OLS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy import cumsum, log, polyfit, sqrt, std, subtract\n",
    "from numpy.random import randn\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import statsmodels.tsa.stattools as ts\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from johansen import Johansen\n",
    "import itertools\n",
    "import os\n",
    "\n",
    "#%matplotlib inline\n",
    "plt.style.use('seaborn-darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#               UTB,       TB,        U10y,      10y,       5y,        2y\n",
    "bonds =        ['UB',      'ZB',      'TN',       'ZN',     'ZF',      'ZT']\n",
    "tick_sizes =   [0.03125,   0.03125,   0.015625,   0.015625, 0.0078125, 0.0078125]\n",
    "point_values = [1000,      1000,      1000,       1000,     1000,      2000]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_hdf('ALL.h5','table')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UB</th>\n",
       "      <th>ZB</th>\n",
       "      <th>TN</th>\n",
       "      <th>ZN</th>\n",
       "      <th>ZF</th>\n",
       "      <th>ZT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-01 15:50:00-06:00</th>\n",
       "      <td>165.531250</td>\n",
       "      <td>151.296875</td>\n",
       "      <td>132.562500</td>\n",
       "      <td>123.492188</td>\n",
       "      <td>115.933594</td>\n",
       "      <td>106.828125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01 15:51:00-06:00</th>\n",
       "      <td>165.515625</td>\n",
       "      <td>151.265625</td>\n",
       "      <td>132.562500</td>\n",
       "      <td>123.492188</td>\n",
       "      <td>115.933594</td>\n",
       "      <td>106.828125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01 15:52:00-06:00</th>\n",
       "      <td>165.531250</td>\n",
       "      <td>151.296875</td>\n",
       "      <td>132.562500</td>\n",
       "      <td>123.507812</td>\n",
       "      <td>115.933594</td>\n",
       "      <td>106.828125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01 15:53:00-06:00</th>\n",
       "      <td>165.531250</td>\n",
       "      <td>151.296875</td>\n",
       "      <td>132.562500</td>\n",
       "      <td>123.507812</td>\n",
       "      <td>115.933594</td>\n",
       "      <td>106.828125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01 15:54:00-06:00</th>\n",
       "      <td>165.468750</td>\n",
       "      <td>151.265625</td>\n",
       "      <td>132.562500</td>\n",
       "      <td>123.492188</td>\n",
       "      <td>115.933594</td>\n",
       "      <td>106.828125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01 15:55:00-06:00</th>\n",
       "      <td>165.468750</td>\n",
       "      <td>151.265625</td>\n",
       "      <td>132.546875</td>\n",
       "      <td>123.492188</td>\n",
       "      <td>115.933594</td>\n",
       "      <td>106.828125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01 15:56:00-06:00</th>\n",
       "      <td>165.406250</td>\n",
       "      <td>151.234375</td>\n",
       "      <td>132.562500</td>\n",
       "      <td>123.492188</td>\n",
       "      <td>115.925781</td>\n",
       "      <td>106.828125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01 15:57:00-06:00</th>\n",
       "      <td>165.453125</td>\n",
       "      <td>151.234375</td>\n",
       "      <td>132.562500</td>\n",
       "      <td>123.492188</td>\n",
       "      <td>115.933594</td>\n",
       "      <td>106.828125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01 15:58:00-06:00</th>\n",
       "      <td>165.437500</td>\n",
       "      <td>151.265625</td>\n",
       "      <td>132.562500</td>\n",
       "      <td>123.492188</td>\n",
       "      <td>115.933594</td>\n",
       "      <td>106.828125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01 15:59:00-06:00</th>\n",
       "      <td>165.437500</td>\n",
       "      <td>151.265625</td>\n",
       "      <td>132.562500</td>\n",
       "      <td>123.492188</td>\n",
       "      <td>115.941406</td>\n",
       "      <td>106.828125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-03 17:00:00-06:00</th>\n",
       "      <td>164.671875</td>\n",
       "      <td>150.593750</td>\n",
       "      <td>132.289062</td>\n",
       "      <td>123.062500</td>\n",
       "      <td>115.710938</td>\n",
       "      <td>106.796875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-03 17:01:00-06:00</th>\n",
       "      <td>164.109375</td>\n",
       "      <td>150.390625</td>\n",
       "      <td>131.992188</td>\n",
       "      <td>123.085938</td>\n",
       "      <td>115.695312</td>\n",
       "      <td>106.765625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-03 17:02:00-06:00</th>\n",
       "      <td>164.015625</td>\n",
       "      <td>150.328125</td>\n",
       "      <td>131.921875</td>\n",
       "      <td>123.054688</td>\n",
       "      <td>115.699219</td>\n",
       "      <td>106.761719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-03 17:03:00-06:00</th>\n",
       "      <td>164.093750</td>\n",
       "      <td>150.328125</td>\n",
