "      <td>-0.042288</td>\n",
       "      <td>0.085366</td>\n",
       "      <td>0.042683</td>\n",
       "      <td>1.032398</td>\n",
       "      <td>...</td>\n",
       "      <td>0.656566</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>-0.002115</td>\n",
       "      <td>-0.349206</td>\n",
       "      <td>-0.150493</td>\n",
       "      <td>-0.147580</td>\n",
       "      <td>0.526108</td>\n",
       "      <td>0.708693</td>\n",
       "      <td>0.160939</td>\n",
       "      <td>0.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 11:10:54.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.609749</td>\n",
       "      <td>0.405728</td>\n",
       "      <td>0.100195</td>\n",
       "      <td>0.040056</td>\n",
       "      <td>0.001340</td>\n",
       "      <td>-0.014181</td>\n",
       "      <td>0.048780</td>\n",
       "      <td>0.048780</td>\n",
       "      <td>1.028527</td>\n",
       "      <td>...</td>\n",
       "      <td>0.838384</td>\n",
       "      <td>0.878788</td>\n",
       "      <td>-0.000654</td>\n",
       "      <td>-0.122807</td>\n",
       "      <td>-0.144059</td>\n",
       "      <td>-0.136984</td>\n",
       "      <td>0.521644</td>\n",
       "      <td>0.689920</td>\n",
       "      <td>0.185092</td>\n",
       "      <td>0.834783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26 11:11:55.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.535274</td>\n",
       "      <td>0.470670</td>\n",
       "      <td>0.054951</td>\n",
       "      <td>0.076080</td>\n",
       "      <td>-0.034515</td>\n",
       "      <td>-0.031977</td>\n",
       "      <td>-0.002933</td>\n",
       "      <td>0.024927</td>\n",
       "      <td>0.970747</td>\n",
       "      <td>...</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.878788</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.166569</td>\n",
       "      <td>-0.141390</td>\n",
       "      <td>0.497537</td>\n",
       "      <td>0.666861</td>\n",
       "      <td>0.158889</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 11:08:10.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.501179</td>\n",
       "      <td>0.527495</td>\n",
       "      <td>0.076589</td>\n",
       "      <td>0.059234</td>\n",
       "      <td>-0.094746</td>\n",
       "      <td>0.030607</td>\n",
       "      <td>-0.006536</td>\n",
       "      <td>-0.110022</td>\n",
       "      <td>0.902228</td>\n",
       "      <td>...</td>\n",
       "      <td>0.919192</td>\n",
       "      <td>0.616162</td>\n",
       "      <td>0.001250</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.095856</td>\n",
       "      <td>-0.015026</td>\n",
       "      <td>0.828974</td>\n",
       "      <td>0.595049</td>\n",
       "      <td>1.296826</td>\n",
       "      <td>0.978947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 11:12:00.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.571316</td>\n",
       "      <td>0.428868</td>\n",
       "      <td>0.049391</td>\n",
       "      <td>0.074132</td>\n",
       "      <td>-0.070532</td>\n",
       "      <td>0.004524</td>\n",
       "      <td>-0.016260</td>\n",
       "      <td>-0.166667</td>\n",
       "      <td>0.934344</td>\n",
       "      <td>...</td>\n",
       "      <td>0.797980</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000746</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>0.142368</td>\n",
       "      <td>0.008981</td>\n",
       "      <td>0.828308</td>\n",
       "      <td>0.640367</td>\n",
       "      <td>1.204190</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 11:16:06.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.484426</td>\n",
       "      <td>0.517985</td>\n",
       "      <td>0.191805</td>\n",
       "      <td>0.090387</td>\n",
       "      <td>-0.008251</td>\n",
       "      <td>-0.072356</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>-0.190517</td>\n",
       "      <td>0.873608</td>\n",
       "      <td>...</td>\n",
       "      <td>0.969697</td>\n",
       "      <td>0.676768</td>\n",
       "      <td>0.001515</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.215042</td>\n",
       "      <td>0.044869</td>\n",
       "      <td>0.846308</td>\n",
       "      <td>0.684423</td>\n",
       "      <td>1.170077</td>\n",
       "      <td>0.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 11:20:56.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.595400</td>\n",
       "      <td>0.448491</td>\n",
       "      <td>0.064734</td>\n",
       "      <td>0.063057</td>\n",
       "      <td>-0.003162</td>\n",
       "      <td>-0.178773</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.053333</td>\n",
       "      <td>0.997003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>0.717172</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.270343</td>\n",
       "      <td>0.070276</td>\n",
       "      <td>0.858186</td>\n",
       "      <td>0.725995</td>\n",
       "      <td>1.122569</td>\n",
       "      <td>1.053191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 11:22:11.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.500803</td>\n",
       "      <td>0.505591</td>\n",
       "      <td>0.109618</td>\n",
       "      <td>0.052122</td>\n",
       "      <td>-0.057126</td>\n",
       "      <td>-0.081403</td>\n",
       "      <td>0.005952</td>\n",
       "      <td>-0.142857</td>\n",
       "      <td>0.979930</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.003429</td>\n",
       "      <td>1.043478</td>\n",
       "      <td>0.360732</td>\n",
       "      <td>0.111957</td>\n",
       "      <td>0.882186</td>\n",
       "      <td>0.763849</td>\n",
       "      <td>1.118861</td>\n",
       "      <td>1.086022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 11:24:59.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.468516</td>\n",
       "      <td>0.533429</td>\n",
       "      <td>0.082785</td>\n",
       "      <td>0.102139</td>\n",
       "      <td>-0.096352</td>\n",
       "      <td>0.030744</td>\n",
       "      <td>-0.007463</td>\n",
       "      <td>-0.134328</td>\n",
       "      <td>0.890947</td>\n",
       "      <td>...</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.003824</td>\n",
