unsupervisedProject.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas_datareader import data\n",
    "import datetime as dt\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Get Response Data -- SHORTVOL index\n",
    "\n",
    "Get data from Yahoo since 2008-01-07"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2008-01-07    0.016092\n",
       "2008-01-08   -0.021566\n",
       "2008-01-09   -0.003674\n",
       "2008-01-10    0.037562\n",
       "2008-01-11   -0.043531\n",
       "Name: Adj Close, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get SHORTVOL data from Yahoo. Thanks for letting me know, Stuart ^_^\n",
    "\n",
    "svix = data.DataReader('^SHORTVOL', 'yahoo', '1990-01-01')\n",
    "svix_rets = svix['Adj Close'].pct_change().dropna()\n",
    "svix_rets = svix_rets['2008-01-07':]\n",
    "svix_rets.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Get variable data from my dropbox (calculated in R). \n",
    "\n",
    "Plot a heatmap of missing variable data. \n",
    "\n",
    "Start some imputations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get variables from my dropbox. These things are calculated in R because I have my workflow set up in R already. \n",
    "# Will have to learn how to dynamically update the dropbox\n",
    "\n",
    "variables = pd.read_csv(\"https://www.dropbox.com/s/ykimtkrwpxp80o9/rawVariables.csv?raw=1\", parse_dates=True, index_col=0)\n",
    "variables.head()\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.heatmap(variables.isnull())\n",
    "plt.title('Missing values heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>VIX9D</th>\n",
       "      <th>VIX</th>\n",
       "      <th>VIX3M</th>\n",
       "      <th>VIX6M</th>\n",
       "      <th>C1</th>\n",
       "      <th>C2</th>\n",
       "      <th>C3</th>\n",
       "      <th>C4</th>\n",
       "      <th>C5</th>\n",
       "      <th>C6</th>\n",
       "      <th>C7</th>\n",
       "      <th>C8</th>\n",
       "      <th>c30</th>\n",
       "      <th>c60</th>\n",
       "      <th>vxxCrush</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-04</th>\n",
       "      <td>25.10</td>\n",
       "      <td>20.74</td>\n",
       "      <td>20.75</td>\n",
       "      <td>20.60</td>\n",
       "      <td>19.425</td>\n",
       "      <td>19.275</td>\n",
       "      <td>19.125</td>\n",
       "      <td>19.175</td>\n",
       "      <td>18.975</td>\n",
       "      <td>18.975</td>\n",
       "      <td>18.975</td>\n",
       "      <td>19.100</td>\n",
       "      <td>19.361842</td>\n",
       "      <td>19.353947</td>\n",
       "      <td>-0.328135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-05</th>\n",
       "      <td>25.10</td>\n",
       "      <td>20.74</td>\n",
       "      <td>20.75</td>\n",
       "      <td>20.60</td>\n",
       "      <td>19.025</td>\n",
       "      <td>19.050</td>\n",
       "      <td>18.975</td>\n",
       "      <td>19.025</td>\n",
       "      <td>18.900</td>\n",
       "      <td>18.900</td>\n",
       "      <td>18.925</td>\n",
       "      <td>19.100</td>\n",
       "      <td>19.036842</td>\n",
       "      <td>19.085526</td>\n",
       "      <td>-0.328135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-06</th>\n",
       "      <td>25.11</td>\n",
       "      <td>21.19</td>\n",
       "      <td>21.06</td>\n",
       "      <td>20.83</td>\n",
       "      <td>19.925</td>\n",
       "      <td>19.475</td>\n",
       "      <td>19.225</td>\n",
       "      <td>19.275</td>\n",
       "      <td>19.075</td>\n",
       "      <td>19.025</td>\n",
       "      <td>19.025</td>\n",
       "      <td>19.225</td>\n",
       "      <td>19.688158</td>\n",
       "      <td>19.580263</td>\n",
       "      <td>-0.352122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            VIX9D    VIX  VIX3M  VIX6M      C1      C2      C3      C4  \\\n",
       "Index                                                                    \n",
       "2018-12-04  25.10  20.74  20.75  20.60  19.425  19.275  19.125  19.175   \n",
       "2018-12-05  25.10  20.74  20.75  20.60  19.025  19.050  18.975  19.025   \n",
       "2018-12-06  25.11  21.19  21.06  20.83  19.925  19.475  19.225  19.275   \n",
       "\n",
       "                C5      C6      C7      C8        c30        c60  vxxCrush  \n",
       "Index                                                                       \n",
       "2018-12-04  18.975  18.975  18.975  19.100  19.361842  19.353947 -0.328135  \n",
       "2018-12-05  18.900  18.900  18.925  19.100  19.036842  19.085526 -0.328135  \n",
       "2018-12-06  19.075  19.025  19.025  19.225  19.688158  19.580263 -0.352122  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "variables['2018-12-04':'2018-12-06']\n",
    "variables.loc['2018-12-05',['VIX9D', 'VIX', 'VIX3M', 'VIX6M', 'vxxCrush']] = variables.loc['2018-12-04',['VIX9D', 'VIX', 'VIX3M', 'VIX6M', 'vxxCrush']]\n",
    "variables['2018-12-04':'2018-12-06']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Initial Feature Engineering -- Contangos and Ratios\n",
    "\n",
    "Do some feature engineering here. Create the following variables:\n",
    "\n",
    "M1 roll yield: C1/VIX - 1  \n",
    "VX30 roll yield: C30 (30 day constant maturity) / VIX - 1  \n",
    "Contango: C2/C1 - 1  \n",
    "Adjusted Contango: C60 (60 day constant maturity) / C30 - 1  \n",
    "M4M7 Contango: C7/C4 - 1  \n",
    "Fast Cross: VIX9D/VIX  \n",
    "Medium Cross: VIX/VIX3M  \n",
    "Slow Cross: VIX/VIX6M  \n",
    "Strat Cross: VIX3M/VIX6M  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Do some feature engineering -- roll yields and crossover ratios \n",
    "# like the one found in my strategy -- called stratcross here\n",
    "\n",
    "variables['m1_roll_yield'] = variables['C1']/variables['VIX']-1\n",
    "variables['VX30_roll_yield'] = variables['c30']/variables['VIX']-1\n",
    "variables['contango'] = variables['C2']/variables['C1']-1\n",
    "variables['adj_contango'] = variables['c60']/variables['c30']-1\n",
    "variables['m4m7_contango'] = variables['C7']/variables['C4']-1\n",