       "      <td>131.937500</td>\n",
       "      <td>123.070312</td>\n",
       "      <td>115.707031</td>\n",
       "      <td>106.757812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-03 17:04:00-06:00</th>\n",
       "      <td>164.125000</td>\n",
       "      <td>150.328125</td>\n",
       "      <td>131.937500</td>\n",
       "      <td>123.070312</td>\n",
       "      <td>115.707031</td>\n",
       "      <td>106.765625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-03 17:05:00-06:00</th>\n",
       "      <td>164.171875</td>\n",
       "      <td>150.359375</td>\n",
       "      <td>131.937500</td>\n",
       "      <td>123.070312</td>\n",
       "      <td>115.699219</td>\n",
       "      <td>106.757812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-03 17:06:00-06:00</th>\n",
       "      <td>164.125000</td>\n",
       "      <td>150.359375</td>\n",
       "      <td>131.953125</td>\n",
       "      <td>123.070312</td>\n",
       "      <td>115.691406</td>\n",
       "      <td>106.757812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-03 17:07:00-06:00</th>\n",
       "      <td>164.093750</td>\n",
       "      <td>150.296875</td>\n",
       "      <td>131.921875</td>\n",
       "      <td>123.054688</td>\n",
       "      <td>115.683594</td>\n",
       "      <td>106.757812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-03 17:08:00-06:00</th>\n",
       "      <td>164.156250</td>\n",
       "      <td>150.359375</td>\n",
       "      <td>131.945312</td>\n",
       "      <td>123.070312</td>\n",
       "      <td>115.691406</td>\n",
       "      <td>106.757812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-03 17:09:00-06:00</th>\n",
       "      <td>164.062500</td>\n",
       "      <td>150.296875</td>\n",
       "      <td>131.921875</td>\n",
       "      <td>123.070312</td>\n",
       "      <td>115.691406</td>\n",
       "      <td>106.757812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   UB          ZB          TN          ZN  \\\n",
       "timestamp                                                                   \n",
       "2017-12-01 15:50:00-06:00  165.531250  151.296875  132.562500  123.492188   \n",
       "2017-12-01 15:51:00-06:00  165.515625  151.265625  132.562500  123.492188   \n",
       "2017-12-01 15:52:00-06:00  165.531250  151.296875  132.562500  123.507812   \n",
       "2017-12-01 15:53:00-06:00  165.531250  151.296875  132.562500  123.507812   \n",
       "2017-12-01 15:54:00-06:00  165.468750  151.265625  132.562500  123.492188   \n",
       "2017-12-01 15:55:00-06:00  165.468750  151.265625  132.546875  123.492188   \n",
       "2017-12-01 15:56:00-06:00  165.406250  151.234375  132.562500  123.492188   \n",
       "2017-12-01 15:57:00-06:00  165.453125  151.234375  132.562500  123.492188   \n",
       "2017-12-01 15:58:00-06:00  165.437500  151.265625  132.562500  123.492188   \n",
       "2017-12-01 15:59:00-06:00  165.437500  151.265625  132.562500  123.492188   \n",
       "2017-12-03 17:00:00-06:00  164.671875  150.593750  132.289062  123.062500   \n",
       "2017-12-03 17:01:00-06:00  164.109375  150.390625  131.992188  123.085938   \n",
       "2017-12-03 17:02:00-06:00  164.015625  150.328125  131.921875  123.054688   \n",
       "2017-12-03 17:03:00-06:00  164.093750  150.328125  131.937500  123.070312   \n",
       "2017-12-03 17:04:00-06:00  164.125000  150.328125  131.937500  123.070312   \n",
       "2017-12-03 17:05:00-06:00  164.171875  150.359375  131.937500  123.070312   \n",
       "2017-12-03 17:06:00-06:00  164.125000  150.359375  131.953125  123.070312   \n",
       "2017-12-03 17:07:00-06:00  164.093750  150.296875  131.921875  123.054688   \n",
       "2017-12-03 17:08:00-06:00  164.156250  150.359375  131.945312  123.070312   \n",
       "2017-12-03 17:09:00-06:00  164.062500  150.296875  131.921875  123.070312   \n",
       "\n",
       "                                   ZF          ZT  \n",
       "timestamp                                          \n",
       "2017-12-01 15:50:00-06:00  115.933594  106.828125  \n",
       "2017-12-01 15:51:00-06:00  115.933594  106.828125  \n",
       "2017-12-01 15:52:00-06:00  115.933594  106.828125  \n",
       "2017-12-01 15:53:00-06:00  115.933594  106.828125  \n",
       "2017-12-01 15:54:00-06:00  115.933594  106.828125  \n",
       "2017-12-01 15:55:00-06:00  115.933594  106.828125  \n",
       "2017-12-01 15:56:00-06:00  115.925781  106.828125  \n",
       "2017-12-01 15:57:00-06:00  115.933594  106.828125  \n",
       "2017-12-01 15:58:00-06:00  115.933594  106.828125  \n",
       "2017-12-01 15:59:00-06:00  115.941406  106.828125  \n",
       "2017-12-03 17:00:00-06:00  115.710938  106.796875  \n",
       "2017-12-03 17:01:00-06:00  115.695312  106.765625  \n",
       "2017-12-03 17:02:00-06:00  115.699219  106.761719  \n",
       "2017-12-03 17:03:00-06:00  115.707031  106.757812  \n",
       "2017-12-03 17:04:00-06:00  115.707031  106.765625  \n",
       "2017-12-03 17:05:00-06:00  115.699219  106.757812  \n",
       "2017-12-03 17:06:00-06:00  115.691406  106.757812  \n",