       "      <td>1.238095</td>\n",
       "      <td>0.439345</td>\n",
       "      <td>0.145666</td>\n",
       "      <td>0.895901</td>\n",
       "      <td>0.798467</td>\n",
       "      <td>1.090768</td>\n",
       "      <td>0.940594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 11:28:20.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.398488</td>\n",
       "      <td>0.606019</td>\n",
       "      <td>0.084609</td>\n",
       "      <td>0.121608</td>\n",
       "      <td>-0.069913</td>\n",
       "      <td>-0.014743</td>\n",
       "      <td>0.008584</td>\n",
       "      <td>-0.077253</td>\n",
       "      <td>1.049610</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.878788</td>\n",
       "      <td>0.003030</td>\n",
       "      <td>0.869565</td>\n",
       "      <td>0.512184</td>\n",
       "      <td>0.174802</td>\n",
       "      <td>0.895901</td>\n",
       "      <td>0.826048</td>\n",
       "      <td>1.035606</td>\n",
       "      <td>0.950980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 13:30:16.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.524443</td>\n",
       "      <td>0.508837</td>\n",
       "      <td>0.059697</td>\n",
       "      <td>0.098242</td>\n",
       "      <td>-0.026276</td>\n",
       "      <td>-0.067829</td>\n",
       "      <td>-0.024096</td>\n",
       "      <td>0.033133</td>\n",
       "      <td>1.006297</td>\n",
       "      <td>...</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.590771</td>\n",
       "      <td>0.205355</td>\n",
       "      <td>0.889704</td>\n",
       "      <td>0.846742</td>\n",
       "      <td>0.975629</td>\n",
       "      <td>0.903846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 13:31:39.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.494151</td>\n",
       "      <td>0.543224</td>\n",
       "      <td>0.055777</td>\n",
       "      <td>0.163053</td>\n",
       "      <td>-0.041988</td>\n",
       "      <td>-0.042986</td>\n",
       "      <td>-0.002976</td>\n",
       "      <td>-0.139881</td>\n",
       "      <td>0.964911</td>\n",
       "      <td>...</td>\n",
       "      <td>0.939394</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.637369</td>\n",
       "      <td>0.218386</td>\n",
       "      <td>0.870260</td>\n",
       "      <td>0.859337</td>\n",
       "      <td>0.892105</td>\n",
       "      <td>0.941748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 13:34:27.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.529305</td>\n",
       "      <td>0.479719</td>\n",
       "      <td>0.053354</td>\n",
       "      <td>0.147316</td>\n",
       "      <td>-0.089404</td>\n",
       "      <td>-0.076240</td>\n",
       "      <td>-0.037453</td>\n",
       "      <td>-0.116105</td>\n",
       "      <td>0.991138</td>\n",
       "      <td>...</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>0.002877</td>\n",
       "      <td>0.807692</td>\n",
       "      <td>0.634512</td>\n",
       "      <td>0.206038</td>\n",
       "      <td>0.840815</td>\n",
       "      <td>0.863654</td>\n",
       "      <td>0.795137</td>\n",
       "      <td>0.932039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 13:36:40.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.447322</td>\n",
       "      <td>0.554445</td>\n",
       "      <td>0.100660</td>\n",
       "      <td>0.060257</td>\n",
       "      <td>-0.002650</td>\n",
       "      <td>-0.044774</td>\n",
       "      <td>0.027586</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>0.956812</td>\n",
       "      <td>...</td>\n",
       "      <td>0.939394</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001765</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.657754</td>\n",
       "      <td>0.207358</td>\n",
       "      <td>0.849149</td>\n",
       "      <td>0.865672</td>\n",
       "      <td>0.816102</td>\n",
       "      <td>1.029412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 13:39:05.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.549230</td>\n",
       "      <td>0.463318</td>\n",
       "      <td>0.052375</td>\n",
       "      <td>0.046845</td>\n",
       "      <td>0.015847</td>\n",
       "      <td>-0.067432</td>\n",
       "      <td>-0.002257</td>\n",
       "      <td>-0.141084</td>\n",
       "      <td>0.944757</td>\n",
       "      <td>...</td>\n",
       "      <td>0.929293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002113</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>0.675089</td>\n",
       "      <td>0.205657</td>\n",
       "      <td>0.877482</td>\n",
       "      <td>0.870589</td>\n",
       "      <td>0.891267</td>\n",
       "      <td>1.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 13:42:47.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.456006</td>\n",
       "      <td>0.556398</td>\n",
       "      <td>0.042616</td>\n",
       "      <td>0.011608</td>\n",
       "      <td>0.017068</td>\n",
       "      <td>-0.180271</td>\n",
       "      <td>0.056604</td>\n",
       "      <td>0.028302</td>\n",
       "      <td>0.942038</td>\n",
       "      <td>...</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002800</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.683624</td>\n",
       "      <td>0.199642</td>\n",
       "      <td>0.887482</td>\n",
       "      <td>0.874707</td>\n",
       "      <td>0.913033</td>\n",
       "      <td>1.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 13:43:40.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.603173</td>\n",
       "      <td>0.551112</td>\n",
       "      <td>0.102223</td>\n",
       "      <td>0.069780</td>\n",
       "      <td>-0.057097</td>\n",
       "      <td>-0.136282</td>\n",
       "      <td>-0.033333</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>1.038609</td>\n",
       "      <td>...</td>\n",
       "      <td>0.919192</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002703</td>\n",
       "      <td>0.740741</td>\n",
       "      <td>0.674719</td>\n",
       "      <td>0.185207</td>\n",
       "      <td>0.885650</td>\n",
       "      <td>0.877453</td>\n",
       "      <td>0.902043</td>\n",