       "2017-12-03 17:07:00-06:00  115.683594  106.757812  \n",
       "2017-12-03 17:08:00-06:00  115.691406  106.757812  \n",
       "2017-12-03 17:09:00-06:00  115.691406  106.757812  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.iloc[1370:1390]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[bonds]=train[bonds]*np.array(point_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg=linear_model.LinearRegression()\n",
    "reg.fit(train[['UB','ZB','TN','ZN','ZF']].iloc[:20*1380],train['ZT'].iloc[:20*1380])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120500.48477445773"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fit_intercept': True,\n",
       " 'normalize': False,\n",
       " 'copy_X': True,\n",
       " 'n_jobs': 1,\n",
       " 'coef_': array([-0.03053976,  0.07289506, -0.31213368,  0.17342202,  0.92402161]),\n",
       " '_residues': 8382368.464803021,\n",
       " 'rank_': 5,\n",
       " 'singular_': array([313956.23995571,  43169.42770394,   8693.85475305,   4877.65607726,\n",
       "          1821.89733832]),\n",
       " 'intercept_': 120500.48477445773}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.03053976,  0.07289506, -0.31213368,  0.17342202,  0.92402161])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import f_regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_1=f_regression(train[['UB','ZB','TN','ZN','ZF']].iloc[0:20*1380],train['ZT'].iloc[0:20*1380])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 12780.58917273,  24097.28374541,  40222.02308617,  83544.5278842 ,\n",
       "        265027.19797259]), array([0., 0., 0., 0., 0.]))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 12780.58917273,  24097.28374541,  40222.02308617,  83544.5278842 ,\n",
       "        265027.19797259]), array([0., 0., 0., 0., 0.]))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(reg_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "coint_vec=np.append(-reg.coef_,np.array([1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.03053976, -0.07289506,  0.31213368, -0.17342202, -0.92402161,\n",
       "        1.        ])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coint_vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio = (train*coint_vec).sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "pmean = portfolio[:1380*20].mean()\n",
    "pstdev = portfolio[:1380*20].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2725da84898>"
      ]
     },
     "execution_count": 28,
     "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": [
    "portfolio.plot()\n",
    "plt.axhline(pmean)\n",
    "plt.axhline(pmean+pstdev)\n",
    "plt.axhline(pmean-pstdev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2725c297240>]"
      ]
     },
     "execution_count": 29,
     "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.plot(portfolio.diff().dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2725db362e8>"
      ]
     },
     "execution_count": 30,
     "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": [
    "train.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'fit' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-31-63040c2220eb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcoint_another\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1380\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'fit' is not defined"
     ]
    }
   ],
   "source": [
    "coint_another=fit(train.iloc[0:20*1380])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UB</th>\n",
       "      <th>ZB</th>\n",
       "      <th>TN</th>\n",
       "      <th>ZN</th>\n",
       "      <th>ZF</th>\n",
       "      <th>ZT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:00:00-06:00</th>\n",
       "      <td>163406.250</td>\n",
       "      <td>149921.875</td>\n",
       "      <td>132000.0000</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115847.65625</td>\n",
       "      <td>213640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:01:00-06:00</th>\n",
       "      <td>163375.000</td>\n",
       "      <td>149921.875</td>\n",
       "      <td>132000.0000</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115839.84375</td>\n",
       "      <td>213640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:02:00-06:00</th>\n",
       "      <td>163406.250</td>\n",
       "      <td>149921.875</td>\n",
       "      <td>132000.0000</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115839.84375</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:03:00-06:00</th>\n",
       "      <td>163406.250</td>\n",
       "      <td>149921.875</td>\n",
       "      <td>132000.0000</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115839.84375</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:04:00-06:00</th>\n",
       "      <td>163406.250</td>\n",
       "      <td>149921.875</td>\n",
       "      <td>132000.0000</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115839.84375</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:05:00-06:00</th>\n",
       "      <td>163406.250</td>\n",
       "      <td>149921.875</td>\n",