       "      <td>1.164948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 13:44:25.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.448482</td>\n",
       "      <td>0.562118</td>\n",
       "      <td>0.144242</td>\n",
       "      <td>0.043736</td>\n",
       "      <td>-0.085323</td>\n",
       "      <td>-0.036430</td>\n",
       "      <td>0.015152</td>\n",
       "      <td>-0.042929</td>\n",
       "      <td>0.999348</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002208</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>0.645228</td>\n",
       "      <td>0.161201</td>\n",
       "      <td>0.883324</td>\n",
       "      <td>0.877567</td>\n",
       "      <td>0.894839</td>\n",
       "      <td>1.185567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 13:46:04.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.380695</td>\n",
       "      <td>0.657784</td>\n",
       "      <td>0.018588</td>\n",
       "      <td>0.068760</td>\n",
       "      <td>-0.051710</td>\n",
       "      <td>0.063542</td>\n",
       "      <td>-0.022472</td>\n",
       "      <td>-0.073034</td>\n",
       "      <td>0.998990</td>\n",
       "      <td>...</td>\n",
       "      <td>0.838384</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003165</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>0.627536</td>\n",
       "      <td>0.144295</td>\n",
       "      <td>0.876333</td>\n",
       "      <td>0.875610</td>\n",
       "      <td>0.877780</td>\n",
       "      <td>1.147059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 13:46:48.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.554512</td>\n",
       "      <td>0.455911</td>\n",
       "      <td>0.107663</td>\n",
       "      <td>0.192037</td>\n",
       "      <td>0.031177</td>\n",
       "      <td>-0.045497</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.110465</td>\n",
       "      <td>0.977789</td>\n",
       "      <td>...</td>\n",
       "      <td>0.939394</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002048</td>\n",
       "      <td>0.515152</td>\n",
       "      <td>0.580732</td>\n",
       "      <td>0.113679</td>\n",
       "      <td>0.857184</td>\n",
       "      <td>0.871738</td>\n",
       "      <td>0.828076</td>\n",
       "      <td>1.145631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 13:48:57.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.526508</td>\n",
       "      <td>0.488291</td>\n",
       "      <td>0.123267</td>\n",
       "      <td>0.118162</td>\n",
       "      <td>-0.066130</td>\n",
       "      <td>-0.030489</td>\n",
       "      <td>0.006250</td>\n",
       "      <td>-0.081250</td>\n",
       "      <td>0.921441</td>\n",
       "      <td>...</td>\n",
       "      <td>0.939394</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001852</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.551378</td>\n",
       "      <td>0.093317</td>\n",
       "      <td>0.854052</td>\n",
       "      <td>0.868173</td>\n",
       "      <td>0.825810</td>\n",
       "      <td>1.210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 13:51:37.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.569247</td>\n",
       "      <td>0.459602</td>\n",
       "      <td>0.098048</td>\n",
       "      <td>0.076695</td>\n",
       "      <td>-0.092439</td>\n",
       "      <td>-0.017960</td>\n",
       "      <td>0.004854</td>\n",
       "      <td>-0.125000</td>\n",
       "      <td>0.996968</td>\n",
       "      <td>...</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001852</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.530793</td>\n",
       "      <td>0.078359</td>\n",
       "      <td>0.860163</td>\n",
       "      <td>0.867163</td>\n",
       "      <td>0.846162</td>\n",
       "      <td>1.291667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 13:55:03.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.686056</td>\n",
       "      <td>0.323369</td>\n",
       "      <td>0.056320</td>\n",
       "      <td>0.073184</td>\n",
       "      <td>-0.116383</td>\n",
       "      <td>-0.014045</td>\n",
       "      <td>-0.006961</td>\n",
       "      <td>-0.184455</td>\n",
       "      <td>0.884305</td>\n",
       "      <td>...</td>\n",
       "      <td>0.919192</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001053</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.524786</td>\n",
       "      <td>0.071438</td>\n",
       "      <td>0.877853</td>\n",
       "      <td>0.870867</td>\n",
       "      <td>0.891825</td>\n",
       "      <td>1.305263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 13:58:39.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.481132</td>\n",
       "      <td>0.522660</td>\n",
       "      <td>0.032973</td>\n",
       "      <td>0.074408</td>\n",
       "      <td>-0.185026</td>\n",
       "      <td>0.037135</td>\n",
       "      <td>-0.019608</td>\n",
       "      <td>-0.070261</td>\n",
       "      <td>0.969222</td>\n",
       "      <td>...</td>\n",
       "      <td>0.737374</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>0.000769</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.489824</td>\n",
       "      <td>0.050385</td>\n",
       "      <td>0.850075</td>\n",
       "      <td>0.870960</td>\n",
       "      <td>0.808306</td>\n",
       "      <td>1.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 14:01:12.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.575698</td>\n",
       "      <td>0.436479</td>\n",
       "      <td>0.107459</td>\n",
       "      <td>0.067263</td>\n",
       "      <td>-0.043669</td>\n",
       "      <td>-0.002426</td>\n",
       "      <td>0.001616</td>\n",
       "      <td>-0.165590</td>\n",
       "      <td>0.855447</td>\n",
       "      <td>...</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001351</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.486864</td>\n",
       "      <td>0.046386</td>\n",
       "      <td>0.827853</td>\n",
       "      <td>0.865997</td>\n",
       "      <td>0.751565</td>\n",
       "      <td>1.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 14:06:21.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.532056</td>\n",
       "      <td>0.504019</td>\n",
       "      <td>0.058785</td>\n",
       "      <td>0.102009</td>\n",
       "      <td>-0.085935</td>\n",
       "      <td>0.007150</td>\n",
       "      <td>-0.009050</td>\n",
       "      <td>-0.160633</td>\n",
       "      <td>0.909137</td>\n",
       "      <td>...</td>\n",
       "      <td>0.616162</td>\n",
       "      <td>0.939394</td>\n",
       "      <td>0.000811</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.431710</td>\n",
       "      <td>0.016489</td>\n",
       "      <td>0.780794</td>\n",
       "      <td>0.855328</td>\n",
       "      <td>0.631726</td>\n",
       "      <td>1.134615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 14:11:54.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.470855</td>\n",
       "      <td>0.530440</td>\n",
       "      <td>0.073695</td>\n",
       "      <td>0.090581</td>\n",
       "      <td>-0.016886</td>\n",
       "      <td>-0.090859</td>\n",
       "      <td>0.006504</td>\n",
       "      <td>-0.222764</td>\n",
       "      <td>0.878624</td>\n",
       "      <td>...</td>\n",
       "      <td>0.696970</td>\n",
       "      <td>0.919192</td>\n",
       "      <td>-0.000149</td>\n",
       "      <td>-0.029412</td>\n",
       "      <td>0.391061</td>\n",
       "      <td>-0.004660</td>\n",
       "      <td>0.756512</td>\n",
       "      <td>0.842414</td>\n",
       "      <td>0.584708</td>\n",
       "      <td>1.155340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 14:17:02.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.488971</td>\n",
       "      <td>0.515644</td>\n",
       "      <td>0.055053</td>\n",
       "      <td>0.070697</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>-0.063036</td>\n",
       "      <td>-0.001112</td>\n",
       "      <td>-0.190211</td>\n",
       "      <td>0.814158</td>\n",
       "      <td>...</td>\n",
       "      <td>0.515152</td>\n",
       "      <td>0.878788</td>\n",
       "      <td>-0.000149</td>\n",
       "      <td>-0.029412</td>\n",
       "      <td>0.315683</td>\n",
       "      <td>-0.042116</td>\n",
       "      <td>0.717662</td>\n",
       "      <td>0.825848</td>\n",
       "      <td>0.501288</td>\n",
       "      <td>1.165049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 14:24:33.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.490603</td>\n",
       "      <td>0.514304</td>\n",
       "      <td>0.101009</td>\n",
       "      <td>0.084529</td>\n",
       "      <td>-0.037728</td>\n",
       "      <td>-0.035136</td>\n",
       "      <td>0.002165</td>\n",
       "      <td>-0.204004</td>\n",
       "      <td>0.846031</td>\n",
       "      <td>...</td>\n",
       "      <td>0.575758</td>\n",
       "      <td>0.858586</td>\n",
       "      <td>-0.000294</td>\n",
       "      <td>-0.057143</td>\n",
       "      <td>0.269222</td>\n",
       "      <td>-0.063240</td>\n",
       "      <td>0.698313</td>\n",
       "      <td>0.808046</td>\n",
       "      <td>0.478847</td>\n",
       "      <td>1.178218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 14:32:16.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.570368</td>\n",
       "      <td>0.430186</td>\n",
       "      <td>0.086798</td>\n",
       "      <td>0.083934</td>\n",
       "      <td>-0.018339</td>\n",
       "      <td>-0.074002</td>\n",
       "      <td>0.008653</td>\n",
       "      <td>-0.203337</td>\n",
       "      <td>0.862856</td>\n",
       "      <td>...</td>\n",
       "      <td>0.696970</td>\n",
       "      <td>0.858586</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.060606</td>\n",
       "      <td>0.237348</td>\n",
       "      <td>-0.076016</td>\n",
       "      <td>0.687462</td>\n",
       "      <td>0.791074</td>\n",
       "      <td>0.480238</td>\n",
       "      <td>1.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 14:39:03.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.558384</td>\n",
       "      <td>0.449685</td>\n",
       "      <td>0.068911</td>\n",
       "      <td>0.033435</td>\n",
       "      <td>0.008718</td>\n",
       "      <td>-0.022816</td>\n",
       "      <td>0.008065</td>\n",
       "      <td>-0.094758</td>\n",
       "      <td>0.926228</td>\n",
       "      <td>...</td>\n",
       "      <td>0.878788</td>\n",
       "      <td>0.878788</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.225683</td>\n",
       "      <td>-0.078047</td>\n",
       "      <td>0.691150</td>\n",
       "      <td>0.774784</td>\n",
       "      <td>0.523882</td>\n",
       "      <td>1.247423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 14:41:07.000</th>\n",
       "      <td>0</td>\n",
       "      <td>0.485114</td>\n",
       "      <td>0.518715</td>\n",
       "      <td>0.066916</td>\n",
       "      <td>0.155610</td>\n",
       "      <td>-0.057534</td>\n",
       "      <td>-0.033218</td>\n",
       "      <td>-0.006645</td>\n",
       "      <td>-0.156146</td>\n",
       "      <td>1.007434</td>\n",
       "      <td>...</td>\n",
       "      <td>0.525253</td>\n",
       "      <td>0.838384</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.199641</td>\n",
       "      <td>-0.087166</td>\n",
       "      <td>0.661150</td>\n",
       "      <td>0.754882</td>\n",
       "      <td>0.473685</td>\n",
       "      <td>1.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29 14:43:37.500</th>\n",
       "      <td>0</td>\n",
       "      <td>0.452129</td>\n",
       "      <td>0.630905</td>\n",
       "      <td>0.107430</td>\n",
       "      <td>0.189495</td>\n",
       "      <td>0.015035</td>\n",
       "      <td>-0.141915</td>\n",
       "      <td>0.011299</td>\n",
       "      <td>-0.098870</td>\n",
       "      <td>0.932201</td>\n",
       "      <td>...</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.838384</td>\n",
       "      <td>-0.000145</td>\n",
       "      <td>-0.028571</td>\n",
       "      <td>0.208379</td>\n",
       "      <td>-0.078438</td>\n",
       "      <td>0.649571</td>\n",
       "      <td>0.732054</td>\n",
       "      <td>0.484605</td>\n",