       "      <td>132000.0000</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115839.84375</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:06:00-06:00</th>\n",
       "      <td>163406.250</td>\n",
       "      <td>149921.875</td>\n",
       "      <td>132000.0000</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115839.84375</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:07:00-06:00</th>\n",
       "      <td>163406.250</td>\n",
       "      <td>149921.875</td>\n",
       "      <td>132000.0000</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115832.03125</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:08:00-06:00</th>\n",
       "      <td>163375.000</td>\n",
       "      <td>149890.625</td>\n",
       "      <td>131984.3750</td>\n",
       "      <td>123164.0625</td>\n",
       "      <td>115832.03125</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:09:00-06:00</th>\n",
       "      <td>163375.000</td>\n",
       "      <td>149890.625</td>\n",
       "      <td>131984.3750</td>\n",
       "      <td>123164.0625</td>\n",
       "      <td>115832.03125</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:10:00-06:00</th>\n",
       "      <td>163375.000</td>\n",
       "      <td>149890.625</td>\n",
       "      <td>131984.3750</td>\n",
       "      <td>123164.0625</td>\n",
       "      <td>115832.03125</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:11:00-06:00</th>\n",
       "      <td>163375.000</td>\n",
       "      <td>149890.625</td>\n",
       "      <td>131984.3750</td>\n",
       "      <td>123164.0625</td>\n",
       "      <td>115832.03125</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:12:00-06:00</th>\n",
       "      <td>163375.000</td>\n",
       "      <td>149890.625</td>\n",
       "      <td>131984.3750</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115839.84375</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:13:00-06:00</th>\n",
       "      <td>163375.000</td>\n",
       "      <td>149890.625</td>\n",
       "      <td>132000.0000</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115839.84375</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:14:00-06:00</th>\n",
       "      <td>163375.000</td>\n",
       "      <td>149890.625</td>\n",
       "      <td>132000.0000</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115839.84375</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:15:00-06:00</th>\n",
       "      <td>163375.000</td>\n",
       "      <td>149890.625</td>\n",
       "      <td>131992.1875</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115839.84375</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:16:00-06:00</th>\n",
       "      <td>163375.000</td>\n",
       "      <td>149890.625</td>\n",
       "      <td>132000.0000</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115839.84375</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:17:00-06:00</th>\n",
       "      <td>163375.000</td>\n",
       "      <td>149890.625</td>\n",
       "      <td>132000.0000</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115839.84375</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:18:00-06:00</th>\n",
       "      <td>163375.000</td>\n",
       "      <td>149890.625</td>\n",
       "      <td>131992.1875</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115839.84375</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:19:00-06:00</th>\n",
       "      <td>163375.000</td>\n",
       "      <td>149890.625</td>\n",
       "      <td>132000.0000</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115839.84375</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:20:00-06:00</th>\n",
       "      <td>163375.000</td>\n",
       "      <td>149890.625</td>\n",
       "      <td>132000.0000</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115847.65625</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:21:00-06:00</th>\n",
       "      <td>163375.000</td>\n",
       "      <td>149921.875</td>\n",
       "      <td>132000.0000</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115839.84375</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:22:00-06:00</th>\n",
       "      <td>163421.875</td>\n",
       "      <td>149921.875</td>\n",
       "      <td>132000.0000</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115839.84375</td>\n",
       "      <td>213625.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:23:00-06:00</th>\n",
       "      <td>163437.500</td>\n",
       "      <td>149921.875</td>\n",
       "      <td>132000.0000</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115839.84375</td>\n",
       "      <td>213640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:24:00-06:00</th>\n",
       "      <td>163437.500</td>\n",
       "      <td>149921.875</td>\n",
       "      <td>132007.8125</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115847.65625</td>\n",
       "      <td>213640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:25:00-06:00</th>\n",
       "      <td>163468.750</td>\n",
       "      <td>149953.125</td>\n",