       "      <td>1.155340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24855 rows × 176 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         c001      f000      f001      f002      f003  \\\n",
       "start_time                                                              \n",
       "2017-06-26 09:28:27.500     0  0.659444  0.361323  0.124693  0.035322   \n",
       "2017-06-26 09:30:07.000     0  0.670770  0.340530  0.048595  0.048165   \n",
       "2017-06-26 09:32:50.000     0  0.615574  0.386582  0.023649  0.047859   \n",
       "2017-06-26 09:35:15.000     0  0.495852  0.516137  0.030558  0.054404   \n",
       "2017-06-26 09:40:52.000     0  0.557426  0.453824  0.057840  0.038567   \n",
       "2017-06-26 09:44:25.500     0  0.568492  0.457017  0.048735  0.071056   \n",
       "2017-06-26 09:48:15.500     0  0.615329  0.402207  0.032208  0.038619   \n",
       "2017-06-26 09:50:42.000     0  0.512648  0.502701  0.044784  0.025876   \n",
       "2017-06-26 09:55:42.500     0  0.448473  0.556214  0.019288  0.066020   \n",
       "2017-06-26 09:58:18.500     0  0.497761  0.528533  0.045520  0.025196   \n",
       "2017-06-26 10:00:26.500     0  0.390627  0.620302  0.025758  0.108302   \n",
       "2017-06-26 10:05:13.000     0  0.499013  0.526048  0.041826  0.048233   \n",
       "2017-06-26 10:07:45.000     0  0.511669  0.497110  0.071646  0.030075   \n",
       "2017-06-26 10:30:09.000     0  0.706094  0.323853  0.037216  0.047758   \n",
       "2017-06-26 10:32:03.500     0  0.652999  0.351288  0.072727  0.039289   \n",
       "2017-06-26 10:34:42.500     0  0.530319  0.470023  0.029784  0.094017   \n",
       "2017-06-26 10:36:59.500     0  0.529754  0.474854  0.040195  0.087625   \n",
       "2017-06-26 10:42:23.000     0  0.561922  0.441630  0.037123  0.051994   \n",
       "2017-06-26 10:50:29.500     0  0.530555  0.469531  0.071444  0.056124   \n",
       "2017-06-26 10:56:30.000     0  0.521486  0.482222  0.089390  0.067446   \n",
       "2017-06-26 11:00:12.500     0  0.588676  0.434642  0.084963  0.057525   \n",
       "2017-06-26 11:01:17.000     0  0.734661  0.336664  0.030809  0.058337   \n",
       "2017-06-26 11:02:24.000     0  0.534962  0.471044  0.059242  0.046201   \n",
       "2017-06-26 11:07:02.000     0  0.409582  0.590504  0.012198  0.102315   \n",
       "2017-06-26 11:08:03.000     0  0.481112  0.544028  0.053952  0.025555   \n",
       "2017-06-26 11:09:02.000     0  0.526817  0.480030  0.020102  0.177471   \n",
       "2017-06-26 11:09:31.000     0  0.431009  0.608259  0.040292  0.056025   \n",
       "2017-06-26 11:10:13.000     0  0.544787  0.476346  0.087031  0.045773   \n",
       "2017-06-26 11:10:54.000     0  0.609749  0.405728  0.100195  0.040056   \n",
       "2017-06-26 11:11:55.500     0  0.535274  0.470670  0.054951  0.076080   \n",
       "...                       ...       ...       ...       ...       ...   \n",
       "2018-03-29 11:08:10.500     0  0.501179  0.527495  0.076589  0.059234   \n",
       "2018-03-29 11:12:00.000     0  0.571316  0.428868  0.049391  0.074132   \n",
       "2018-03-29 11:16:06.000     0  0.484426  0.517985  0.191805  0.090387   \n",
       "2018-03-29 11:20:56.000     0  0.595400  0.448491  0.064734  0.063057   \n",
       "2018-03-29 11:22:11.000     0  0.500803  0.505591  0.109618  0.052122   \n",
       "2018-03-29 11:24:59.000     0  0.468516  0.533429  0.082785  0.102139   \n",
       "2018-03-29 11:28:20.000     0  0.398488  0.606019  0.084609  0.121608   \n",
       "2018-03-29 13:30:16.000     0  0.524443  0.508837  0.059697  0.098242   \n",
       "2018-03-29 13:31:39.000     0  0.494151  0.543224  0.055777  0.163053   \n",
       "2018-03-29 13:34:27.000     0  0.529305  0.479719  0.053354  0.147316   \n",
       "2018-03-29 13:36:40.500     0  0.447322  0.554445  0.100660  0.060257   \n",
       "2018-03-29 13:39:05.500     0  0.549230  0.463318  0.052375  0.046845   \n",
       "2018-03-29 13:42:47.000     0  0.456006  0.556398  0.042616  0.011608   \n",
       "2018-03-29 13:43:40.000     0  0.603173  0.551112  0.102223  0.069780   \n",
       "2018-03-29 13:44:25.000     0  0.448482  0.562118  0.144242  0.043736   \n",
       "2018-03-29 13:46:04.000     0  0.380695  0.657784  0.018588  0.068760   \n",
       "2018-03-29 13:46:48.500     0  0.554512  0.455911  0.107663  0.192037   \n",
       "2018-03-29 13:48:57.500     0  0.526508  0.488291  0.123267  0.118162   \n",
       "2018-03-29 13:51:37.500     0  0.569247  0.459602  0.098048  0.076695   \n",
       "2018-03-29 13:55:03.500     0  0.686056  0.323369  0.056320  0.073184   \n",
       "2018-03-29 13:58:39.000     0  0.481132  0.522660  0.032973  0.074408   \n",
       "2018-03-29 14:01:12.000     0  0.575698  0.436479  0.107459  0.067263   \n",
       "2018-03-29 14:06:21.500     0  0.532056  0.504019  0.058785  0.102009   \n",
       "2018-03-29 14:11:54.500     0  0.470855  0.530440  0.073695  0.090581   \n",
       "2018-03-29 14:17:02.000     0  0.488971  0.515644  0.055053  0.070697   \n",
       "2018-03-29 14:24:33.500     0  0.490603  0.514304  0.101009  0.084529   \n",
       "2018-03-29 14:32:16.500     0  0.570368  0.430186  0.086798  0.083934   \n",
       "2018-03-29 14:39:03.000     0  0.558384  0.449685  0.068911  0.033435   \n",
       "2018-03-29 14:41:07.000     0  0.485114  0.518715  0.066916  0.155610   \n",
       "2018-03-29 14:43:37.500     0  0.452129  0.630905  0.107430  0.189495   \n",
       "\n",
       "                             f004      f005      f006      f007      f008  \\\n",
       "start_time                                                                  \n",
       "2017-06-26 09:28:27.500  0.000439 -0.187917  0.045226 -0.060302  0.955379   \n",
       "2017-06-26 09:30:07.000 -0.017165 -0.027069 -0.003067  0.004601  0.931486   \n",
       "2017-06-26 09:32:50.000 -0.042082 -0.040098 -0.017241  0.079310  0.878137   \n",
       "2017-06-26 09:35:15.000 -0.023043 -0.047867  0.001484 -0.103858  0.965186   \n",
       "2017-06-26 09:40:52.000  0.064315 -0.092178  0.014052 -0.080796  0.909392   \n",
       "2017-06-26 09:44:25.500 -0.011376 -0.045159  0.002174 -0.044565  0.947866   \n",
       "2017-06-26 09:48:15.500 -0.044898 -0.001190 -0.017065  0.051195  0.956583   \n",
       "2017-06-26 09:50:42.000 -0.095700  0.030078 -0.003328 -0.103993  0.963818   \n",
       "2017-06-26 09:55:42.500 -0.136553  0.078886 -0.022436  0.008013  0.977124   \n",
       "2017-06-26 09:58:18.500  0.001383 -0.065603  0.003906  0.001953  0.961195   \n",
       "2017-06-26 10:00:26.500 -0.117430  0.006364 -0.012216 -0.064572  0.872153   \n",
       "2017-06-26 10:05:13.000 -0.040531 -0.022581  0.006579 -0.074013  0.958761   \n",
       "2017-06-26 10:07:45.000  0.028619 -0.091377  0.004499 -0.169854  0.927673   \n",
       "2017-06-26 10:30:09.000  0.084516 -0.103375  0.017467  0.034934  1.031603   \n",
       "2017-06-26 10:32:03.500 -0.008102 -0.099348  0.009434  0.025157  0.896875   \n",
       "2017-06-26 10:34:42.500 -0.006248 -0.018508 -0.014599 -0.009124  0.967121   \n",
       "2017-06-26 10:36:59.500 -0.025943 -0.022692  0.003091 -0.116692  0.934929   \n",
       "2017-06-26 10:42:23.000 -0.037616  0.019279 -0.003083 -0.204008  0.854043   \n",
       "2017-06-26 10:50:29.500 -0.066095  0.011362 -0.002774 -0.162968  0.940344   \n",
       "2017-06-26 10:56:30.000 -0.013764 -0.055318  0.006742 -0.162921  0.989176   \n",
       "2017-06-26 11:00:12.500  0.044938 -0.112877  0.038760  0.054264  0.960118   \n",
       "2017-06-26 11:01:17.000 -0.023221 -0.131106 -0.007463  0.033582  0.928092   \n",
       "2017-06-26 11:02:24.000 -0.015422 -0.019733 -0.005396 -0.138489  0.909334   \n",
       "2017-06-26 11:07:02.000 -0.192776  0.060214 -0.098361 -0.053279  0.930550   \n",
       "2017-06-26 11:08:03.000  0.018975  0.003891  0.016949  0.016949  1.033411   \n",
       "2017-06-26 11:09:02.000 -0.135775  0.035156 -0.103448  0.172414  0.951960   \n",
       "2017-06-26 11:09:31.000 -0.055851 -0.052735  0.035714  0.136905  1.011037   \n",
       "2017-06-26 11:10:13.000  0.021374 -0.042288  0.085366  0.042683  1.032398   \n",
       "2017-06-26 11:10:54.000  0.001340 -0.014181  0.048780  0.048780  1.028527   \n",
       "2017-06-26 11:11:55.500 -0.034515 -0.031977 -0.002933  0.024927  0.970747   \n",
       "...                           ...       ...       ...       ...       ...   \n",
       "2018-03-29 11:08:10.500 -0.094746  0.030607 -0.006536 -0.110022  0.902228   \n",
       "2018-03-29 11:12:00.000 -0.070532  0.004524 -0.016260 -0.166667  0.934344   \n",
       "2018-03-29 11:16:06.000 -0.008251 -0.072356  0.013793 -0.190517  0.873608   \n",
       "2018-03-29 11:20:56.000 -0.003162 -0.178773  0.006667  0.053333  0.997003   \n",
       "2018-03-29 11:22:11.000 -0.057126 -0.081403  0.005952 -0.142857  0.979930   \n",
       "2018-03-29 11:24:59.000 -0.096352  0.030744 -0.007463 -0.134328  0.890947   \n",
       "2018-03-29 11:28:20.000 -0.069913 -0.014743  0.008584 -0.077253  1.049610   \n",
       "2018-03-29 13:30:16.000 -0.026276 -0.067829 -0.024096  0.033133  1.006297   \n",
       "2018-03-29 13:31:39.000 -0.041988 -0.042986 -0.002976 -0.139881  0.964911   \n",
       "2018-03-29 13:34:27.000 -0.089404 -0.076240 -0.037453 -0.116105  0.991138   \n",
       "2018-03-29 13:36:40.500 -0.002650 -0.044774  0.027586 -0.100000  0.956812   \n",
       "2018-03-29 13:39:05.500  0.015847 -0.067432 -0.002257 -0.141084  0.944757   \n",
       "2018-03-29 13:42:47.000  0.017068 -0.180271  0.056604  0.028302  0.942038   \n",
       "2018-03-29 13:43:40.000 -0.057097 -0.136282 -0.033333  0.111111  1.038609   \n",
       "2018-03-29 13:44:25.000 -0.085323 -0.036430  0.015152 -0.042929  0.999348   \n",
       "2018-03-29 13:46:04.000 -0.051710  0.063542 -0.022472 -0.073034  0.998990   \n",
       "2018-03-29 13:46:48.500  0.031177 -0.045497  0.000000 -0.110465  0.977789   \n",
       "2018-03-29 13:48:57.500 -0.066130 -0.030489  0.006250 -0.081250  0.921441   \n",
       "2018-03-29 13:51:37.500 -0.092439 -0.017960  0.004854 -0.125000  0.996968   \n",
       "2018-03-29 13:55:03.500 -0.116383 -0.014045 -0.006961 -0.184455  0.884305   \n",
       "2018-03-29 13:58:39.000 -0.185026  0.037135 -0.019608 -0.070261  0.969222   \n",
       "2018-03-29 14:01:12.000 -0.043669 -0.002426  0.001616 -0.165590  0.855447   \n",
       "2018-03-29 14:06:21.500 -0.085935  0.007150 -0.009050 -0.160633  0.909137   \n",
       "2018-03-29 14:11:54.500 -0.016886 -0.090859  0.006504 -0.222764  0.878624   \n",
       "2018-03-29 14:17:02.000  0.011214 -0.063036 -0.001112 -0.190211  0.814158   \n",
       "2018-03-29 14:24:33.500 -0.037728 -0.035136  0.002165 -0.204004  0.846031   \n",
       "2018-03-29 14:32:16.500 -0.018339 -0.074002  0.008653 -0.203337  0.862856   \n",
       "2018-03-29 14:39:03.000  0.008718 -0.022816  0.008065 -0.094758  0.926228   \n",
       "2018-03-29 14:41:07.000 -0.057534 -0.033218 -0.006645 -0.156146  1.007434   \n",
       "2018-03-29 14:43:37.500  0.015035 -0.141915  0.011299 -0.098870  0.932201   \n",
       "\n",
       "                         ...      f149      f150      f151      f152  \\\n",
       "start_time               ...                                           \n",
       "2017-06-26 09:28:27.500  ...  0.656566  0.575758 -0.001158 -0.207547   \n",
       "2017-06-26 09:30:07.000  ...  0.616162  0.575758 -0.000108 -0.021277   \n",
       "2017-06-26 09:32:50.000  ...  0.191919  0.575758  0.000000  0.000000   \n",
       "2017-06-26 09:35:15.000  ...  0.373737  0.595960 -0.000947 -0.173077   \n",
       "2017-06-26 09:40:52.000  ...  0.757576  0.616162 -0.000505 -0.096154   \n",
       "2017-06-26 09:44:25.500  ...  0.919192  0.656566  0.000101  0.020408   \n",
       "2017-06-26 09:48:15.500  ...  0.757576  0.696970  0.002143  0.545455   \n",
       "2017-06-26 09:50:42.000  ...  0.555556  0.696970  0.003784  1.217391   \n",
       "2017-06-26 09:55:42.500  ...  0.070707  0.656566  0.001944  0.482759   \n",
       "2017-06-26 09:58:18.500  ...  0.393939  0.676768  0.001622  0.387097   \n",
       "2017-06-26 10:00:26.500  ... -0.090909  0.595960  0.002286  0.592593   \n",
       "2017-06-26 10:05:13.000  ...  0.111111  0.555556  0.001781  0.433333   \n",
       "2017-06-26 10:07:45.000  ...  0.595960  0.595960  0.001892  0.466667   \n",
       "2017-06-26 10:30:09.000  ...  0.747475  0.696970  0.002593  0.700000   \n",
       "2017-06-26 10:32:03.500  ...  0.858586  0.717172  0.002500  0.666667   \n",
       "2017-06-26 10:34:42.500  ...  0.636364  0.717172  0.002000  0.500000   \n",