       "      <td>132015.6250</td>\n",
       "      <td>123179.6875</td>\n",
       "      <td>115847.65625</td>\n",
       "      <td>213640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:26:00-06:00</th>\n",
       "      <td>163484.375</td>\n",
       "      <td>149953.125</td>\n",
       "      <td>132015.6250</td>\n",
       "      <td>123195.3125</td>\n",
       "      <td>115847.65625</td>\n",
       "      <td>213640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:27:00-06:00</th>\n",
       "      <td>163500.000</td>\n",
       "      <td>149953.125</td>\n",
       "      <td>132015.6250</td>\n",
       "      <td>123195.3125</td>\n",
       "      <td>115847.65625</td>\n",
       "      <td>213640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:28:00-06:00</th>\n",
       "      <td>163500.000</td>\n",
       "      <td>149953.125</td>\n",
       "      <td>132031.2500</td>\n",
       "      <td>123195.3125</td>\n",
       "      <td>115847.65625</td>\n",
       "      <td>213640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 17:29:00-06:00</th>\n",
       "      <td>163531.250</td>\n",
       "      <td>149984.375</td>\n",
       "      <td>132031.2500</td>\n",
       "      <td>123195.3125</td>\n",
       "      <td>115847.65625</td>\n",
       "      <td>213640.6250</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:30:00-05:00</th>\n",
       "      <td>158703.125</td>\n",
       "      <td>144859.375</td>\n",
       "      <td>128250.0000</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:31:00-05:00</th>\n",
       "      <td>158703.125</td>\n",
       "      <td>144859.375</td>\n",
       "      <td>128242.1875</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:32:00-05:00</th>\n",
       "      <td>158703.125</td>\n",
       "      <td>144859.375</td>\n",
       "      <td>128242.1875</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113585.93750</td>\n",
       "      <td>211640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:33:00-05:00</th>\n",
       "      <td>158703.125</td>\n",
       "      <td>144859.375</td>\n",
       "      <td>128242.1875</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113585.93750</td>\n",
       "      <td>211640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:34:00-05:00</th>\n",
       "      <td>158703.125</td>\n",
       "      <td>144859.375</td>\n",
       "      <td>128242.1875</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113582.03125</td>\n",
       "      <td>211640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:35:00-05:00</th>\n",
       "      <td>158703.125</td>\n",
       "      <td>144859.375</td>\n",
       "      <td>128242.1875</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:36:00-05:00</th>\n",
       "      <td>158703.125</td>\n",
       "      <td>144859.375</td>\n",
       "      <td>128242.1875</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:37:00-05:00</th>\n",
       "      <td>158703.125</td>\n",
       "      <td>144859.375</td>\n",
       "      <td>128242.1875</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:38:00-05:00</th>\n",
       "      <td>158703.125</td>\n",
       "      <td>144859.375</td>\n",
       "      <td>128257.8125</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:39:00-05:00</th>\n",
       "      <td>158703.125</td>\n",
       "      <td>144859.375</td>\n",
       "      <td>128257.8125</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:40:00-05:00</th>\n",
       "      <td>158703.125</td>\n",
       "      <td>144859.375</td>\n",
       "      <td>128257.8125</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:41:00-05:00</th>\n",
       "      <td>158703.125</td>\n",
       "      <td>144859.375</td>\n",
       "      <td>128257.8125</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:42:00-05:00</th>\n",
       "      <td>158734.375</td>\n",
       "      <td>144890.625</td>\n",
       "      <td>128273.4375</td>\n",
       "      <td>120351.5625</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:43:00-05:00</th>\n",
       "      <td>158765.625</td>\n",
       "      <td>144890.625</td>\n",
       "      <td>128273.4375</td>\n",
       "      <td>120351.5625</td>\n",
       "      <td>113597.65625</td>\n",
       "      <td>211640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:44:00-05:00</th>\n",
       "      <td>158734.375</td>\n",
       "      <td>144890.625</td>\n",
       "      <td>128273.4375</td>\n",
       "      <td>120351.5625</td>\n",
       "      <td>113593.75000</td>\n",
       "      <td>211640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:45:00-05:00</th>\n",
       "      <td>158734.375</td>\n",
       "      <td>144890.625</td>\n",
       "      <td>128273.4375</td>\n",
       "      <td>120351.5625</td>\n",
       "      <td>113597.65625</td>\n",
       "      <td>211640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:46:00-05:00</th>\n",
       "      <td>158734.375</td>\n",
       "      <td>144890.625</td>\n",
       "      <td>128273.4375</td>\n",