       "2017-06-26 10:36:59.500  ...  0.797980  0.757576  0.001282  0.294118   \n",
       "2017-06-26 10:42:23.000  ...  0.737374  0.777778  0.000811  0.176471   \n",
       "2017-06-26 10:50:29.500  ...  0.484848  0.777778  0.000526  0.111111   \n",
       "2017-06-26 10:56:30.000  ...  0.898990  0.818182  0.000526  0.111111   \n",
       "2017-06-26 11:00:12.500  ...  1.000000  0.898990  0.000649  0.138889   \n",
       "2017-06-26 11:01:17.000  ...  0.939394  1.000000  0.000526  0.111111   \n",
       "2017-06-26 11:02:24.000  ...  0.898990  1.000000  0.000256  0.052632   \n",
       "2017-06-26 11:07:02.000  ...  0.252525  0.979798 -0.000361 -0.069767   \n",
       "2017-06-26 11:08:03.000  ...  0.494949  0.959596 -0.002414 -0.388889   \n",
       "2017-06-26 11:09:02.000  ... -0.333333  0.818182 -0.003258 -0.491525   \n",
       "2017-06-26 11:09:31.000  ... -0.090909  0.777778 -0.003895 -0.560606   \n",
       "2017-06-26 11:10:13.000  ...  0.656566  0.777778 -0.002115 -0.349206   \n",
       "2017-06-26 11:10:54.000  ...  0.838384  0.878788 -0.000654 -0.122807   \n",
       "2017-06-26 11:11:55.500  ...  0.636364  0.878788  0.000000  0.000000   \n",
       "...                      ...       ...       ...       ...       ...   \n",
       "2018-03-29 11:08:10.500  ...  0.919192  0.616162  0.001250  0.285714   \n",
       "2018-03-29 11:12:00.000  ...  0.797980  0.636364  0.000746  0.161290   \n",
       "2018-03-29 11:16:06.000  ...  0.969697  0.676768  0.001515  0.357143   \n",
       "2018-03-29 11:20:56.000  ...  0.979798  0.717172  0.002500  0.666667   \n",
       "2018-03-29 11:22:11.000  ...  1.000000  0.777778  0.003429  1.043478   \n",
       "2018-03-29 11:24:59.000  ...  0.979798  0.818182  0.003824  1.238095   \n",
       "2018-03-29 11:28:20.000  ...  1.000000  0.878788  0.003030  0.869565   \n",
       "2018-03-29 13:30:16.000  ...  0.979798  1.000000  0.004000  1.333333   \n",
       "2018-03-29 13:31:39.000  ...  0.939394  1.000000  0.004000  1.333333   \n",
       "2018-03-29 13:34:27.000  ...  0.777778  0.979798  0.002877  0.807692   \n",
       "2018-03-29 13:36:40.500  ...  0.939394  1.000000  0.001765  0.428571   \n",
       "2018-03-29 13:39:05.500  ...  0.929293  1.000000  0.002113  0.535714   \n",
       "2018-03-29 13:42:47.000  ...  0.979798  1.000000  0.002800  0.777778   \n",
       "2018-03-29 13:43:40.000  ...  0.919192  1.000000  0.002703  0.740741   \n",
       "2018-03-29 13:44:25.000  ...  1.000000  1.000000  0.002208  0.566667   \n",
       "2018-03-29 13:46:04.000  ...  0.838384  1.000000  0.003165  0.925926   \n",
       "2018-03-29 13:46:48.500  ...  0.939394  1.000000  0.002048  0.515152   \n",
       "2018-03-29 13:48:57.500  ...  0.939394  1.000000  0.001852  0.454545   \n",
       "2018-03-29 13:51:37.500  ...  0.979798  1.000000  0.001852  0.454545   \n",
       "2018-03-29 13:55:03.500  ...  0.919192  1.000000  0.001053  0.235294   \n",
       "2018-03-29 13:58:39.000  ...  0.737374  0.979798  0.000769  0.166667   \n",
       "2018-03-29 14:01:12.000  ...  0.818182  1.000000  0.001351  0.312500   \n",
       "2018-03-29 14:06:21.500  ...  0.616162  0.939394  0.000811  0.176471   \n",
       "2018-03-29 14:11:54.500  ...  0.696970  0.919192 -0.000149 -0.029412   \n",
       "2018-03-29 14:17:02.000  ...  0.515152  0.878788 -0.000149 -0.029412   \n",
       "2018-03-29 14:24:33.500  ...  0.575758  0.858586 -0.000294 -0.057143   \n",
       "2018-03-29 14:32:16.500  ...  0.696970  0.858586  0.000294  0.060606   \n",
       "2018-03-29 14:39:03.000  ...  0.878788  0.878788  0.000149  0.030303   \n",
       "2018-03-29 14:41:07.000  ...  0.525253  0.838384  0.000000  0.000000   \n",
       "2018-03-29 14:43:37.500  ...  0.666667  0.838384 -0.000145 -0.028571   \n",
       "\n",
       "                             f153      f154      f155      f156      f157  \\\n",
       "start_time                                                                  \n",
       "2017-06-26 09:28:27.500 -0.464131 -0.240405  0.401316  0.280108  0.643732   \n",
       "2017-06-26 09:30:07.000 -0.361604 -0.177121  0.463158  0.294303  0.800867   \n",
       "2017-06-26 09:32:50.000 -0.262342 -0.118634  0.508480  0.316690  0.892058   \n",
       "2017-06-26 09:35:15.000 -0.191958 -0.077510  0.548684  0.347634  0.950785   \n",
       "2017-06-26 09:40:52.000 -0.121411 -0.038361  0.592544  0.385207  1.007219   \n",
       "2017-06-26 09:44:25.500 -0.044430  0.002047  0.639181  0.429137  1.059271   \n",
       "2017-06-26 09:48:15.500  0.071458  0.059889  0.666667  0.472624  1.054753   \n",
       "2017-06-26 09:50:42.000  0.190461  0.116396  0.680702  0.512529  1.017048   \n",
       "2017-06-26 09:55:42.500  0.232157  0.131424  0.686952  0.551670  0.957515   \n",
       "2017-06-26 09:58:18.500  0.299764  0.158656  0.702083  0.588977  0.928297   \n",
       "2017-06-26 10:00:26.500  0.312833  0.157258  0.670833  0.615928  0.780643   \n",
       "2017-06-26 10:05:13.000  0.329078  0.157517  0.645833  0.634196  0.669108   \n",
       "2017-06-26 10:07:45.000  0.367862  0.169034  0.653472  0.648695  0.663026   \n",
       "2017-06-26 10:30:09.000  0.429099  0.191201  0.670833  0.660910  0.690680   \n",
       "2017-06-26 10:32:03.500  0.448469  0.191325  0.678125  0.669468  0.695439   \n",
       "2017-06-26 10:34:42.500  0.451749  0.183399  0.673264  0.672876  0.674039   \n",
       "2017-06-26 10:36:59.500  0.412854  0.154782  0.685417  0.674751  0.706747   \n",
       "2017-06-26 10:42:23.000  0.361070  0.121151  0.696875  0.676369  0.737887   \n",
       "2017-06-26 10:50:29.500  0.361057  0.115087  0.709375  0.678611  0.770903   \n",
       "2017-06-26 10:56:30.000  0.344832  0.101220  0.728125  0.681215  0.821944   \n",
       "2017-06-26 11:00:12.500  0.322078  0.084782  0.784375  0.692569  0.967986   \n",
       "2017-06-26 11:01:17.000  0.296178  0.067593  0.824030  0.710389  1.051312   \n",
       "2017-06-26 11:02:24.000  0.260588  0.046418  0.834591  0.728501  1.046770   \n",
       "2017-06-26 11:07:02.000  0.176875  0.002241  0.785483  0.739966  0.876518   \n",
       "2017-06-26 11:08:03.000  0.083769 -0.044424  0.709144  0.743068  0.641297   \n",
       "2017-06-26 11:09:02.000 -0.028484 -0.098329  0.627894  0.738531  0.406620   \n",
       "2017-06-26 11:09:31.000 -0.115171 -0.136756  0.557805  0.725770  0.221875   \n",
       "2017-06-26 11:10:13.000 -0.150493 -0.147580  0.526108  0.708693  0.160939   \n",
       "2017-06-26 11:10:54.000 -0.144059 -0.136984  0.521644  0.689920  0.185092   \n",
       "2017-06-26 11:11:55.500 -0.166569 -0.141390  0.497537  0.666861  0.158889   \n",
       "...                           ...       ...       ...       ...       ...   \n",
       "2018-03-29 11:08:10.500  0.095856 -0.015026  0.828974  0.595049  1.296826   \n",
       "2018-03-29 11:12:00.000  0.142368  0.008981  0.828308  0.640367  1.204190   \n",
       "2018-03-29 11:16:06.000  0.215042  0.044869  0.846308  0.684423  1.170077   \n",
       "2018-03-29 11:20:56.000  0.270343  0.070276  0.858186  0.725995  1.122569   \n",
       "2018-03-29 11:22:11.000  0.360732  0.111957  0.882186  0.763849  1.118861   \n",
       "2018-03-29 11:24:59.000  0.439345  0.145666  0.895901  0.798467  1.090768   \n",
       "2018-03-29 11:28:20.000  0.512184  0.174802  0.895901  0.826048  1.035606   \n",
       "2018-03-29 13:30:16.000  0.590771  0.205355  0.889704  0.846742  0.975629   \n",
       "2018-03-29 13:31:39.000  0.637369  0.218386  0.870260  0.859337  0.892105   \n",
       "2018-03-29 13:34:27.000  0.634512  0.206038  0.840815  0.863654  0.795137   \n",
       "2018-03-29 13:36:40.500  0.657754  0.207358  0.849149  0.865672  0.816102   \n",
       "2018-03-29 13:39:05.500  0.675089  0.205657  0.877482  0.870589  0.891267   \n",
       "2018-03-29 13:42:47.000  0.683624  0.199642  0.887482  0.874707  0.913033   \n",
       "2018-03-29 13:43:40.000  0.674719  0.185207  0.885650  0.877453  0.902043   \n",
       "2018-03-29 13:44:25.000  0.645228  0.161201  0.883324  0.877567  0.894839   \n",
       "2018-03-29 13:46:04.000  0.627536  0.144295  0.876333  0.875610  0.877780   \n",
       "2018-03-29 13:46:48.500  0.580732  0.113679  0.857184  0.871738  0.828076   \n",
       "2018-03-29 13:48:57.500  0.551378  0.093317  0.854052  0.868173  0.825810   \n",
       "2018-03-29 13:51:37.500  0.530793  0.078359  0.860163  0.867163  0.846162   \n",
       "2018-03-29 13:55:03.500  0.524786  0.071438  0.877853  0.870867  0.891825   \n",
       "2018-03-29 13:58:39.000  0.489824  0.050385  0.850075  0.870960  0.808306   \n",
       "2018-03-29 14:01:12.000  0.486864  0.046386  0.827853  0.865997  0.751565   \n",
       "2018-03-29 14:06:21.500  0.431710  0.016489  0.780794  0.855328  0.631726   \n",
       "2018-03-29 14:11:54.500  0.391061 -0.004660  0.756512  0.842414  0.584708   \n",
       "2018-03-29 14:17:02.000  0.315683 -0.042116  0.717662  0.825848  0.501288   \n",
       "2018-03-29 14:24:33.500  0.269222 -0.063240  0.698313  0.808046  0.478847   \n",
       "2018-03-29 14:32:16.500  0.237348 -0.076016  0.687462  0.791074  0.480238   \n",
       "2018-03-29 14:39:03.000  0.225683 -0.078047  0.691150  0.774784  0.523882   \n",
       "2018-03-29 14:41:07.000  0.199641 -0.087166  0.661150  0.754882  0.473685   \n",
       "2018-03-29 14:43:37.500  0.208379 -0.078438  0.649571  0.732054  0.484605   \n",
       "\n",
       "                             f158  \n",
       "start_time                         \n",
       "2017-06-26 09:28:27.500  0.812500  \n",
       "2017-06-26 09:30:07.000  0.828829  \n",
       "2017-06-26 09:32:50.000  0.819820  \n",
       "2017-06-26 09:35:15.000  0.801802  \n",
       "2017-06-26 09:40:52.000  0.870370  \n",
       "2017-06-26 09:44:25.500  0.854545  \n",
       "2017-06-26 09:48:15.500  0.821429  \n",
       "2017-06-26 09:50:42.000  0.812500  \n",
       "2017-06-26 09:55:42.500  0.796460  \n",
       "2017-06-26 09:58:18.500  0.836364  \n",
       "2017-06-26 10:00:26.500  0.853211  \n",
       "2017-06-26 10:05:13.000  0.871560  \n",
       "2017-06-26 10:07:45.000  0.942857  \n",
       "2017-06-26 10:30:09.000  0.915094  \n",
       "2017-06-26 10:32:03.500  0.951923  \n",
       "2017-06-26 10:34:42.500  0.925926  \n",
       "2017-06-26 10:36:59.500  1.095745  \n",
       "2017-06-26 10:42:23.000  1.107527  \n",
       "2017-06-26 10:50:29.500  0.910000  \n",
       "2017-06-26 10:56:30.000  1.000000  \n",
       "2017-06-26 11:00:12.500  1.064516  \n",
       "2017-06-26 11:01:17.000  0.989583  \n",
       "2017-06-26 11:02:24.000  0.909091  \n",
       "2017-06-26 11:07:02.000  0.756757  \n",
       "2017-06-26 11:08:03.000  0.718182  \n",
       "2017-06-26 11:09:02.000  0.689655  \n",
       "2017-06-26 11:09:31.000  0.692308  \n",
       "2017-06-26 11:10:13.000  0.769231  \n",
       "2017-06-26 11:10:54.000  0.834783  \n",
       "2017-06-26 11:11:55.500  0.857143  \n",
       "...                           ...  \n",
       "2018-03-29 11:08:10.500  0.978947  \n",
       "2018-03-29 11:12:00.000  0.900000  \n",
       "2018-03-29 11:16:06.000  0.940000  \n",
       "2018-03-29 11:20:56.000  1.053191  \n",
       "2018-03-29 11:22:11.000  1.086022  \n",
       "2018-03-29 11:24:59.000  0.940594  \n",
       "2018-03-29 11:28:20.000  0.950980  \n",
       "2018-03-29 13:30:16.000  0.903846  \n",
       "2018-03-29 13:31:39.000  0.941748  \n",
       "2018-03-29 13:34:27.000  0.932039  \n",
       "2018-03-29 13:36:40.500  1.029412  \n",
       "2018-03-29 13:39:05.500  1.070000  \n",
       "2018-03-29 13:42:47.000  1.166667  \n",
       "2018-03-29 13:43:40.000  1.164948  \n",
       "2018-03-29 13:44:25.000  1.185567  \n",
       "2018-03-29 13:46:04.000  1.147059  \n",
       "2018-03-29 13:46:48.500  1.145631  \n",
       "2018-03-29 13:48:57.500  1.210000  \n",
       "2018-03-29 13:51:37.500  1.291667  \n",
       "2018-03-29 13:55:03.500  1.305263  \n",
       "2018-03-29 13:58:39.000  1.176471  \n",
       "2018-03-29 14:01:12.000  1.176471  \n",
       "2018-03-29 14:06:21.500  1.134615  \n",
       "2018-03-29 14:11:54.500  1.155340  \n",
       "2018-03-29 14:17:02.000  1.165049  \n",
       "2018-03-29 14:24:33.500  1.178218  \n",
       "2018-03-29 14:32:16.500  1.181818  \n",
       "2018-03-29 14:39:03.000  1.247423  \n",
       "2018-03-29 14:41:07.000  1.166667  \n",
       "2018-03-29 14:43:37.500  1.155340  \n",
       "\n",
       "[24855 rows x 176 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
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