       "      <td>120351.5625</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:47:00-05:00</th>\n",
       "      <td>158734.375</td>\n",
       "      <td>144890.625</td>\n",
       "      <td>128273.4375</td>\n",
       "      <td>120351.5625</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:48:00-05:00</th>\n",
       "      <td>158734.375</td>\n",
       "      <td>144890.625</td>\n",
       "      <td>128273.4375</td>\n",
       "      <td>120351.5625</td>\n",
       "      <td>113593.75000</td>\n",
       "      <td>211640.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:49:00-05:00</th>\n",
       "      <td>158750.000</td>\n",
       "      <td>144890.625</td>\n",
       "      <td>128273.4375</td>\n",
       "      <td>120351.5625</td>\n",
       "      <td>113593.75000</td>\n",
       "      <td>211632.8125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:50:00-05:00</th>\n",
       "      <td>158734.375</td>\n",
       "      <td>144875.000</td>\n",
       "      <td>128265.6250</td>\n",
       "      <td>120351.5625</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211632.8125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:51:00-05:00</th>\n",
       "      <td>158671.875</td>\n",
       "      <td>144843.750</td>\n",
       "      <td>128250.0000</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211632.8125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:52:00-05:00</th>\n",
       "      <td>158703.125</td>\n",
       "      <td>144828.125</td>\n",
       "      <td>128250.0000</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211632.8125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:53:00-05:00</th>\n",
       "      <td>158703.125</td>\n",
       "      <td>144828.125</td>\n",
       "      <td>128250.0000</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211632.8125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:54:00-05:00</th>\n",
       "      <td>158671.875</td>\n",
       "      <td>144828.125</td>\n",
       "      <td>128250.0000</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211632.8125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:55:00-05:00</th>\n",
       "      <td>158671.875</td>\n",
       "      <td>144828.125</td>\n",
       "      <td>128250.0000</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211632.8125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:56:00-05:00</th>\n",
       "      <td>158703.125</td>\n",
       "      <td>144843.750</td>\n",
       "      <td>128242.1875</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211632.8125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:57:00-05:00</th>\n",
       "      <td>158703.125</td>\n",
       "      <td>144843.750</td>\n",
       "      <td>128242.1875</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211632.8125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:58:00-05:00</th>\n",
       "      <td>158687.500</td>\n",
       "      <td>144843.750</td>\n",
       "      <td>128250.0000</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113589.84375</td>\n",
       "      <td>211632.8125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:59:00-05:00</th>\n",
       "      <td>158687.500</td>\n",
       "      <td>144828.125</td>\n",
       "      <td>128234.3750</td>\n",
       "      <td>120335.9375</td>\n",
       "      <td>113593.75000</td>\n",
       "      <td>211632.8125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>270480 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   UB          ZB           TN           ZN  \\\n",
       "timestamp                                                                     \n",
       "2017-11-30 17:00:00-06:00  163406.250  149921.875  132000.0000  123179.6875   \n",
       "2017-11-30 17:01:00-06:00  163375.000  149921.875  132000.0000  123179.6875   \n",
       "2017-11-30 17:02:00-06:00  163406.250  149921.875  132000.0000  123179.6875   \n",
       "2017-11-30 17:03:00-06:00  163406.250  149921.875  132000.0000  123179.6875   \n",
       "2017-11-30 17:04:00-06:00  163406.250  149921.875  132000.0000  123179.6875   \n",
       "2017-11-30 17:05:00-06:00  163406.250  149921.875  132000.0000  123179.6875   \n",
       "2017-11-30 17:06:00-06:00  163406.250  149921.875  132000.0000  123179.6875   \n",
       "2017-11-30 17:07:00-06:00  163406.250  149921.875  132000.0000  123179.6875   \n",
       "2017-11-30 17:08:00-06:00  163375.000  149890.625  131984.3750  123164.0625   \n",
       "2017-11-30 17:09:00-06:00  163375.000  149890.625  131984.3750  123164.0625   \n",
       "2017-11-30 17:10:00-06:00  163375.000  149890.625  131984.3750  123164.0625   \n",
       "2017-11-30 17:11:00-06:00  163375.000  149890.625  131984.3750  123164.0625   \n",
       "2017-11-30 17:12:00-06:00  163375.000  149890.625  131984.3750  123179.6875   \n",
       "2017-11-30 17:13:00-06:00  163375.000  149890.625  132000.0000  123179.6875   \n",
       "2017-11-30 17:14:00-06:00  163375.000  149890.625  132000.0000  123179.6875   \n",
       "2017-11-30 17:15:00-06:00  163375.000  149890.625  131992.1875  123179.6875   \n",
       "2017-11-30 17:16:00-06:00  163375.000  149890.625  132000.0000  123179.6875   \n",
       "2017-11-30 17:17:00-06:00  163375.000  149890.625  132000.0000  123179.6875   \n",
       "2017-11-30 17:18:00-06:00  163375.000  149890.625  131992.1875  123179.6875   \n",
       "2017-11-30 17:19:00-06:00  163375.000  149890.625  132000.0000  123179.6875   \n",
       "2017-11-30 17:20:00-06:00  163375.000  149890.625  132000.0000  123179.6875   \n",
       "2017-11-30 17:21:00-06:00  163375.000  149921.875  132000.0000  123179.6875   \n",
       "2017-11-30 17:22:00-06:00  163421.875  149921.875  132000.0000  123179.6875   \n",
       "2017-11-30 17:23:00-06:00  163437.500  149921.875  132000.0000  123179.6875   \n",
       "2017-11-30 17:24:00-06:00  163437.500  149921.875  132007.8125  123179.6875   \n",
       "2017-11-30 17:25:00-06:00  163468.750  149953.125  132015.6250  123179.6875   \n",
       "2017-11-30 17:26:00-06:00  163484.375  149953.125  132015.6250  123195.3125   \n",
       "2017-11-30 17:27:00-06:00  163500.000  149953.125  132015.6250  123195.3125   \n",
       "2017-11-30 17:28:00-06:00  163500.000  149953.125  132031.2500  123195.3125   \n",
       "2017-11-30 17:29:00-06:00  163531.250  149984.375  132031.2500  123195.3125   \n",
       "...                               ...         ...          ...          ...   \n",
       "2018-08-31 15:30:00-05:00  158703.125  144859.375  128250.0000  120335.9375   \n",
       "2018-08-31 15:31:00-05:00  158703.125  144859.375  128242.1875  120335.9375   \n",
       "2018-08-31 15:32:00-05:00  158703.125  144859.375  128242.1875  120335.9375   \n",
       "2018-08-31 15:33:00-05:00  158703.125  144859.375  128242.1875  120335.9375   \n",
       "2018-08-31 15:34:00-05:00  158703.125  144859.375  128242.1875  120335.9375   \n",
       "2018-08-31 15:35:00-05:00  158703.125  144859.375  128242.1875  120335.9375   \n",
       "2018-08-31 15:36:00-05:00  158703.125  144859.375  128242.1875  120335.9375   \n",
       "2018-08-31 15:37:00-05:00  158703.125  144859.375  128242.1875  120335.9375   \n",
       "2018-08-31 15:38:00-05:00  158703.125  144859.375  128257.8125  120335.9375   \n",
       "2018-08-31 15:39:00-05:00  158703.125  144859.375  128257.8125  120335.9375   \n",
       "2018-08-31 15:40:00-05:00  158703.125  144859.375  128257.8125  120335.9375   \n",
       "2018-08-31 15:41:00-05:00  158703.125  144859.375  128257.8125  120335.9375   \n",
       "2018-08-31 15:42:00-05:00  158734.375  144890.625  128273.4375  120351.5625   \n",
       "2018-08-31 15:43:00-05:00  158765.625  144890.625  128273.4375  120351.5625   \n",
       "2018-08-31 15:44:00-05:00  158734.375  144890.625  128273.4375  120351.5625   \n",
       "2018-08-31 15:45:00-05:00  158734.375  144890.625  128273.4375  120351.5625   \n",
       "2018-08-31 15:46:00-05:00  158734.375  144890.625  128273.4375  120351.5625   \n",
       "2018-08-31 15:47:00-05:00  158734.375  144890.625  128273.4375  120351.5625   \n",
       "2018-08-31 15:48:00-05:00  158734.375  144890.625  128273.4375  120351.5625   \n",
       "2018-08-31 15:49:00-05:00  158750.000  144890.625  128273.4375  120351.5625   \n",
       "2018-08-31 15:50:00-05:00  158734.375  144875.000  128265.6250  120351.5625   \n",
       "2018-08-31 15:51:00-05:00  158671.875  144843.750  128250.0000  120335.9375   \n",
       "2018-08-31 15:52:00-05:00  158703.125  144828.125  128250.0000  120335.9375   \n",
       "2018-08-31 15:53:00-05:00  158703.125  144828.125  128250.0000  120335.9375   \n",
       "2018-08-31 15:54:00-05:00  158671.875  144828.125  128250.0000  120335.9375   \n",
       "2018-08-31 15:55:00-05:00  158671.875  144828.125  128250.0000  120335.9375   \n",
       "2018-08-31 15:56:00-05:00  158703.125  144843.750  128242.1875  120335.9375   \n",
       "2018-08-31 15:57:00-05:00  158703.125  144843.750  128242.1875  120335.9375   \n",
       "2018-08-31 15:58:00-05:00  158687.500  144843.750  128250.0000  120335.9375   \n",
       "2018-08-31 15:59:00-05:00  158687.500  144828.125  128234.3750  120335.9375   \n",
       "\n",
       "                                     ZF           ZT  \n",
       "timestamp                                             \n",
       "2017-11-30 17:00:00-06:00  115847.65625  213640.6250  \n",
       "2017-11-30 17:01:00-06:00  115839.84375  213640.6250  \n",
       "2017-11-30 17:02:00-06:00  115839.84375  213625.0000  \n",
       "2017-11-30 17:03:00-06:00  115839.84375  213625.0000  \n",
       "2017-11-30 17:04:00-06:00  115839.84375  213625.0000  \n",
       "2017-11-30 17:05:00-06:00  115839.84375  213625.0000  \n",
       "2017-11-30 17:06:00-06:00  115839.84375  213625.0000  \n",
       "2017-11-30 17:07:00-06:00  115832.03125  213625.0000  \n",
       "2017-11-30 17:08:00-06:00  115832.03125  213625.0000  \n",
       "2017-11-30 17:09:00-06:00  115832.03125  213625.0000  \n",
       "2017-11-30 17:10:00-06:00  115832.03125  213625.0000  \n",
       "2017-11-30 17:11:00-06:00  115832.03125  213625.0000  \n",
       "2017-11-30 17:12:00-06:00  115839.84375  213625.0000  \n",
       "2017-11-30 17:13:00-06:00  115839.84375  213625.0000  \n",
       "2017-11-30 17:14:00-06:00  115839.84375  213625.0000  \n",
       "2017-11-30 17:15:00-06:00  115839.84375  213625.0000  \n",
       "2017-11-30 17:16:00-06:00  115839.84375  213625.0000  \n",
       "2017-11-30 17:17:00-06:00  115839.84375  213625.0000  \n",
       "2017-11-30 17:18:00-06:00  115839.84375  213625.0000  \n",
       "2017-11-30 17:19:00-06:00  115839.84375  213625.0000  \n",
       "2017-11-30 17:20:00-06:00  115847.65625  213625.0000  \n",
       "2017-11-30 17:21:00-06:00  115839.84375  213625.0000  \n",
       "2017-11-30 17:22:00-06:00  115839.84375  213625.0000  \n",
       "2017-11-30 17:23:00-06:00  115839.84375  213640.6250  \n",
       "2017-11-30 17:24:00-06:00  115847.65625  213640.6250  \n",
       "2017-11-30 17:25:00-06:00  115847.65625  213640.6250  \n",
       "2017-11-30 17:26:00-06:00  115847.65625  213640.6250  \n",
       "2017-11-30 17:27:00-06:00  115847.65625  213640.6250  \n",
       "2017-11-30 17:28:00-06:00  115847.65625  213640.6250  \n",
       "2017-11-30 17:29:00-06:00  115847.65625  213640.6250  \n",
       "...                                 ...          ...  \n",
       "2018-08-31 15:30:00-05:00  113589.84375  211640.6250  \n",
       "2018-08-31 15:31:00-05:00  113589.84375  211640.6250  \n",
       "2018-08-31 15:32:00-05:00  113585.93750  211640.6250  \n",
       "2018-08-31 15:33:00-05:00  113585.93750  211640.6250  \n",
       "2018-08-31 15:34:00-05:00  113582.03125  211640.6250  \n",
       "2018-08-31 15:35:00-05:00  113589.84375  211640.6250  \n",
       "2018-08-31 15:36:00-05:00  113589.84375  211640.6250  \n",
       "2018-08-31 15:37:00-05:00  113589.84375  211640.6250  \n",
       "2018-08-31 15:38:00-05:00  113589.84375  211640.6250  \n",
       "2018-08-31 15:39:00-05:00  113589.84375  211640.6250  \n",
       "2018-08-31 15:40:00-05:00  113589.84375  211640.6250  \n",
       "2018-08-31 15:41:00-05:00  113589.84375  211640.6250  \n",
       "2018-08-31 15:42:00-05:00  113589.84375  211640.6250  \n",
       "2018-08-31 15:43:00-05:00  113597.65625  211640.6250  \n",
       "2018-08-31 15:44:00-05:00  113593.75000  211640.6250  \n",
       "2018-08-31 15:45:00-05:00  113597.65625  211640.6250  \n",
       "2018-08-31 15:46:00-05:00  113589.84375  211640.6250  \n",
       "2018-08-31 15:47:00-05:00  113589.84375  211640.6250  \n",
       "2018-08-31 15:48:00-05:00  113593.75000  211640.6250  \n",
       "2018-08-31 15:49:00-05:00  113593.75000  211632.8125  \n",
       "2018-08-31 15:50:00-05:00  113589.84375  211632.8125  \n",
       "2018-08-31 15:51:00-05:00  113589.84375  211632.8125  \n",
       "2018-08-31 15:52:00-05:00  113589.84375  211632.8125  \n",
       "2018-08-31 15:53:00-05:00  113589.84375  211632.8125  \n",
       "2018-08-31 15:54:00-05:00  113589.84375  211632.8125  \n",
       "2018-08-31 15:55:00-05:00  113589.84375  211632.8125  \n",
       "2018-08-31 15:56:00-05:00  113589.84375  211632.8125  \n",
       "2018-08-31 15:57:00-05:00  113589.84375  211632.8125  \n",
       "2018-08-31 15:58:00-05:00  113589.84375  211632.8125  \n",
       "2018-08-31 15:59:00-05:00  113593.75000  211632.8125  \n",
       "\n",
       "[270480 rows x 6 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(data):\n",
    "    x = data.values\n",
    "    x_centered = x - np.mean(x, axis=0)\n",
    "    johansen = Johansen(x_centered, model=3, significance_level=0)\n",
    "    eigenvectors, r = johansen.johansen()\n",
    "    #print('rejected r values are: {}'.format(r))\n",
    "    vec = eigenvectors[:, 0]\n",
    "    #vec_max = np.max(np.abs(vec))\n",
    "    vec_min = min(np.abs(vec))\n",
    "    vec = vec / vec_min\n",
    "    #print('The first cointegrating relation: {}'.format(vec))\n",
    "    return vec    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "coint_another=fit(train.iloc[0:20*1380])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -1.07675621,   1.59653967,   5.99281051, -13.01482361,\n",
       "         7.55255591,  -1.        ])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coint_another"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_another=(train*coint_another).sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_mean = portfolio_another[:1380*20].mean()\n",
    "p_stdev = portfolio_another[:1380*20].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x272693e5400>"
      ]
     },
     "execution_count": 39,
     "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": [
    "portfolio_another.plot()\n",
    "plt.axhline(p_mean)\n",
    "plt.axhline(p_mean+p_stdev)\n",
    "plt.axhline(p_mean-p_stdev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25ad0c25ac8>"
      ]
     },
     "execution_count": 40,
     "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": [
    "portfolio_another.diff().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adfuller(portfolio_another)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import adfuller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adfuller(portfolio)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}