    "variables['fastcross'] = variables['VIX9D']/variables['VIX']\n",
    "variables['medcross'] = variables['VIX']/variables['VIX3M']\n",
    "variables['slowcross'] = variables['VIX']/variables['VIX6M']\n",
    "variables['stratcross'] = variables['VIX3M']/variables['VIX6M']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# The Response Variable and Variable Histograms\n",
    "\n",
    "Create the response variable -- sign of the returns of SHORTVOL two days in the future.  \n",
    "Let's also plot some histograms of some variables, such as the returns, a futures contract, and a contango relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2008-01-07    1\n",
       "2008-01-08   -1\n",
       "2008-01-09   -1\n",
       "2008-01-10    1\n",
       "2008-01-11   -1\n",
       "Name: Adj Close, dtype: int64"
      ]
     },
     "execution_count": 6,
     "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": [
    "# get the sign of returns\n",
    "\n",
    "def sign_func(x):\n",
    "  if x < 0:\n",
    "    return -1\n",
    "  if x > 0:\n",
    "    return 1\n",
    "  if x == 0:\n",
    "    return 0\n",
    "\n",
    "return_sign = svix_rets.apply(sign_func)\n",
    "plt.hist(return_sign)\n",
    "plt.title('Histogram of sign of returns')\n",
    "return_sign.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2020-02-24    0.015858\n",
       "2020-02-25   -0.137426\n",
       "2020-02-26   -0.115638\n",
       "2020-02-27         NaN\n",
       "2020-02-28         NaN\n",
       "Name: Adj Close, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# regression predicted variable -- that is, we predict the value of 2020-01-23\n",
    "# with the values we see on 2020-01-21\n",
    "reg_response = svix_rets.shift(-2, axis = 0)\n",
    "\n",
    "# classification predicted variable -- we'll be using this one\n",
    "clf_response = return_sign.shift(-2, axis = 0)\n",
    "\n",
    "reg_response.tail()\n",
    "\n",
    "# We'll add these later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ilya\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:824: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "C:\\Users\\Ilya\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:825: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# variables generally are not normal -- the response variable, a contract\n",
    "# and an example of a contango relationship -- a ratio between two contracts\n",
    "# log transforms don't really work here due to negative values above -1\n",
    "# negative values mean boxcox transforms also don't work\n",
    "\n",
    "plt.figure(figsize = (20, 5))\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.hist(reg_response)\n",
    "plt.title('Histogram of response')\n",
    "plt.subplot(1, 3, 2)\n",
    "\n",
    "plt.hist(variables['C4'])\n",
    "plt.title('Histogram of a futures contract')\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.hist(variables['vxxCrush'])\n",
    "plt.title('Histogram of a contango relationship')\n",
    "plt.show()\n",
    "#variables = variables.ffill(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# VIX9D Scatterplot\n",
    "\n",
    "VIX9D starts later than VIX, VIX3M, and VIX6M. We can impute that. Let's look at a scatterplot of values to visualize the relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x214f8763748>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7pixbl9cTTmSIJDPklSKSzBBOZNi6fH4yAi0KoVAtyevxeNi5cycf+MAHys4S+vr6+PKXv8w//MM/cMMNN7B06VK+/e1vz+qaGs1c4vf42bViF06rk8HEIE6rk10rds2NPQEMu8GmW8DqhugZY7npFm1PmIKl9S5u2uzHaTPTH07itJm5abN/XuwJsEDqKcyWrcvrefglw5vCbbcQS2UJJzJcs7Zx1ue+8847ufXWWyd4IgH82Z/9GZ/5zGcoBt995Stf4dprr+Xd7343DQ0Ns762RjMX+D3+uRMC5ahr00Jghiytd82bEBjPgqjRvG3bNjW+yM4rr7zCxo0bKz7HXHsfXUjM9FloNJrFgYgcUEptm8kxi2KmAOdX8mo0Gs1CYVHYFDQajUZTGVooaDQajWYULRQ0Go1GM4oWChqNRqMZRQsFjUaj0YyihcI5MtvU2T//+c/ZsmUL7e3tbNu2jaeffhqAzs5ORIQvfOELo8cNDg5itVr5+Mc/Pg93ptFoFjNaKJwjs02dff3113Pw4EE6Ojq45557+NCHPjR63OrVq3nooYdG13/4wx+yadOm6t+URqNZ9CyaOAWGTxvpemP9RjKutu2zirKcbepsj+ds5shYLDYmu6rT6WTjxo3s37+fbdu28cADD/Ce97yH3t7ec+6vRqPRVMLimClUodDHXKTO/ulPf8qGDRt429vexj333DNm2x133MHu3bvp7u7GbDbT2tp6zn3VaDSaSlkcQqG00IeYjKWz1mifBbNNnf2ud72LI0eO8LOf/WyMDQHghhtu4NFHH+X+++/n9ttvn1U/NRqNplIWh1CoUqGPuUqdfd1113H8+HEGBwfPds9mY+vWrfzzP/8z7373u2fVT41Go6mUxSEUqlToYzaps48dOzYqIJ5//nnS6TQ+39jCJp/61Kf40pe+NKFdo9FoqsXiMDS3bTdsCGDMENJRo9DH6jfN+tTnmjr7xz/+Md/73vewWq04nU4eeOCBCfaITZs2aa8jjUYzryya1Nlz7X10IaFTZ2s0mnLo1NlToQt9aDQazbRU1aYgInUi8iMROSIir4jIDhFpEJFHReS1wnJ+Co9qNBqNZlqqbWj+KvCIUmoDcDnwCvBZ4DGl1DrgscK6RqPRaC4AqqY+EpEa4DrgbgClVBpIi8jNwM7CbvcCTwB/Xq1+aC5eAtEAHQMdBBNBfE4f7U3t1a01fBHbpeaCi7nk7WKimjOF1cAA8B8i8oKIfFtE3ECLUioAUFiW9QsVkY+IyH4R2T8wMFDFbmoWIoFogL2n9pLIJGh0NpLIJNh7ai+BaKA6F6xCVPzFRE8ozsMvBUikczTXOEikczz8UoCeUPx8d00zQ6opFCzAlcA3lFJXADFmoCpSSn1TKbVNKbWt6NKp0RTpGOjAa/XisXkwiQmPzYPX6qVjoKM6F6xSVPzFwoGuEDVOK16HFZMIXoeVGqeVA12h8901zQypplDoBrqVUs8W1n+EISTOiIgfoLCcXVjxeWK2qbMBnnjiCdrb29m0aRNvfOMbR/cTEe66667R9Ww2S1NTE29/+9ureUsLimAiiMs6VjXhsroIJoLVuWCVouIvFgYjKdz2sdpot93CYCR1nnqkOVeqJhSUUn3AaRG5pNB0PfAy8CDw/kLb+4GfV6sP1WS2qbOHh4f56Ec/yoMPPsjhw4f54Q9/OHqc2+3m0KFDJBIJAB599FGWLl06Pze2QPA5fcQzY1UT8Uwcn7NK0d9Vioq/WGj02omlsmPaYqksjV77eeqR5lyptvfRHwP/KSIvAu3A3wJ/D7xFRF4D3lJYrzqBaIA9J/dw38v3sefknlnrnm+77TYeeughUinjTWiq1Nmf//zn+eIXvzgmdfYPfvADbr31VpYvXw5Ac/PYweXGG2/kF7/4BQD3339/2TQai5n2pnYimQjRdJS8yhNNR4lkIrQ3tVfngm3bjSj4VBhU3lgmRox2DVuX1xNOZIgkM+SVIpLMEE5k2Lpce5wvNKoqFJRSHQW7wBal1C1KqZBSKqiUul4pta6wHKpmH6A6RsnZps5+9dVXCYVC7Ny5k61bt/K9731vzHHF1NnJZJIXX3yRq6+++pz7ejHi9/jZtWIXTquTwcQgTquTXSt2Vc/7qK4NNt0CVjdEzxjLTbdo76MCS+td3LTZj9Nmpj+cxGkzc9Nmv/Y+WoAsiojmUqMkMLrsGOiY1SBSVCHdfPPN7N69e0JNBJiYOruxsREw7AQHDhzgscceI5FIsGPHDl73utexfv16ALZs2UJnZyf3338/N9100zn38WLG7/FX1wV1PDoqfkqW1ru0ELgIWBRZUqtllJxN6uxly5Zxww034Ha7aWxs5LrrruPgwYNjjn3nO9/Jpz/9aa060mg088aimCkUjZKeEu+RuTBKVpo6e9++fTQ1NfGtb32Lb3/723z4wx/m5ptv5uMf/zjZbJZ0Os2zzz7LJz/5yTHHf+ADH6C2tpbNmzfzxBNPzKqvmsVJVQL8LpIgPh1sV55FMVOoplHyzjvv5ODBg9xxxx0TtpVLnf03f/M3DA0NsXHjRm644Qa2bNnC9u3b+dCHPjTqylpk2bJlfOITn5h1HzWLk6oE+F0kQXw62G5yFk3q7HlPiTCP6NTZmnLsObmHRCYxZoYcTUdxWp3cuOrGczvpSz82BIG95mxbKmwY3jcvnAqBDx7sIZHO4XVYR9siyQxOm5l3Xn7xuH/r1NlTMO9GSY3mPBNMBGl0No5pc1ldDCYGJzmiAmL9xgyhFJvH8MhaQAxGUjTXOMa0ue0W+sPJ89SjC4dFoT7SaBYjVQnwu0iC+HSw3eQsaKGwEFRf1UY/A81kVMWWdpEE8elgu8lZsOojh8NBMBjE5/NNCBhbLCilCAaDOByO6XfWVMx5sT9VwaOnGODXMdDBYGIQn9PHjtYds7uXYhDf6X2GysjdbNQ6n2fvo9l6DhWD7Q50hegPJ2n02rlmbaP2PmIBG5ozmQzd3d0kk4tbB+hwOFi2bBlWq3X6nTXTUvTY8Vq9uKwu4pk4kUykutHSRY8eZ62hn09HjbdvHTFdlqLnUI3TittuIZbKEk5kdAR1GRaVodlqtbJq1arz3Q3NRUa1ot+npDQtN5xdnt6nhUIZStN0A6PLA10hLRTmgAVtU9Bo5pp5T8kNOi33DNFpuquLFgoaTQnznpIbLhqPnvlCew5VFy0UNJoS5j0lN1w0Hj3zhfYcqi5aKGg0Jcx7Sm7QablniE7TXV0WrKFZo6kW5yX6XaflnhE6TXf10DMFjUaj0YyihYJGo9FoRtFCQaPRaDSjaKGg0Wg0mlG0UNBoNBrNKFooaDQajWYULRQ0Go1GM0pV4xREpBOIADkgq5TaJiINwAPASqATeI9SKlTNfmg0Go2mMuZjpvAmpVR7SfrWzwKPKaXWAY8V1jUajUZzAXA+1Ec3A/cWPt8L3HIe+qDRaDSaMlRbKChgr4gcEJGPFNpalFIBgMKybCpIEfmIiOwXkf0DAwNV7qZGo9FooPq5j96glOoVkWbgURE5UumBSqlvAt8Eo/JatTqo0WgWPrMuoTrLcqizLQ96IVHVmYJSqrew7Ad+CmwHzoiIH6Cw1JVENBrNOVMsoZrIJGh0NpLIJNh7ai+BaKCyExTLoWZi4Gkxlod/ZrRXQLE8aCKdo7nGQSKd4+GXAvSE4tMffAFSNaEgIm4R8RY/A7uAQ8CDwPsLu70f+Hm1+qDRaC5+SkuomsSEx+bBa/XSMdBR2QlKy6GKyVg6a432CigtD2oSweuwUuO0cqBrYTpVVlN91AL8VESK1/mBUuoREXkO+H8i8kGgC/gfVeyDRqO5yAkmgjQ6G8e0uawuBhODlZ0g1m/MEEqxeYzaFhUwGEnRXOMY0+a2W+gPJyu7/gVG1YSCUuoEcHmZ9iBwfbWuq9FoZs6sdfLlmKWevlKKJVQ9JXWuKy2h2hOKExi0kDh1Eoe3ntWNHnxu24zKoRbLg3od1tG2hVweVEc0azQLgEA0wJ6Te7jv5fvYc3JPZfry4dPw0o/hd98wlpPoyGetk5/s2hXo6XtCcR482MM9T5/gwYM956SHP9cSqkVbQK93Cw3mJPlEmBdOBRkaCs6oHOrFVh5UCwWN5gLnnAbtGRhPZ62TL0cFevq5MtCeawnVoi3A0rCcM8tuwOr00JAPcWIkP6NyqBdbeVBdjlOjucApHbSB0WXHQMeYgW+MCmjgGO02H357jbGxuDy9b8JgN2OdfCVqoQr09KUGWmB0eaArNOMBdSYlVIvuow8d7KW1zsnaJg8Nnlb63K3klaI/nGTbDNVcF1N5UD1T0GgucIKJIC7r2AHHZXURTARH1yfMJhJD7I11EkgNnz3I5jEG63EUdfKlTKqTr3QG4m429PKljNPTD0ZSuO1j30vddguDkVS5xzAnlM5OltY5iSSzPN8VYihqXHMh2wLmCi0UNJoLnEoG7QkqIFcjXoSOWNfZgyYxns5IJ1+p+2bbdkMvnwqDyhvLcXr6ooG2lGoPyqWzkzVNHpQCEeHYQHTB2wLmCi0UNJoLnEoG7QmzicZ1uLJZgvHgpINykRnp5GP9xoyjlHIzkLo2Qy9vdRsqI6t7gp7+fBhoS2cnDR47Vy6vo8ZhoXc4seBtAXOFtiloNBc4xUG7Y6CDwcQgPqePHa07xgzaE9wyXT7i/svwDXcbg7K7GVa/aVLjacU6+aJaqGijgMndN+vapjTWFg20B7pC9IeTNHrtXLO2saqD8nj30QaPHavFxBUr6nnn5Uurdt2FhBYKmkXPQvDRn27Qbm9qZ++pvYBhb4hn4kQsVnZs/UOY5b2U5vVZZlrO1fGnqG3AmCGko8YMZPWbzunc822g3bq8nodfMry23HYLsVSWcCLDNWsbpzly8SBKXfi55rZt26b2799/vruhuQgpGmi9Vu/ZwTQTqcilcVKKxlhn7diBcwZujufCbITbZMcWDbM1TuvoIJob6uKttV34GKlc4M1TINtUFIXb8TNRhpMZah0W1rZ4F3TyuukQkQMltWwqQs8UNIuaSt09Z0SpMRamdAedS2billlKqWBsdDYSz8TZe2ovu1bs4kBXfoLbaKRhOc/YVlWubikVkp4WQ0ge/lnVhWQppcJt3RLv6AzhYhYI54oWCppFzazz5pRjlrl05oPSmcHpyGlanC143BMF42Dkkgl5fXzZfsyn9kFCKnvrPwchOdepqOcyJuJiRwsFzaJmNnlzJmUmxtjzwPiZwcGBg4RTYdxWN/VOw/OnKBjHG2YdsV7qT+0pvPUvK/vWP0EVFTqOv2Ht2E6UCsnh0wSO7aVj6DBBkxlzzUZ6I5eyrKaV5hoHsVSWh18KTO4ZNHya4JGn6O05xaCqJdN6FZs2bhqzb7mkdalMjmdPBC+KGghziXZJ1SxqzjVvzpSM89EPhE+zp/8A96V7J89bVGGeorlgfEyDQ+roGU7y0JEO9p8aYiiWGhWM491GHX37CeNiuX9J2TiFsik50n0EIj1jO1EUksOnCXR8n73BDhI2J40mK4PHn6YvtpesDE2finr4NCP7f8ihzgBhi48GW46mrkd4ct/zY9JljI+JGIqm+N3JIexW80VRA2Eu0UJBs6g517w5U1Liox8YOsbe2EkS/s00Nqwrn7dolkVeZkppTMNQLMXwSB3pbI6cKUIqk+O3nb10jwRpb2qfkNfHmxvislVLjUyiRUriFMrmUWq6lI6RY+UD2U7voyMfwWuvxWN2YrK6yCs3rekgp2Ivj15i0kjn0/s4EbNgcdXitFnJWT2YXXWsTBweI0TGC7fDgTCiFJv8NRdFDYS5RKuPNIueczXQTknBR7/j5B68mbVTG7Ln2TBdqjI7MRjD56rHmVvDSGaANCPU2r3UqstG+zfGbfSl9YbQokQVU6IaK7XRhBIhOiOdhNNhYiQIDR0inw7jcy+hfd078Ne1wZGHCKosjabC+cN9+MOHMadDnI6dojZXx0jTFZNGOg8PdLOvO0M0k0CARo+dlQ1OGlSIIyVCZHxMRCqT4+rVPho8Z8+5kGsgzCVaKGgWHXMalzCNq2VFhux5NkyXxjSMxNM47FnEZOK6ltuot7eMJoUrMpM4haLAyeQyHBw8iNPiJJthYl6XAAAgAElEQVTN0p+NcsDh5aqlO0mYbewNHWJXTSt+dzO+eBfxfApPbBh6DuAymQlYvNRnsqx+9f/yUvqDhF2bJsQS9ITiPN9rIpUIg8WN2WSiL5xCJcOkmhomCJFS4fbgwR4S6dyY7TrvkYFWH2kWFXNaO6ACtU9FyeYqSB43l5SqzLCEUXkrW+qupd5uCKbSwXF8euuQtYVf5LYTTFvKpq8o2miOhI7gMBuzib54HytqVlBnr+NU5NTY1Nxt22k3eYmkRogOvkLebCZtMaFq61lv8xPBy5r+R8oamQ90hThsvoTlzjSufAxRijpTApIjPJNaO2W6jIutBsJcooWCZlExp7UDKkgOV5Ehu4LkcXON3+PnxlU38ifbPkCb7fVYVEPZwbFc/WFzw3Kecb4RXvdHsPndY2ZGRYGTyqdI5VLYzDYaXY3UO+qxm+1E0hGgJMtrXRv+9rvY5WvHmRxh0GLD6fXzjro17GhpZv2KZayxjZT1ChqMpBgwNXFqyVuora3DxxBxZeO3jmupXbJqSk+ii60Gwlyi1UeaBc9M1EEVqXMqjb6tQO1TSd6iUcP06X0V5Smajpn4+E+Xf+hc6g/7PX6uXXotiUwCj83DC/0vkMoZ+n2vzQuMnS0FLBY6fK0Efavx5XK0u1rxWwr9TQ5DTSFIruR7CVJLKNDMiaCTPqubZQ3X411iJZHOUa8Ua1o8E/pV7t61EJiIFgqaBc1U0bjlBMO0cQkzib6tMB6hIkP2NMnjKqU0crciH3+mHhzPtf5wqd1iuXc5+88YaWquarlqdLa0o3XH2O9vxRuJv/Jz9qpj7HKvwp9NQzwEWz8w5nsJSj2HTvawI32UAdtVHI7X8GpflrYGJ9k8rPa5tBpoFlSkPhKRJhFpqnZnNJqZMlN10LTqnErrBcB5UftMRzl1z2xcLc9V915qt8ipHFtbtrK1ZStZlR3j9jvm+2tah+fSW/BaPXSEjxnP/tpPwfLtY76XE8E4FlctnrpG3tbQzUZ/LQIEhpNsX1nPnVev0DOAWTDpTEFEBPgi8HFAAJOIZIGvKaX+9zz1T6OZkpmmqZhWnTMTT6A5VvvMBeei7ikyXg3nd/kJxAOkXAFei9rxRNewuqGNNzYnWdK9B44W1Gu1bTByeoK6rdwMqScU59GX+/jHfX1AH1nPq+xYsRJPMeyhcS0u32rj+7v0D84eWPK9RBIZal02sspFQ3aQnRuaue6SJvrDSe5+w+rZPD4NU6uP/hR4A3CVUuokgIisBr4hIp9USv1LJRcQETOwH+hRSr1dRFYBu4EG4HngLqVUejY3oVm8nEuaiinVOTNNUTFHap+54lzVPePVcL2RXn5x4hdc2Xwla32txGviRDJHuareypKT/31WvTbcRaDjPjoa2wja7PjiXbQHj+Jvv8t4LuPsAA/1+TkYq6HOZQUFh88IpwaPs6KugSV1DlY3urFZMxO/v5Lvxeu0kszk8JIg5fBVfI+ayphKffQ+4M6iQABQSp0A/qCwrVI+AbxSsv4l4F+UUuuAEPDBGZxLoxnDnKepKKMSCoS72SMJ7nv5vrNpKqqclqInFOfBgz3c8/QJHjzYU3H6hXNV94xXww0kB6i11TKQGBirlnvtv8ao1wKhE+y1ZglEejidCvHraCf/OtxBx8s/OmsHGDkNoU7iLz3IVSe/zubcUVw2C+FkmuDQUgKRIY4FBwlGkmOiqcdQ8r2s9rnIxkfIxYcZqm/X7qRzzFRCwaqUmjAHV0oNANYy+09ARJYBbwO+XVgX4PeAHxV2uRe4ZSYd1iwcAtEAe07uGTuYzpRpBt85T1MxroxkIJ9hr8dFwuY6G9dw5AECHd+vOC3FTAf48bEBM8nLc66uluPLeUbSEWpsNaMupFBwI431jSnH2RHvJWeycSwTIqvyNFm8mMw2dvc9ReDYXlA56H8FchmGTQ2klZnXD/8U00g3rwSiOEw+nOkryGQsdI70YcZKrdo28fsr+V58KsRlK/0MLL+BrlyDdiedY6ZSH02l0qlU3fMV4DOAt7DuA4aVUsXMVN1A2aTsIvIR4CMAy5cvr/BymguFmXoFlaVCT6A5T1NRohIy0lQkxqapiAzQofL4K0hLcS7eQLNN8zyZN9FUrrujarhsCgZfwxs6Sdgk1NStHD0+nonjcy8Zo17rAU5Eh0hgJh3LUeM0USsWBsx2OoYO489ZweYGqxOnPUvI6sWZDVI39AJ5dTV2k2CWRlptbbS6nNhyQi49yT2WfC8+YNe0T0JzLkwlFC4XkXCZdmFM4pPyiMjbgX6l1AER2Vly7HjKln5TSn0T+CYYldemu57mwmKq4jXF5ZRxBcOn4b+/BvFBcLdA41pwFfTMVS5WU0rRkD0US3FiMEY4kWHNcJB87biBaxJj9LkM8LMxFk/GdEK6vamdvUcegIHXcNlraXI1czp8gjXDAfINg8StDsONdN07CLz6SzryEbpSKV5OZojmozRbmyHWR2okQdZhweXbxAsjKdYNnAbvElpqrLR4nUTCYQYzddiSg1gE4uksDW47LTV2HFYTZ8JJrlih1UDnk0mFglLKPMtzvwF4p4jchCFEajBmDnUiYinMFpYBvbO8juYCZDKvoBPDJ+iP9089gyjOEGKD4GmGXNIQBG3bwVk/r8VqfE4fPSMhjgayuGxmap02QmE7lqEUQV/6bLbQSYzRMxngi2/yRxPH6UzWcknd5rKpJ0aZQYnL6SrM+T1+duGlw17DIFn8jno+5LmeQCLAYOAFfGt+j1XudvZ2j/BCKM3yXBx7IkGtvYVjpgjWTIhmZSVmsRPJW/EPDGKu2YTdNkwmOczJVI5VdRbWN1g4nG0l0u/AZhacdhurGt247RaG4xksJpO2DZxnpo1TKMQoXCEim0Vk+jDBAkqpzymllimlVgJ3AL9WSr0XeBy4rbDb+4Gfn0O/NRc4k+X8GUmPTB9XUPRJ9zRDLg1WF9hcMHhs3ovVtDe1c2RgALM5hd1iIpWPEfY0sVm8dAX6po1PGJ/HH8oP8KU5mdr9bYyk4jw38ATB5JnyhtQZptsebzOAklQTBfzZNDe6V3N9LAm9L3Ko92nIZbje1kSzbOfep0b4xdH/Zjjro9d5JY/lryBXfx2brcsZsDrpdTeSdy7BkmnBbHNxhdlF1+r3YjMparJB+mM53Ms2s31VE3e++zZuuWIZb7vMT63LSv9IkmxecdfrlmvbwHlmqjiFS4F/BVYCy4EXgGYReRL4hFJq5Byv+efAbhH568I5v3OO59FcwJRGtLqsLuKZOJGMYbwsNziVzRrqWwvdzxltZjvEzkCilcCSTXSc3DN9Wos5KBbv9/hZatlOTE4QzgzhtdaxrvntSH2W4Z7p4xO2Lq/n4ZcMA7vbbqFnKM7LfRFWNDh58GDPaAqK0jd5jw12rGzl5TP9HBo8yBta3zwm9QRQNt12IDVCx8HvEGxaO7nNoNR1N9yNb7jbMOK7myERJnDmBX5EjCHJk06lsfX1c8jVxpmTe7ksfhql9pMfqeekpQ3lqKd7OMG1OLHYVtJAPV2xEXJxE0ssbdSTYGDVFbzqaqE2+DzpkT7W1C6Dtu0sqWvjphojHYfVYuLKFfW68tkFgihVXl0vIr8D3q+UOioi24GPKaXeLyIfBt6qlLqt7IFVYNu2bWr//v3zdTnNHFHOsNkx0DGaE6dINB3FaXVy46objYaXfmy8+dprDBVS8BhE+8HdSKD9dvaGDuG1escImwkG7FIjdWmK53MoFl9Ms1zq+x9JZnDazFy1xlTePjLOP/9Afh2HozWcGoqzYYmHZQ3u0eLxN23283jgJzQ6GzHJ2cl7XuUZTAzyJv+tE3MZHb3XEJyF/QOpYfYOvUQuGeZM02qGEkNYzBZuX3877S3tY2wKLquLeLibyIkn2IULPyYw2wmEjvN1hnhF8jRYHLSKDZVOcyqtWBGFN0Y9/M4rRKxuzHkrh2Q9SVsd7ZaXMWWy1MXW4rGZ8Tqs2HIxenIpXqj3k1QjNOaFXVa4dUXNOQtozcwRkQNKqW0zOWYq9ZFTKXUUQCm1D9hc+Pwt4NJz7qVm0VDMxPkHl/4BN666cdSgOaOsoa4GaLkUWq+A1/8xHan+ytJazCRdxTRM5vvf1pgqn4a798AY1Y7PlmWX+h2bPGF2rPGx3OeZkIJiMnWb5Lxl3VOD1I5Jt90R6yKXTXIsHyebz9LkasKEid1HdxOIBia67p45wq5UFr/VA84GAvk0e9N9dJKjwWRHVI4T2QSDeSfNqRBd9jRBaWBZ0ootf4acKcOS3GlSuTgBawP+mBUvcawiLHVkiKT7+JVEGYxFacqb8EU6eC75Mi9nTFWvLKeZHVMJheMi8gUReb2I/BPQASAiVnQiPc05UlFcwbhYgdKc/ZXoxgFDZWQbZwIrKRs5Eybz/e9LHy0voMYFeBUFkrX3Odz2sf86xTKTkwnLbGJ5+VxG+XVjguyC8SBnEkGctctwWByICLX2WnIqNyowxwjpWBS/ewlYnSBCB3G8Ni+WXAacddhczWTMNcRUGExmslhBBGfey7p4A95sDBii1unhrRvfw0jLLbQ0NtBqHWEoZ+W5uqUoq59M1saSXA/+uibqXPX8euDErAS0pvpMNbh/APiLwt9BjMhkABczi2jWaMYwm6yhFae1mGm6imko5/v/eGCSvEuxPvBtGXsCm4dG6aF/khQUk+Vk2tORwF0zUZB0p3xw2dm8Sz5HPQdNWZpKnkMql8Ln8BEcOgb934Se50Ggo2YJeyJHOKUU1rSVzfE6+pwpNlpdtOVGOJlLIDghkyOpUozY3KyI27BZTORyeawpF9fls3Q4t+Jv2Em93Ue+xsrz2VYcDSZG4hmGso/Q4qnFZjWzLJsha6uhRkEgNTL6PObTi0xTOVMJhbBS6jPjG5VSIyJypIp90mgmZTID9o7WHWN3bNtuqCigbNnIuWBSATUuwAuAdJTWpSv4XSIDGAN70aZQLDNZTlg2ensmz2VUt/RsxbNogEf2/T2HBw9jEhNmkxmP1cMW7wp8vQchkQFnPR3RHr4deg53Ok1DBoJWL0+4U7RlbJxK5ljavJFBU5JQfIShXA4nXlZbG3irBeKkSIgVvyNBs8dJ0HMl/oK77ZpGN893DRs5iRwWXPF6Iukolza0kI15MeVSDCtFc/GZzLMXmaZyplIf7ReRq8c3isiHMBLZaTTzTsVpLaZQQc0Vk9pH1r2jbEpt34ZrZ5yCYia5jFpcLWRUhkw+Awoy+QzR4Ku0Z82GbUbl2ZM4Ta1YsOZtWMRESz5JfTbDiCXDkL2WZ9OXYLLtxOa9Apd3NS7TDtZGWnHWbMJfX0ObNUKjy0zfJXfxjuuuwmQSIskMdW4b61vc5PKKWoeVbUsup9adI0eCsGsFseQwsdQIv9e0+oJIMa6ZnKm8j64Bvg7sw3AjXQH8O0Zqik8qpbrnq5Pa+0hzoTJp6og5cIctUkkltT0n95DIJMjkM3SGO4mkI1hNVtYN9/H7KcBZB4PH+UTuFK3YGI7EURYnWZMNVJZTjhq2ON7Hr4e6uXKVBZ+zkRXuS1GZOro7X6U18iJX+DK0Ll2Bb8O1o/cyVd9e6DnBL177LX3RAdosFt7hMnOpDe19NI+ci/fRVBHNT4vIlcBfAceBKPBBpdTe2XVTo7l4mNQ+MocptSspG1mMIDeJiXqHMYvIqzyD8ccgm4FsErJxloiNMFnEbCYmTpL2RjK5MFn7RoZlPY0s5U2tqzBJISONHeo2XUZ/eC2br5lYq2Cqvl2xdDVXLNX1DRYa03kR/Q/gTuAbwJuB20Vkv1JqqOo902g0FTOpfWPJ5QS6D9ExdJggEeryWV5TSdwmO/0ZO5IfQCwJdo5EsGd+ycYVV09qwyg3KzBZR+jofIxg30F8+RztDZvwr92lZwELmKkimn8FJIA3K6VOisj/wqjC9pyIfKmQsE6jmXemyvY5gTlU48wVlaiDxjPmnvPQnkzhDEfpGLLxm8QqAhYLJm8nVy7zs7S2nngmTvfgYazhM+wZeQ0fOdaLibpYChdZei0mYvZe6rN5loXXU1+znmvXO2gwPc8vhixEGpaPMYavbXJPyPa6+/kXsdmeYmm0k0Z7LXGLjb3BDnZFz5wtsqNZcExlU3iXUuqnZdqXAP9cyGM0L2ibgqbIhMjcySKaYU6jmueK0lTapYPuVAbnMfecSRLveoYzWYUrtIzscBpbNspe2yZOW7txeXpp81lwJ/uJDh0jr7L4LA48rhaC6TQSzHDGmqBGCdvTeaJmC4O1K3l93VZuWLMSUmGCaQvPON84Rmgd6ApNiOr+3ZlfUTe8n8t8HrA4AYjmkjjzOW5s2gab3z0fj1QzBXNtU5ggEArtfcC8CQSNppTpsn2OoUx+oNH28yQUziWV9ph77juEx1FH51Cck6leGrNtmM0xnNYnsJkuY2TED8mDuLOvUp/P0WMxczobxz98ElvWQcii8OTdJMmQyWYRPHjjI/zO8ipqKEwwHcGXy3HVZW/C7zlrD/jl4T6aaxw4Yr3UDb2APRnkYK4LZ2wYWs7GarhMdgZzYYj1z2xGp7lgmEp99OJkmwCllNoyyXaNpmqcGD5BOB0mlonhtXlZ6V2JQrG/f//EwaeYWK+YPykZNn69ifC8qpN6QnF+9fIZDnYP81p/hEtavLS31dPgMTKlTlcrYUwa8lQYHLWoTIrBTIilFhOdtjj1uQzdZgexXCcbYr0M27Octlqw5CGXhyFyeIiRMJvxZBxYsZMnhzMTJCoujiNsyTXQKFbiVtuEdOYmgUOHD3Hp8ONEHLXU19bjyXVjzQ1BbJCQzUVntJvBWB8NuRwd7iEOWlJ4a5afe5ElzXlhKkPzaeBvMYor6SI3mvNOIBrgdOQ0ZjFTa68llUvx28BvSefStLhbJg4+7mYY7jLKQdrcYDJD34tgsoBYzubgqaI6qScU5/5nuzg5GKXWZaPGbuXomSiRZJZr1zXR4LFPWnS++Kb9cuA57IlhmhNWpL+HbDZDWmB51oYj5ydqCpHKmenLHSFnO0k6FyUhFnIKGpUwKIqUMlOrkmQwExVYnXGQEKjL93DUlGS58uBJRkBMeNq2g8VOR+dj+JWT4YFuTN0xYvGjPGVV2IlRP5Cm0bWUREOElwcO87xVkU5HsWFmqcnNA/lB1gVewuNqAZtn6hmd5oJiquC1vcA/AU8AHwXqlVKnin/z0TmNppSOgQ7W169HoUjlUtjNdoZTwwynh9lQv2Figry27dB3GETAYjdsDGYr1K+GoePzkoPnV68d5cXhJwlafs0A+/DVxVmSH2RD8FF8h+6h7viD5Ia6JgSjjdZXCPewOZEkEB3mV6Eu4vkRXAyQNmXxR0ysDT/DkuRxRsynaM53YclaSIkQNkHMBAMmRV4B5EiYTdTkLDTnLNhzaTz5YYbEwbDVymZMMNINvnXg8uHKJAme+g1kYvwukqPTepwVdFEvdpL5DKesp/A6LLxuSTvPW7JksknqMbPU5qXfXUfEZKGfDAy+NnpPZXNUaS44prIpfBX4qoiswCiS8x8i4gDuB3YrpV6dpz5qFjgV65an8RQKJoK0elpxW910hjsJp8MopWhxtVDvPDuojtZnWNUGvlWGuig5Avk0NG4ARw0kho2+qSwdgWcImhMT+tYTivPMgRdInvgt3myI+uZlbLjqepasWF/RPT9++nF+0vkYqYyL5WYfTZFOzJmHeZvKcia7llPpVq6WFG8z76NWVmCkFTMo2hFUbwcDERvR/nrcphAvWsxclWvhTeksCZWmnhhZ5SSKEyc5WnNxem1CwpTFkQdRkBGoz8OaZA2whksSOZLmk4SsNupsdWx2OrAt3QxKGeVPWUu8/7CRT8pew6HEQby2WpyOelZIEF/NJcTzKU5kAjgTNlpcfpowITY3ICTzGYKpIYL5jKHuKlA2R5XmgmPabKeFWcGXgC+JyBXAPcAXgdmW69QsAqarDTxKqaeQp8XwFBqn2in64tc76kcDtH7b+9sJ1xwz+DRecrY2Q9ezRmnPbBIcNUYNgsEX8NpqJvQtn6nloSefpfH0I7idtSTsjcT6B1CPfR+uv2tKwVC8586RTqO+cypEMvEsyuynJpvlmDXN6+jG4l/K5WuXMzQUZP/Tj/Bi3fWj3j7BRBBz3ktvdy9nMk7yKo+XHAMKglk7AzkHSTwMm71g72YtA/TbMwybwaKgJmsmaYY8OTamwWT30dW4jB1n6gmbGrgkmcWOC3c+jWn55bycG4FsGlfoCPH4IJHhk+xYthOAkClMJJXgmDWHLxGkLtOMGS8pGSaYEHz1q0lFenHkM2CyYRcLFjFjVjmiFjsulZ88R5XmgmNaoVBIlX0DxmzheuBJjChnjWZaKvYWqsBTqFwyvAZHAwpFNB0tnyCvNDGebzV0Pm18XnENHcNH8ebzeFouM3TpJX3LhLfgDb4AzjqU3YsNSJvqCGajdL/05JRCoXjPmXyGpbU1WEOnieVt9EmcZfk0ATFjsrlYbQoQjLXxQl+ahnyI5uUOuodi/PqVfoKpVwjHDlGX68dKBqslg12lWZe1YM3BM1Y3m80ZYuYIR20RRsxm0iI48lnyAva8l9pMHodKE7TCapMVW81qaureysCRp4jaMhz3jDDo9uGKhNhhcRMdOsKgxYrP5WNHIom/90UCYiJuiRJNZ6kTJ2FnM4HMIP5smMv8q/G2biCt0hzLRCEcwG5VjKgsNWLmdvdaAk0bx2R91faEC5+pvI/eghHN/DaM/Ee7gY8opWLz1DfNRUCp50woEaIzclbt097Ujj+bNQb+wz+B2mWGTttd8LQZl165XHrp29YbBQCLbZLzQmILezoSNHp72Lrcx9JNhRTT6RiseIPhNqGyBPNZGpe/AVxnVRpF1VM6kqI+HSTnOpvJ02YWYhk7mXBfRffstXlJ59Iscyv6zR7OJOMMY2WFzcTaxia8xHhuMIrXlAJ3M8OxNK+eiZEeeRFSHWTMaaIIaVuUmEkI5xRvjMdYmosxmF/PIUeCEVeQiFhICKREkTKDCTNiSnAZXmzJPNFcgvWRXtKx/2bQsYFXmrfR582yKtlFq9nL6VyEBwL7udRqY1X9Bto9K/A7lkLnU3T0PsvmOj+HVTf5RIKIfSUZu4lhl4VrX/cxAPpP7WVt8+X0Wz0MhE5gzqW4Y8m1tF96G+06gG3BMdVM4S+AHwCf1mktNOdKUeWTyWU4OHgQp8WJ3WRHodh75AF2ReP4a5ZBTRskhwmceoqOumaCZsGHmfaatZS+W06Wa8ifzRI88xRHj78I7j7iLVsJppt5+KWAERhWJpDKd3IP8UyC0lI8RdVTxmsnavNhz8RRdi8A6ZzCLSmsNUsmnKs0Srk3K6TqQqz0rjTu2eGg0ZrGV1fHUpODXdE4khzheMrK4Ug3fnuK2JqrOdg9zJlwEkv4ScxSg9uqyNr6iYsJVz6PQ+XpslqozafZmj/Gr131DFkUeQFnXsiYFFkT5AFRWcKZMHFzngRmlMnMW2PDXDpyHwMb34qzto20u4XEyGGCuT4suTzhhmYSVjt7Q4fZVb8J/4prCJ78L1qVCXfdajqbrbgF3FY3Nbaa0e+hKKhtZhuXt12j4xEWOFMZmucu8bxm0VJU+XSOdOIwG7n3k7kklzddjjXwIh0qj99eA03rCHT+hr25IbzhNI31q+iNneHJfJS23/4tq9Np2sWFv37NxNiCgj2ia1ChPC14JUVd7y8xLb0BnE1jAsNKB2+zbQkjsp9ltWNrM6yuXc3h6H6e9ozQMnSCNtpwShP5VASfI8OyzW8cc4+lUcrNNQ6ykUt49tXd+KxnSObC9OdSSDrGm2xNvMmsqEkLZ4bOEK/fiNtTwz7LRo6dFI6eGcRigtXmIdLZBiKWITanU/RbTCCQxYRJmTllU6xPZUnb6kgSx0kGpUwkMZNWZswCKHjVAua8iSaFMdjXm9g1EiQ9cpi6xrcAafpyw1jzJsxWG0eS/UTNJqxi4fHhI/x+3aX4/FcS92+h3uahaMov1tQuUlHRJM2CQZfV1FSVosrnax1fQ5RQY6+hxdlieA+FjqKsDtpTl+B3+ehwufEOduMZ6SOk8hzz1GI2mQn3vUjA6eM36RBtkWOs6vkN7Vveh791Kz2hOIFnHiERS3M8bGKlD0TSuCOdrHv5q9S2XMsx26XA0gmDdyxlYTC8mZR6mXj4t5jSUeImE9/pfopGbxuXr13DqW4XHQNHuDKdYFXzeho2vIF9w24GT58YkwKiNErZEu3DHj5ONzlcLjv1Ag3xEVZG8+QkynByBKfZTNztw1x7NcdPj3Ak+jRJRwjJ1xDLO7BLnKwpgyVvoimb5ZTNhCMvOFWOkMVExGLmysQQj3vspHJJ0ibBBqBMZMhjEjO+TJacWFiOE4dYieaS/MqVpiV6FFuP0JBPcVRiZJWVmCWPNxmlxtZI0mrm8eAhSIQYbljJ6TPPsa52Ha3eVm0wXgRooaCpOn6Pn2uXXmvk+y9VI9k8qHyavWf2cXl4hKciRxCTmRpvAzGriXxsgJFwmiO5OJ5MiIZslrAaJGGrZW/Ht9ii6tl/XLE1NoDL24I1FuVUVxfu/FHCJgdxU5znzM8TyzzGcz98ltPhDbhNPlY0etjsDrMueZhLh45Re/I01jUb2GvL0x/voymbRhw+unJHaL/kcqwb1+G0OllX98aCUMmNJoV7+KUAkWSG9UsMw/hQNMVrXXuokQa84qFerDD4Gi5bLcftCn/Owcmsg1a3HU/4VXyZEDkTiMmDStdgMmfoNdewnNPYlKJfPPgJ0pgDe95G2JymPqe4JmUjbLKwz20lbYIcGfKAyZTHbvbhyuZwywhxkwmnWJFchvpsgj6LHZunAVK9pNJpvJ56eh1xzOJgqaMByURJ5FPEyPKau4arl1yBI9rLq6FXSeQSrK5brQ3GFzlaKGjmhbJqJGctl2f7cgsAACAASURBVEeGiQWP8UB6CEf+/2fvzYIku877zt855y55c8/aq7qrq7rRDTTQQAMgSAiUREogJdDgjGzaomSFIhRShGxG6MGa7WEcjpiYmIh58LzMxIRnHDMca2I0tjWiLMuWFyEEWgRBgtiXbiy9oLfqrr0qK/e8+zlnHrK6CZAAJYosgEDnL6K78mbezHs66/b97rf9vxyRp6TG8IYdUnACRNKnLwVR2qclJYkscRTDSvMtnnrhf+JI9WdZCgrM5SETRQ+zfo6m3OR8MeY1F4b9kDsLM9juBTZ1lwYPcWr7bRZ7f0o9UBTNgBCPt7Zfo9I4QiYUVa+ECNsMgzm+ceVN6vIEOBvsFE9SDVyy3PDKVpvtbkw7TBkmGbuDlFPzVc6udUizXXIziedodN8wq2M6kUeebZEWpik5EaJ5ibJtslIscq8tMS+PkCmD9SucTRqsmRJz+RbXSntcVwW0tLjWMJ8Lvjj0KdkChxqzfD72+Ya7TuIIHMdjShtKMsB1XNpORjmJQCcokxBjmdCaWtnyED5PFAx9s0srzzlRmKVYmCBO+6wogRJFLgzWKOyeZbm6zMNzDxO4AY8fffxDPpPGHDQHZhT2G92+Bfj7x/lja+1/L4Q4yqiSaYLRWM/fsNamB7WOMT8ZzJfnuX/qfr5x4xskeUKj0ODB6lEaextci5vk2ZCT+JwNSgQWbDKgZTXC5kgNJeESYbiSD8gzTUkr4vY2YRDxLSfkFwch+Z6mLq/yraLDjiMItE9Ajys53OVMUPWquOlZHmm9SssXPCsNrt5gMs25vuew1Gnhzhwi8RUi7LMeT2PEkLlqjjU1nrnc5PShKtf2QqyF5iAGBElu2exEbHdi1jshd3oNpBiiqLLVT6gpDyFCahr2ekMWs2uIpE3iBXSk4FiyTlHkXFHHcGzOJ+UNXnUW2LK/yMDpo8QeFRuSI1hzJH9WkkxGguXGDD/dthwOM572Fet6jQlVZMo9hiWmpVsE7hxm0CPNusSOywm3TCAsZ+MdZv0qAo+W43Al3UXrlKlCnUQPqXpVqm6VVKec3T3L6anThHn4YZ9GYz4ADtJTSIDPWWsH+70OzwghngD+a+B/sdb+oRDi/wB+m9EQnzEfYd6za/lmuelwhzMm5g/Dq2ijKbtlannOyuUnqA0H7AnDhPRoaMn9+KxIi9IxoY5Z9CbI0i4pBgykWtPNEzIxi69zLm/mnJif5ZtByL3tFzjnS4pGEXsBUrs4WCpZn62gyoRfwUQv8Rdek7dLUErhwSwjk4ZtkRPEEfXVAeerDXAnEOWMguMTmyGn658h60mevdri2HSZjU6E74z++0yVJTMVn7V2RJJbouJPM+n8R9JU4kqPLeszK/dYtLNM6W1sMtIYatka9bRFXwUEwnDI6XGdQ+Qi4WS6QauyQtN2sLaIFVNUnRxDzEWp+T2/xBvDa8QmQKg7+ZvJDkM7xVOFWXpZzkNzh3lEzPIXvYuEJmbCmWBJ+SgkxB20E3A5bhIUJ7gnWOBCuE5HD5konaASSzKbMVOcoeCMvLqL7Ys8PD+eqXw78IO0j34k7IjB/qa7/8cCnwP+eP/53we+dFBrGPPBcEunJ4uYCqaIsognL3yNzTP/HLIhm67P13aeQ3XXORbMEqddVnffQFvLBd9HWc2sBoymkYQ8iMdpVWQeRa2+hOfXscLi5hZpBD2nQO5AQ83SjlIu7WS83o1YY5YX3WNYWUHmktxarLVUbcauLJHLPYzb4YavKOeSgk14O3CxpNyRpFzyXAIE9/e22YwUvWyPucIyp+ufoeHPcvdchb1BisUSJvm+BpPmUKOAAeZrBe6er1BtnMYv/jK58Sm4Lbq6hi79Okn5ExTTPbTJCb0ZigWXh3TOnhOAawlEwkRNUilr/q6zh3ZDjHTxhKAuhyRelY4zzY4L65Uj7M7+Mv3GHTRru/TlkLb/Ke727ufx0kP86sKD/MLcJ/ldXeFzqs5iaZ55I3iMMkYpdmxI4LgUSjOUreFkcAhZnOTycIPFyiLTwTRKKuz+d9iMmjww/cCHfaqN+QA40JyCEEIBrwDHgf+d0aznjrU2399lDTj0Pu/9CvAVgCNHjhzkMsf8iDy1+hQr3RUyk92Ss670d2+Vmz61/RyrcQs3jwnCXeaQdLKYbZmxIBx+LYaznmQgJEWd0Y37RI7H0Ano9bscKi4wFfm8nYYgDZ50uUNX2ApO4mnJ3s4Kpwsd7pW7DE3Irltn0oSsOx0So+h6k8S2RD+6xpFskkjtMmMjykJjsVxxPO5LMrrGxZEFOsLnPjVBn7/Dz8zeTWuQ8NJmi2Yvph44DOMcK0ZSQUenSjhS4jmSONN4ruLUfJUrzXvZ6S1iBZwotblbXiKNMi7J48zINYoqJxNFimqOz6QZ5zxDXPYICoJ63OG1mke5rIjjFNe6CG3w0h4d11CUAfOVGVpUqM3+NDLaYMM8yy+kF1lVRZ4PPda2K6M+DwGP10+CzqCQwWCLybTPWZsyPXk3zI8U8J085pR06Kd9PjH7CTKd3Wo0dKU7Ti7fRhyoUbDWauABIUQd+DfA3e+12/u896vAV2E0ee3AFjnmR2JzsMlzG88xHUxT9aok0R5nt15nqb3Jdc/larjDtwbX8JIQR7nkJqepQ47ko6lfp7XP4dRQjIdcLMBGeYZNtYCUGccrx7gYr7AyHDDtF1mwZQKbYVSRVe8u1iKfMjssuxc5HhzlG6qG0Tvsqg5BPsPhtEi/MCAuaI5bl51wHUcM6Is2K8JjIYeZPKYrFVecwxhdZTEtc9zzKQRz/F9hhdW9IRe2ekgpcRzJQ0t13t4Z8skjdTa7Mbm2pHnOYqPMINaAxXUkDy01mK14XL50gf9MvUypPsWgvsR6HHPU79DPHYbOFIGJWYwu42VTXKrcw8V8l1mbEPt3UUmG3GAPYXPqThHXJhihqTtLfGL+BHt9FztsMtu7wK5bYDuPeTZfo5pLppI7Ccl5sljgsUKZ+fbaSD588gQPDEo8Ha/QrUxSsyPF2SiPOF47zlJ1iX7Wp+JWuH/6/lslqI8ujtuWbhc+kOoja21HCPFN4BGgLoRw9r2Fw8DGB7GGMQfDmd0zTAVTCCEQ6YBCZ5XQ5DztJNxlBb3udQITM5SC3BoqGFzlsWIyprKEk6YOnsLGIam2DDJB002wleMkns8EkwzzEJNlFAqG090jWDnBN1OBFj0Wsl1q9hBv5h2UdahxgiN2hVX3OiuyTKEwzT1UONV5jX9f90HVKIeCvmyy4RqcFLCKDoL7cxeVR2y789Rlg589PsXVvSGZsUyXXCaLLnthRphkfONCk2rgUHAVx6aKLDSKt+SvX7nRZqcXs9Ao8sihLVa2ArbWUiZFkwnTopu7FPUQnbVJRMCb1Z9jIMt05HXmoxaNbECr+zJFr8yUG9BzEgb5AF8UWCgs0SgVeG77GyS5ptgOSZ0q04UFnhV9nDBn1tPIuEX5ri9CNuTM5hvMz9wN/U3obzEvXH7tvq/wtd4b7Ia7TAQTHK8dRyl16+L/TimRsZdwe3GQ1UfTQLZvEALgFxiprT4FfJlRBdJvAn96UGsYc/DsRXvcVZjh9fVnGPY26FnDitBk1vLzeNwgYSnLuey5oDW5ho6VGGv5rdjjkCvpxms8HUii2oM04xJ79hq78VtMiTuoedOUbRs7vMxybZ457kD11/jtvfOcVzUG7jovFV2MDZiVk0S2SIUSkRrS9ysU3IC9bJd/XhjgxCl7zoC6iZnPhrSU5LJX4LGh5hPDXfrUWXEXUdry0mCJ/+KeWf78rS0+tTxBZ5jy6o0O2hhyC54jOFQvcHKuipSCh440bnVNv7N7+vqbW0xPzlLt7sL6WfrapyenOeQW2AxO0j/6ReLSIWxnlWH3n7KQabYLdS7nWwTJkJOJZFid4N7qEe4//Rv82e4rvLL9ChW3Qsn16ahVenrIF9IjXJVN3KkGOzKjYgzLQlKrHqaZR+AtjGZKHH4YFh/mgfoisz9A0nxsBG5fDtJTmAd+fz+vIIE/stb+ByHEOeAPhRD/I/Aa8HsHuIYxB8ykgWj7TZZVhe+YhMwYTBZSxOEvpAFlqdiMaVPiOpJJKZgQlolekdnMIRcZr1QmkP4EfuUIOh2SCUUhj0nTJuWkjd9fpS+ha/ukjWusRm/SKqd0nCGz2mGCNi0tuSG3mdRTaG+PjhRIZaipAmkSs+nmTGeapVCSOiGbjsNyajihLZ9LZtgWASVX0PNmuVw4xVZa5+vntnhzvYu/M0BrQ+ApNjopUkgmyy5F32Wrn3ByrvKeM5ZfudFmwXWp7jxPunMRjQPePKnJ2bFVthOf/NKzJCe/xGLrDCKN6fs+b5suLSnQQuJYWNQpFenyxPrTZI7PicYJemmPOO5QxzJpQ4rqPEMZ080MwuQo5bG5+Rynp04zP3Ec3qO/YCxPMea9ODCjYK19HXjwPZ6/Coxr2z4mPBAnPCklm3nIEQLyqE3iSLQwCCRkKYOgynqeEBAQuQFJ0qQqLf++6HMs3WZdS0qZ4nrrBfpk9OIUgyZMIlLtIkwbIw2FZotTfp8pt8y1So9BPiQ3yxTyPnXZJ7eTuMUeO+Tk2ifQLntpRmwsZSEZyJw7rKCcCyIh0CjmTJE4y0gLh9GTywzmvoTbT8j3hlzcHnBvqcf1S39OM79KGHispUuE+jRHaofJtCbJ9PvOWI52VqjkLZJhG2U0VrkczldoyQl2g5MsDK9yWLYw2STGrLHRE7zUSLlKj4qQgEPsGHak4jUb8dbGC1Sqh1mqLnG0MAXDDrY4T3e4zUvJNrnRZJ5PSThQqLM53MRiefzYuOFszF+dAytJHXN7MJ+nPDb1IInJ6GU5vhDMWYVvBa4SIAReXqAnHVJpCfKMgdW85Qy5IXZ4xZW87ipeNnuESZM2OaFKaMmElu0xYI+CNWigieG5wVXeDlfQtksFlxvEdJNDZOQUbELmQUdUkDZiZtgiiLYZGEMx12ggw5AZAVbT9lzu1kUwGTOyx8BtEKWaK80BEli79jaDt79Gs7jCwAkQoeUOc45q+Tm0bHNpe4gQvO+M5TuSc3T8BS7692OVy4TeoaR7TOZ7zA4vUDAD4tIcro6Zt1ucLPgUc0vFWFIhKCnBpFKskvFyuI4wOalOudq9yqC9Ao5P4hXxynO0sMxKlxN4ONVD5Mqh4laQQo69gTE/FGOZizE/GqUZ5rMhn6nfybWtPWpeg0G+wZKBDQS5VwWjuSO4ExlusJfvsiVSXAUD6VDVml2R4yhBkZzY9ImlIBUaxyossOZCIjQzuSXGUtMDWtqllGscGRCIWfrJcc57BRZ0wmTaZ0CZ1AgCHSFFxqassBhrpBNyRSkyK6jlhldUG68aUychC1cpqk3SzCcoutyjL3ClEDGMA8o6oUGLCRExGQ25KBvAz5Pmhl6U8bPHp77vqzleDHk58lFKkLpVeiYlsgUOsYtJc3JnBjl5F7lbple7i1r2GkWR8kWvwhsmxlrDmxiE9MEaDgczrJoEqy3rScSR8jzdPGSpMMkEGtwSJZ1xdGpU5BdlEbH+fg9mzJgfxNhTGPOjsfgwRF0ecBrEnstAp/iqSDOYJXJKVIwDekBl+DpRsklTaHxjiNB0BThC4QhLR0JfWIZSEBjwDORSM5RgLLgGcmHpKEnBQj1TtJyMWbqURMqGOISjZ7h3e4472ndTy+vk1jJQloJxqNock9bYs2AxDIRg19FcLIT4jstF/xjXSnvcET7BUa/NUDfpOa+y7lykLN5kibeoyoSuLVLNQ6bi1yjmF1lLnyMpPs3rnafZHGy+66upTx/mE3Med6ot2rJBKzhG1c3xzZCiDVlwhhQLo/uyuLhAq3wnhepd+KrEcm64pAQtKYnRTAifwzOnuKtxF3W/zkAY4izkocoSvzLzKT5VPUo37RErF2stcR7TTbvcO3Xvh3FWjPkIM/YUxvy1eJeshSd4YPMCf1vnPBe1SCsNtomZyR0Wkj43fHgbw0khGBhNzOjEc61moBwKBoxkf9C8wrOGSCl8o3EA3woUFg20hMt5V9EVkKOZyntc8reoyAEmOsFUtsNdps8nB9d4pixpKcHhXPPL/ZCvFR1WnIBpndEgQaLpOC6vyQqz1BmmLmd0n1PeWZoqpBS3mcoFqcy5XICTWRdrct4uanJ/i8D839Sy40ybz426uL939vTiw0z0/i0TtYQjE3Nc3r7A2ahNp1hiUvocS0IqO6/Sn36QyCja3iI/9TNf4Znt/8j5rVeJOldwdUxmDXGhxFBIGoUGucn53OLnebzbHo0w9co8Ghym2VunXZqim3bxpMdSdWncXzDmh2ZsFMb80NyUtai4FaasJGye50lf8tj9X+aLrT30jW9SoU4shmT1GZazAT0TErmaGQOXrMFBUc5zEmEJ7EgDJQGMlWRoEKMcgEEjhUJZi0TQl5p1MfIcJnNYcxXTkWTR9NDiedqVjLMi56ojmbEpnwotysCa51KWXfJkgaLjEap1SjojlxV2peaBZI1VM4/T8JF2g4ekT6c3x3S+QdPLMbhccjIy2SIXBXzjg7VEXpeNy09x6oG/AW7l3bOn64tw6kvQXWWwd4nXxRqVSo1DqkAY7vFyoDlNRGn3TeTEHSz91K8yd+gYz+84dEyC8KuURZXMZITk3OjdYL48j6McHlj+PNzUlhpsM19e4FeW/ivOJDvvWWI6ZsxflbFRGPNDc3Mwfdkrw9ablAt1EIIz4RqPL5xGmDUeLkwg0z5t5bCy+xbFNGQTzVKWURYaicIIgbKayVziasWuC4415AKslQgEChBCEyEQWKrawdeSyMnY9iyVTBF7PYSxaCek5QqsgcOpwKC44BsejGP6eoKBk1BKBxQzyMlJRU5o+6RCcEOkVE2HcnY3m8rjkE6I67MMYgl6QNtJ2VVwVyxYyOvsuCl1VSD0S1xLI041L1FcfJhm1Hz3l1VfhJ/+B5x54nep4FA2BuImZZ2AdNgVV/lk4xT8wm/emia3Oljl5MRJjDVcbF2kHbfpJB2aUZNhPuTv3/v332149pnf/zNmzI/C2CjcbnRWbymXUpqBxYdZt5O3RlROVXwWpxK20ovve8d5czA9wEr3Os92VtiNmyQY/ij/V8QSJjQ07CQrgUuiLGmWkJNhpMPnc3hNamLpEMQ16onlsNNhPS+TINn2Y7TNSQXMaEvRwLaCnpIEJidxHKZyQ0dBpDRtL6EaCoy0eFqz6ilca9BIJIo3fI/joSXIXYyKKZqELIdrRYUyOXMadlCcL4TcHZ3HWfwkO/0Um/e44Q6ZDI5wVLdITYiSGVMFDyNzBoUywigSJ4ekd2u+8/dRX2TPdZkyOeQRWA1uQFEImkqB++7+BsToh8WS6pTYxHjSwxc+U8EU59vnOTV9auwFjDkQxonm24n9WcZkQyjPQjak+/K/4ukXXyVKR9PEtgabfPWVf8tGt/NdxdPrT74riToZTBJmISvtFZ7oXKQf7hAJQyg0152cQp5wQ8FLXptWusUwSfCly7Jx2DOai0GJY8E081owY5sUhKDQf4hH+tN8NulR0S4zySIPhlU8IBQuLoKiVlgEdZ3hWYlnBaEUlLQmUzkJoIVEW0FXgWMVOZLLnkvPSbkj9VnMwQhF34NqDmXr4BlFpGIWcouSkuXhJjulkKi4x4l6gOsXaKsiS9ZHqAIbMmaieJyBkOxmPTIx5OvhOi9uvch88b0v1JPSI6zudxV7JXACQguTeQ6zp0aGep97J++lm3ZZH6xjMNS8GmW/zB2NO5grzdGKW5zZPXPAJ8uY25Wxp3A7sfriKDGpM1h9CZIurXbKohPxjfRL9KOMjnwN6WheXD/H9MBS8SpMB9PvipXfnKL24vaLqDilL2AowLNgEOw6khNpwiuBg6egYLvkWpH4VXyj2Mp6PMgMR0KNNB025B7Kb3NNBExmHo8M+yhiNvyQnnYwUhBJCIGulLQsVHKXio0xQlLThlAJukLSdkEaSUtByWh8a9BWcaWg+LvtDA28UpiiqdocyXPmTJGWk+CRk7kN0mLA4VwTpy5/nmkmTcqkFNw/cw/l2gwvrD3PsLdJfbDNTKHIBadPQwZMTNzBbO0Y31r/Fuda5zCta0xun+eBOGa+fowHjM+TUoLyKGIIdUxfCT4dHBmFgAbbnNk+wxMrT3Ctc41e2iPJE2IdU3AK1Lwai+VFfOXTTbrsRXsf6qk05uPL2CjcTgx3QDqw9jJ4RSjU6IWb1MJnCNyHkeUFrvXXiIbXmLERacdyDcU5v8bdh+7h8aOP36o6GqQD1oZrCBvTdySuMRSsxSBoK0lgM5T1sWga2oJ0GFrFTt6hYASpVlSyLpkMWXd9ClJwOhqSuX3eKHhsO4ZtR+FaSyYskZBYoXEspEDHMfRRlIxhyxFkElxrUUYgMaQoVjzwLSxGHtoE/LvCLJNqA19nHA99Zm1KiZB1x+DLEk6pjBCaF8hYGbSQImFi5hcoOTNcHzT5xN5l7q0usO0X8ZKISrzJf16/i8PHHoXiJO2ozVutt2g33+anti4T+gWe9F0eC3eY373KY/P3ciZNaeoBk4UGny7MMl85BOmAMybmn735z6h5Ne6o38HWcItzrXMEKmAymORw+TAlr0Scx3jKe+8w1ZgxPwbGRuF2ojQD1749Mgj7cezYWnJngjvSc5wXh3BMk4lkm0wXSf0yVaER4QYXVj1eO3KVZ67/Aasbr7IdbhHbiFRZCkZihUQj0FgM8GzgMJQ5ElhxNFrEiCwhF5aiKTAIQw7ZkIuOS0E7IHKEjLjieTjW0pMWaWEoJdKOHrsWcixaCqQBgUJYRaw0gdF4FqQV9JVCWMBCagtc93xc43A0VZzMU3zR4prjs24lR3VOXUo6Xglh45EXJWfwHY8Zrbkevsly6V4WkzVWyGkUS/zWzKeY9+v8i7WnmCpMQHF0gV7pr1DzaqTrTyPzfJRMVoozgWC+cYT5vSvM3/VF2D4HUoIxUJmDqMsTepeaV8ORDtf714nyiAl/gjiPqfk1pJBEWUQ37bJcXR4PvBlzYIxzCrcTiw/DcHs0HcZaGOyyEK/gJh2mNr/FxrW3YbDFJhFbbocd06ZDhlUuc3rIvznzNc5f/yYb4TqeVMzgkQgYkII1xMBACjQQSkkmBKEQdBTEQhNhCSW03SFXgxV2nJRMajwSysaw7kkMllXXYSAlVWNx9idpSDEyNlYIJjKFGCkr4SKYzC1GCECgpaJgRnc7yoIUDrg5mcyYE9v4aKT1qecBDSNRVlERHrnO8IRionKIGA9LwvHgKEdL99HLmiS6Sa4LPNY4xbxfB2AymCB8Rxinn/YhHVAJW6AccHyKFvYGm+DXRoa4tgiNZfCrMLEMtcNw6kts5UMkkuu96+Qmp+gUKbklMpNxvHacWMfEJuah2Yf48p1fHieZxxwYY0/hdqK+CEcfhZ23oLsOg22S0gL9gcBayz3Zk3zHaePoAL8giElJTMYJNc8xfJ5vv4y0MdIKuvmQOM/wMEQChNVIwLEWCzjGkCh1s5AGLQClcTUgLUMB3ywH1HJNKC2N3LLrjCa2RhKKxiIAiSURAovASpBG4QrwgUCPTuBECKL98tUcKFiI8XBETgGFK1xiDLnfQ8YCX8GUMOyogGPDGb6gUzbdIn+id0miNlVbYtqUiUqLdLMmnbSJpya5x5m4ZRA2kw7tqMXzgxUm3/yX3ClLuGZIN+ny8L60B8ojlJJJfGhfhbn74L5ffs9fzVxpjgt7F/AdH200naTDIB3gSY9aocbvPPg7B3pqjBlzk7FRuN04+TjoBPauQLHBcK1DId0ilGW0f4H7siFnnCqOqbHgFBnahI7uYk3ARrZChxQJVI1GGQVS4gjNQmaIbUDXTShZQ4LAvOOwltGdOwpyC01XMBSKFcdhLs+YEJo9RxELibKWiX1ROG0hlwJpQdjRZzQVYC2Zo6lpgxYCiaAnR1pJoQPSWDwbMJsXiZRBKkUiMso2YWBrDIRgPulzX7qF21NUln+Nz6QBUdLBFZrn3AWuxet4SlJSk2xQxecC003BubzDc51LTFnNKQK2TMaz2Q73eBNMhF3c8hHM3iVCDH3l8GktIWzBvV9+31/L48uP88LmCwR5wFAPMcZgrGGpusRzG8/x6OKjY+9gzAfCOHx0u3Gzy1bHDMMhnUFIzcmYMbtEuksxCZnWe+yadc4k19jMdmglWzydvETN5oBGW01bQkeCAsoZGAG+NaOrv7GEUiIZldy/01vIALkfVkoljKrxJRd8Fw24WAJr6KvR3b+REs9YfDuSwNBi9DkumpIx9KWgrwTKGnI5Wsco4mQYmipbbpVcGbQ7ZNuVtKSDECEeezyUhAQyo+AXGEY3aBdrfDMo8GqlTOrcwFWSMMuZKx7hwTvvwTt8mq913+BS7zrTfg3plLjuwsnKMo+WFllOYr4cQ9Bfozl5lEAVeCzOmFdFOPV34Mj7K8Y/MPsAX1j+ApGOSHRC4Aacnj5N4AREWcQ/ee2f8MS1J75PX2nMmB83Y0/hdqS+yN78Z/n62auUoldomB6xDBBC8kbJRVpJmRxpC6TW0LUhAQZH52QKciEQQCo0NW2RUtAGrEwIpSRUozv2m56C+N7jW8vNyUvCjprShDUgBDAyKAbIpSQBkALHgm/AM4KpPGHVUWgxuqdxDERKEhhNyYxyCmueIjca3A7SLlH2FeX0bc6W29yTDKnnhlcqDqu5ZF5pzvYvUYmK/PTM3VzUfa4MrnLPzFGWyku0szZvtldpRk1UMIEKGlS9KmLrdVAFVgarLPU7vCxi9qo1JjsbfL5gmb/rV8D1IeqODPFfwpeOf4nzrfNMFaYoOAVaUYtrvWssV5cRQry3vtKYMT9mxkbhdmK/m7mzu8Z/ur7Bhd5ZvMoK35GCgg7ZVpI9E2CFppgnpEbgE7Hr5OxKQSbFrQu8BayAVAoMlkhKXGNBgP6ezLM3xQAAIABJREFUw9rv2Y6FIBMjo1EAEgEgsUKQi1HY6buhJwVoMgG5gqGyOJnAt1AxBhdBTwqMhYI15EJQMwI303RUhBZVPNugKhb5W8UWzcGAt32fw0pQNIaQjK8VLCeUTzlPoLvByfIU55TP06tPY4Wl4TW4e/JuukkXV7pMFiZJdELBLeLrlK3BFjsYqrLAlCoSNg7zJAmPrX6H+bt+CY49+i45ivdjvjzPI/OPcKl9iV7ao5t2OVo7Sskt4SlvJCsC79ZXGjPmx8zYKNwu3Oxmtprzq29wZXCBkuyy7lqsEXQdwUCCtAl9NyUCrI1wSBlKdetjvvcC35fAfiVQKsX3vf5e3NzHATIpcKwlluJWLNO8594AGgO0HYm0kDuS+TQnUQ7KjiqQfJsjJBzPci4oyUk1x8z0CW6kmvPtNudcg7YuDwoXmcWUpUtuNDtJi8Na0WaHp+J1NpUitBGe8GjGTV7cepGpYIq54hxWWKI8gmIDu3eVXjag5pY5KQKkTihPnqBjU/6f4RaLKmKy/SYPOM5f6UL+6OKjZCaj4lZ4ZfsVfOUT5RF31u8EoOgWv19facyYHyPjnMLtwuqLYDXDtTd4frCNSjTbroO2grYS7DqGPTdDuxFFq6nlYIRmw1V/+WczupD/VQzCTXJG+YWMkedwMxH9/Z8xMhEWectYWAFFa1DG0lcOyhoqWiOw+AYOZwYhDBUdM2ELpK03aZvLdAQ0laKtBH+mUlbESINowlj2MOAGrCjDmo2xElzh4koXgSDRCRJJwSkQ5zH3Td2HcYs0S3UqTpFPapeGW4LJ47SV4tJwnZaw7ysV8n7Ml+d5bOkxAjfACovFcv/U/TSCBsD76yuNGfNjYuwpfExZb4fvErn77N4asrvKag/aAuZMznmpyaQlICWw0BIOXamwxpL6glx87137j5d3hqKkHSWTc/F9GYh9zK07GMeM3p1LgdWwoDUVM0o2N4wgVoJEKO5JNRe9Nfp6i0btbm64CjRMqjLSge+IAbXBkFmvSLc0zaA4Ty/appsNUcaMxlkqiRSSJE8Y5ANONE6wHW6jrebh+Yd54PRXOLPyF0Srz0GhDk6Ble5lpM6ZmrkHKeQPHfaZL88zX56/JSfiKhdjDWEW0s/6fHrh0z/aFz9mzA9gbBQ+bnRW2bvwbS6cfxuTGGoGhLKcz9ZpDK+SGZey3+JKMGTTg4GUaEZVPUaApzWZo0YJ3gPmnQZH2b/cCBm41buQC8FsDlXjELoOQZ5wMjVUsFgLu9Iyl1sO9ds8WZSs7jzPtCpyCocdneIIRdfx+XrREBRq3OPUiLMYa3Ic6VKwgoJfoZf20Gi00UR5xPMbzzNdnGatv8Zqf5Wrnas0Cg32avMcSiKKUYemTnHqiyxP3Xdr7X+dsM9Nr+HM7hmaUZPJYJJPL3x6nE8Yc6CMjcLHif28wYX1jJ1Bzj3ZmygpOOfex9Uu/GJ2nUtOlR3p0hGGoVQM5P7MAvZDOkrhWYuzH9L53qTxQeBpQ6bkX+FYiprROAgi6SDwKNlklIjVho6T8aXekDNBiUxYVh1L34OaWyLWA8pZn+nqMsU85GrWoaccvNIUn5cNfCx9JfjVQ4+y13yRy+kenjUEKqCTdkh1ykJhgcAJCPOQS+1LHCofopf0CFSALVSJG8cIrWGiP8VsMHsr5AN//bDPTa9hzJgPigMzCkKIReD/BeYY3eR91Vr7vwohJoCvAcvACvCr1tr2Qa3j48DW9bdZe+Npst4WbnWOw/f9HHNLd75rn83BJmfO/h43ek1e39H8VNSiKUoEjqIa30DQZ5cya26PE5nPZWXQOHjGIsXoLt2zo4axmyGcD8IgAFglb4WSXBTZrVdurkDd2h5KUCh8JD1p6AYOP6UC6q6lKUKebJTYRNM1MUVcqqVpch1xVUnWhYVog0Fpio4osOw1ODpzH5PhcKQHJWDTRPxO/TT/NFtjPdpDW03NqzFXnGOhsoCnPDaHm5TcEolJmHAn2I13OVE/QeAGt0QDn7z+JIN0QNEtjsM+Yz5SHKSnkAP/jbX2VSFEBXhFCPF14LeAv7DW/mMhxD8E/iHw3x7gOj7SbF1/m2vf/gNkUMerL5BHfa59+w+AX79lGG5ehFR3h94gIBNdLhV3UckclVhSS7aZNju0qXLNkQSZh7VdCga0FHjaEMpRKajcLym9Gb8fdQ4cLNk7Hju3jvlukyQYyWjcDCH5VmMdMNJjx/NYq05TToZ0oogX8xZ+rmg4JWakYjITHJIul2SCqxNEnjLIU66ZXY67dVg8Bc1LFOMuTSl5/FO/y//gOLdmUJ/bO8dSeYnntp5DIGjFLSaDSeI8xlc+vbT3rvDQOOwz5qPMgRkFa+0msLn/uC+EOA8cAv4W8PP7u/0+8E3GRuF9WXvjaWRQxy3WAHCLNbL95+dqAay+yJnN71BxAzZ6hma8R1v26ZuQvnuRvxkLpnVKaH1COaTlZGz7GW1XkkmDsZJE7nsGQiCMIdjvFbBCHGii+b1I39M/0VgUBo0LTBiIJAyNxhDxVhRxKBkyUWjwHZmSekXK1hDlMdfCLeaFTy5h3kgiIE17uDYjsD4vbz3LZneWvf4qeR6zFMyyGe4wv/AQMEoOR3nE0xtPIxklncM8ZLe1S6AChtmQicIEG/0N5ivfveiPwz5jPqp8IDkFIcQy8CDwAjC7bzCw1m4KIWbe5z1fAb4CcOTIkQ9imT+RZL0tvPrCre1BnLPVFxRWz/JmvM3C7Dx7ShF0O6TNMyTekKO5xTcpHQXfKcJEbhmoIRd9yY5S7LgSz0JmIVMgEATaoLFkUpLvH+ugPYQfjpFhyNHsyv21WRAaUgW7WZco7pAIQyIVrqNw9kX6uibBS6ElYNotUJQ+A6tYx9DqrtDrbzDj10kEvB1t8t996x/x0NKjtNDkNmd7uE0rbqFQJGaUgk/zFCkk3aRLw2vw6u6r/L3Zv/dhfkFjxvxYOHCjIIQoA/8a+C+ttT3xviWH78Za+1XgqwCf/OQnf7KuTx8gbnWOPOrjFmsM4pyrzQEFEzLtRAxFmWduxOzZPrQv0JRDCnlCzaYkQoCVvB44SCswSLaUBAEVrRlISaTELW2iaH9Gwc0QzaiP+MPk5grevRKzv+2hEBi0kqBhCAyUQQAGTaZhznWo5RqV5wy9Ap6SFIWHUB4Vr46X9gjT0TxopVxcA75wwWqeXXua0Ctyon4CX/nMlGa40b2BQuEoh0bQQFs9kruQgk/MfILNcJMHGM85GPPR5kCNghDCZWQQ/qW19k/2n94WQszvewnzwM5BruGjzuH7fo5r3/4DMhh5CCZEJT16ssiNHc1OP2KRNhtun76XMJdlREKwJxXrriAVEEuPUGmUHV00B0IgBbiMDEDRmFGCWX03XPThGoSbvLtxzgGMAqPVqIP6HQ1tN/GBsh3JKG2TY5wCQWGSU6V53op3CJ0CReWTWY3FEqA4JgKE9FBC4SIJjaWZtJgrz9HLegRuQG5yym4ZJRSTxUkc4eAoh+XqMr20x0J5YdxpPOZjwYF1NIuRS/B7wHlr7f/8jpf+HfCb+49/E/jTg1rDx4G5pTs5+plfR/klTG+TXPq8Wv4s7eIxdNQlznIm4oQvDPpM6pymkjh2JB0xlAqFpWoywGKQxAiyfcE5y6gaIJKSRIhbYaOfPCSgMICzb61iNTIQQsPIhI1eSIB0XylV6dG/+v7yIqcbd/KJ6nGMyenpCIWk7hTxhaImPWKTYqxhI+uym3ZJLOQ6J8ojZoIZUj0KF2mrUUIxzIfMBDMkOqHiVcadxmM+Nhykp/AzwG8Abwghzuw/94+Afwz8kRDit4EbwK8c4Bo+Fswt3cnc0p1snF3nP53bwnbWCcNt7uq/xJxsACPhuuUEXiz6pMay5UoCa9ACpnVO3zgMJKT7/QfZO8J42fse+cPie6Q1xEglFQUuDjn5dyUvvu99Gs0oYS1RIB2O+g0eOPFL7Gx8m5/TGdvktGyCh+GoN0MiDFmesKsjBIYaHqXyFBvhBgulBSwWg2GYD0f5FxXcmoqW6IRDpUPjktMxHxsOsvroGd5DNXmfzx/UcT/OHKoVaK5d4QviBXKvwnM8wB3mGgW5yreLkqrOeSSMuew5NB2PCZ3jWLE/jcyyJ+RPWPL4PVDqu6mEd2D0yCvI1A/yZ/StvwWKohJgNA8c/RtQXSDc9nmeAUnU4bhT4QsTn2CnMslLzdfZ2n2DoUmoqICgMk+1OE3Uv0aSJ1xoXSBwAu6buo/lyjLb8TY1r4bFUvWqzFdGkhTjaqMxHwfGHc0/Aay3Q946/xbuxktMiS4Lh5aYPPkZ1u3kLf2iXpTy5MWLLJg/4ZxaZSka8DkRMp3Dc4GhrC0lY1FYpqOYDUdyyfcwQLg/d+AnI0/wg1Dv+gGMkh4SrAJXg9IG1KhCymoQChwUqdl/AtAocgUZgkB6vLX5EueHl1nprXDH5CkArveu8791XuVwfphacYqgNEVVutS8GpnJ6KQdfnb+Z3mr/RYzwQxTxSmWK8s0ggZH0iO3GtXGjPm4MTYKHzLr7ZCnX3yVu9tPIYt1enaS1somx5p/wLfdn0FNHGEQZ/zxmTdI7NN8RlxhMo8RMuHlsuHzgxitYCoftZwZLGuuw67jYPhujP0jw/d6CfuujWdGSWTPWrxMs+MqcixWC9KbE3usQliwEhwEdRHguD7/59Y3mM3uZbY8S8EpMEyHbA23QIzkJ9YGa7STNhW3Qt2rc3rmNHEeE5mIQ+VDfPbwZ5Hiu+m3sXz1mI8zY6PwIfPKjTbTnTPciFy6A01DbHPErLO7vs28PM+/4Jd4pllAFy9xv+pQJafOkIJJ2ZCCf113GAqB6zrclWZMastrgQfWktjRRLKPBpJbFuGdlaj7BiKzo2xBBvTkTafgZmZhfycB1oy26k4JgaAiXIwQXO9f50ht1O+yE+2Qm5xYx7cG55ScEmEesjZc41D1EI5w2Iv3eGT+EcIsvKV0CmP56jEfb8ZG4UPmtZU2i6vXWYkr2GiLT4s3iOgxIfrMqpxfFNe4VryPVmWdKbNKIeuQkXDGc7nieyhj+UwU83rB45LvMZdrzrsuQ6U+AuGim6j3fHjTMEgxkrnwjSWVAs271VTlO7fVzecEHoKadLClGVrDzdG0NKdAN+mSmITMZBSdIkKM5iW40qXoFLnRu8HR2lEemX+ERxcf5cnrTwKMdYzG3BaMjcIHzHo75Ovntnh9rUuY5uyuXuaudJWH8jZToktD9PDQaCSXhc/18h5f0k/xlpX0vZT/ELj41sE1loIxuMAF38Ug8Ixh01H0pPoJLi99P0bp4fdKMpctpIxkNwQjQbzv7U+46Q+NipQkiUnJhaLrFTH7SqqdpEPN1hhkA5RQGGsInAAlFXvRHtZa7p2/l1jHLNeWeXTx0bGO0ZjbjrFR+ABZb4f8fy9c5+peSL3oMti+xsPRM1zWNe6WfRbEHgExAwqA5A3fo6YjHBKM9LHCoBEMhSB2JJPGMJXmXPEdcgQFa1l1HPKPUg6B/7+9cw+S667u/OfcR7+m5z0jafTySLZsSRHSyJJlY9jED5kYSIEhYi07IUBM2ADGPJZHSLaSJbvUJuxuIKmCVMHGyyOJwTzj8kIKWUGFHcqWJWRLAtsj2xpZGrU0mtG8+zHdfX/7x73d6mn1PDWjaWnOx9XV3b/7u7fP/OT+ffue8/udAxdC4BVcXbYfF8nnIS/+qiK75KDYF5al+ruzLQw2GYTGUD2hkF8TYX3jelbVreLk6ElsbFoiLbRGW8nkM+RMjhqnhrgbJ51L0xRt4k3XvKk48WseI2UxoaJwGShUQdv3Yg9dvaNk8x4HuzLcNHqQXqKMECPpRXmd/Spx8rh4nDc14AyyNJfmxYiw0kuRH4NDkTA5EWqMR94Yel2bFMKQZTNiX2lqUKHUZ4WmLBaWTXCXALlgRVJp5TZHHMQIITvEivgK0vk0Y94YESfC65e9npaaFqJulA9u/SA/Of4TEiMJuoa66OzvpMVtoSnaRMyJ0V7fPk4QFGWxoaIwD5SWwrRFODecZlVzDb3DaY6dHWZ0zMMDWuxBeoxfiKWfOl7wVnKrNUgTQzTKEIm8S9oyDFth6j0/RcXSfJ60CK4xdIZconmPQccmPc2cUlWDHcz+FdxF5Xh4CBZekPnIsfyd2AVXkW05RJ0ocSfO2vq1NMWaCFkhwk6YrUu2+tcwXnHFUEdrBz3JHjpaO9jYtJGX+l+iN9XLltYtRZeRoixWVBTmmO7+JD8+ksDzDImhNIdPDuABN+XyvNwzyvDYBW94r6mnhhQjxGhkiFpJIxhcstgYtqXz7IlHCRlDSsRP9mBg1LaoyXvEPMPAFSMIfiHNiwRgCkGwsJDgv7A45MxYUQwccagJ1VIbriXuxlkWW8aSGj8lxeDYIDsadxSvU7piqDROkMwl/VrLuvlMUQAVhUum9K7AEjjaPUR/Msu54TSubdE7mkGM8N0D3Qynx4d/T3ot/IGzh2b6uVbOUMsoTpCVyALacnnuGkmxLxbhmWiU5nyO1nyOqOeRcB1GLPGzoVY99pST/0S4lostNnmTJ+pESeUEz3gYDDnj0RRtoqO1o5iCoi5UR2Okkd5U76QF7zVOoCiVUVG4BAp3BXVRF8cSnj5+nuPnRgjbFgPJMSzLLzM5nMkxlM6NWzHTRi/b7WMMehG22T3ESRIli4U3Ltzalstz39AotyXTPB8J8VhtDWlgyLJIWldA2orZqkGAIw62ZSOev2wUIGyHEXxxyOfzZLwMtth8ZOtHihN9YiShK4YUZRaoKFwCB1/rpy7qUhtxOdh1npN9o5zqT5Ecy1MTdmiIuZwfTjNSIePcNquTIRPjVquXc6aOOhnFxpswWVRbLk9PNk+/ZXHGsRmyLk4bXX3MXhAEwRWX5fHlXFN3DYd6DhVFwRYb13YxxhBzY2xbso2oGx036eudgKLMDhWFS6B3OMOSugjnRzIcPNHPuZEM2XyevIGhtH93MBFb5GWulW5ulE5s8kTJVRSEhGOzLxbh2UiYo5EwWWDYsqp0Y1p5YRwDyKTa4ARZT0sRBAuL9Y3ruXXlrbxz3Tv56M8+im1sUvkUfek+MLAksgTbsnUzmaLMISoKl0BLbZjRTI7nTw1wejBFKjs9Z84WOtlhvYBn/EpiNWQmFITv18U54dqMWhYZgVTV1j0I4gZ5m8K6oOncKHgl9ztWEDwO22HWNaxjbeNa1jaspS3exu2rbufJ7ieptWtpijSR9bKk8ina63QJqaLMJSoKs6AQXD7U1c+B1/o5dmaI9CQ/3dvoZZvVyVrppoFRtlgvkzM2jTLCIFHqSRb7Jhyb5yMh+mybU65NwnZIWcJpx/FXGVVVYNm+cFMAJctLp2ejK34gOGpFWVazjJpQDbWhWjD+EtK+dB8drX55y3uuu4ecl+N8+jxj+TFCdoimSBO7rt+lgqAoc4iKwgwpBJf7RzL84pVzvHIuSW6SG4Q2etlt72UlPVxrnSFvhLV0MyBxaslgShxBCcdmTzxK3kCPY7M/EqHHtlmZzeEaQ06kitxGJXcCwWpT/6UABhuLXIWoR2F5aWu0lfb6dk6PnCbn5WiONmOJhWM5pLIpxswYd7TdMW5X8a7rd/HcuefoS/XRHG3WZaSKMg+oKEyT7v4kT/z6LI8fSXC6f4Qzg9kpJ+gtdPIZ559ZJ6fxowEetniEyBMlExSZ9Ek4Nt9siNNtO5xxHUYEemyHvMBLYReMqaLAcplryCp9EjwMtuViG8ia7DgXkQR3Ea7lMjg2SMyJ0Z/ux8Mj5+W4oe4GBjOD5E2e21fdPu5TNXisKPOPisIUFBLYPfFCDyOZHCd7h+lLTT0938l+PuF+l3Z6cIL9t4VsngK4JX0LdwjdtsNx1+WMY+PJBa+MgSpxG5UUwQliyKVErAiIRdZkcS1/dVBhxVDhDgH8ZaZD2SHCTpiYGyPmxmgKN5HMJzmXPIdjO+y+YbcKgKIsACoKjN+A1lIbZtvqRlY0xjjY1ceXnujkyKlBktk8nseUrqJtViebpZNd1pNEyeCWLTOtVN1gXyzCU+EIh2Nh0nP9x80JFVJbC9jBNO9gUx9uwHHC5LwcI2Mj5EyuvIAyDg6eeNhik8lmyIVy5KwcN7fdzEh2hOsar2Ntw1p1CynKArLoRaF0A9qSugijmRw/PpJgVWOEzz/+AqcGM9PaINZGL2+xn2GFOcM7rH+nbprTe8KxeTxeQ2fYpcJ2hiqg8ioiC4vacJ2fhM4Jsal5ExubNnLo3CG6R7oZzAwyPDaMHUiHZVmE7BARK0Iyl8S1XFqiLWxo2sDmpZtVCBSlSlj0ovDEr8/yau8og8kso2M5YiGb184N89K5pJ+Ncxq00csf2Y+zkeNssl8lOoNw8L5YhFOuXX2CYE+8nlQQrolfQ8SNkDM5WiOtbG7ZzPLa5Ti2g91jE7NjnE+fZzAzyOnkaeJuHM/zSHtpGsINfGr7p9i5Zudl/IMURZkOi1oUuvuTPPVyL4LhxbPDJDMeg6NjM5qg2+jlPnsvt8hR1srpcbGC6fBULMqgVWUlMycRBBeXjtYO7mi/o7gCCCimlGiLt/H+Je/n0WOPUhuqJWuy/EbkNzifPs9Qdogau4ZP3vhJFQRFqVIWtSgcfK2fVCbLL08Nkhyb3dqeO62DdEgnq+XcjAVhbyzCgfBMz7oU7IvfTiN1NUBI/DoFtyy/hfdtet9Frp7y94lkglQ2RTafpWu4iyVjS3Atl3WN61QQFKWKmbefqCLysIj0iMjRkrYmEdkjIseC58b5+vzp8Muu87zcm5y1IAC8Xn7FajlLaIYOoIRj862GWvIy33cJdvDLv8LMP01BcHFpr29n94bdFQWhEh2tHQxnh3Ftly2tW7hxyY3FEpeKolQv8zkjfR24u6ztT4C9xph1wN7g/WWnuz/Jf3vsKI88e5IzQ5lLutYyq49GRmec+u35SMhPajefK02lJEhc+lz6mOx0hJCEWNOwht+9/ne5f8P90w4GF2oWRN0ovaleom5U01EoyhXAvLmPjDE/F5H2sua3A7cFr78B7AM+M182VOJgVx9/9sPDvHg2OXXnKdhCJ+t4jZoZBJYLCe5+UFfDKefiAvRzh32x5E9TuWxsbMvGEf9/j/badjwzc0t1s5miXHlc7pjCUmNMAsAYkxCRJRN1FJEPAB8AWL169Zx8+L8e6ebT3zvMUGb2U3HpXoS7rf3TFoSEY/NYvIafxiMM2zYZLlrGP4dUqHA2rbMsXHERS7DFJuJEWB5bjmVZxapliqJc3VRtoNkY81XgqwDbt2+f9fxZTF53op9Hnz3B6CWs/SzsRbCMx5usZ1nOwLTOK2Q7fToSYtCyGLCsOc50WmFz2QwQhDW1axjODhNxIoxmRwFoq2ljaWwpju0UVxkpinJ1c7lF4ayItAV3CW1Az3x+WHd/kkeeOcHJ/hR7fp0geYmbAe60DvBGnudG+yXi0w0sW378oN8Rhi2Lftuew6R2sy9zWUAQlkWW0VrTynXudfRmeomORTEYmiPNhJ0w915/r7qBFGWRcLlF4THgPcBfBc//Mp8ftufXZ/jFK30cOTl4yZvD2ujlrfYzbKBrRoKAQJ9rMyg2CceZ2yynlygIcSdOjVuD4zgMpAe4c/WdpLIpjg0eY1V8FWsa1uhOY0VZZMybKIjII/hB5RYROQX8Bb4YPCoiDwCvAe+ar88/2NXHF3/6EoOXED8oZZvVSQsDRJjGaqWyydryDCcdm7E5XWl0aYqwLLqMqBsl7sbJeTnWNa4jb/K01bbx5rVvViFQlEXKfK4+um+CQ3fO12cWONjVx1/86OicCQJAiwwSZuzCgBXm5ArZQssxAkOT7BKePkHhguBShUpllmVR79bTn+nHYMibPA4OYTeMhUUym8TDw2BwcYmFYyyrWcaahjXkvTweHg9ufVCFQFGU6g00XwqPHU5wdnQsKPcyeyzAEQi5FsOmgZyEuGiv2ZS//m0kvpyUnYEpnUfTixHUWhHCbhzXdhnLj1EfrmdD8wZ2rtpJT7qH/Wf205/qpynaxE3LbmJj00YSyQTHB45zcuQkS6NLSefT9KX7sMXWNNWKohS5KkUhMZAim/NmJQr1YYtbr2th3dJammpC9A6Psao5RnOuFfuZ/TBwdlz/0jm8fMr3nDiuG6E51kLWS+AC2fJO0xCBuISIiE2rW0ck2sy1rRsZTA8yNDaEJRa3rb6N21fdXpzY799w/0XX6MBfPZQYSRSrl21ZskVjBoqijOOqFIW2hiiuY+FYMN0MFi0xl/VttXz8ruvZ1n5hTX5hSetrmSbc1z3EyiffzURbzsbN71YMu2El7PhjOpIJnBM/IMMYjm1mtBxVEF6/8rfY1LIJ27bZ0rKFRDIx65KUuqFMUZTJuCpF4W2b2zj4ah+D03AhhW1Y01LD7h2ruWvjMlY0xsYdX9EYK2lbC9074dWfTm5AbClsugc27YLVO2gbOMmO84fYN/wiBsFieqU16+163nbd21het3ycABR+9SuKosw1V6UobGtv5nP3bOJPf3iYrr4UubzBFjAGsoFC1IVtNq+op2N1A/fdfM1FYjAhW3eDE4Jje8CUrUSqXwvX74Q3PAQNqy60N6zirq1/SPjEz3n67NOkcimMZ8h5ObLB8ta4E2dN7RrEFkazo2xs3si7rn8XHUtVABRFuXxclaIAvjA8/N6b+fGRBJ5nSAyl6RvOkM55bFpRR23EHVd6c9qs2gFDp2H1DhhKwMBxSA3A8q2w8ib/eKkgBHS0dtCT7GFd8zp6kj3jgrxLa5YW/fyzcQkpiqLMFWLM/GXgmSu2b99uDhw4MKtzJ6q/fEkMnIST+2G0B2qWTCgE5ZQGeXXyVxRlvhGRg8aY7TM556q9UygwPiYwRzSsmpYIlKNBXkVRqp0qqwOpKIqiLCQqCoqiKEoRFQVFURT+ncUXAAAIpElEQVSliIqCoiiKUkRFQVEURSmioqAoiqIUUVFQFEVRiqgoKIqiKEWuiB3NInIOODEPl24BeufhunOB2jY71LbZU832qW2z4wZjTO1MTrgidjQbY1rn47oicmCmW8AvF2rb7FDbZk8126e2zQ4RmXF+IHUfKYqiKEVUFBRFUZQii10UvrrQBkyC2jY71LbZU832qW2zY8a2XRGBZkVRFOXysNjvFBRFUZQSVBQURVGUIotGFETkYRHpEZGjJW1NIrJHRI4Fz41VZNt/FZFuEXkueLxlgWxbJSI/E5EXRORXIvLRoH3Bx24S2xZ87EQkIiL7ReT5wLbPBe1rROSZYNy+IyKhKrLt6yJyvGTcFqxAuIjYInJIRB4P3i/4uE1iW1WMm4h0iciRwIYDQduMv6eLRhSArwN3l7X9CbDXGLMO2Bu8Xwi+zsW2AXzRGNMRPH58mW0qkAP+szFmA3AL8GER2Uh1jN1EtsHCj10GuMMYswXoAO4WkVuAvw5sWwf0Aw9UkW0AnyoZt+cWwLYCHwVeKHlfDeNWoNw2qJ5xuz2wobBvYsbf00UjCsaYnwPny5rfDnwjeP0N4J7LalTABLZVBcaYhDHml8HrYfwvwwqqYOwmsW3BMT4jwVs3eBjgDuB7QftCjdtEtlUFIrISeCvwf4L3QhWMWyXbrgBm/D1dNKIwAUuNMQnwJxhgyQLbU86DInI4cC8tiGurFBFpB7YCz1BlY1dmG1TB2AVuhueAHmAP8AowYIzJBV1OsUAiVm6bMaYwbp8Pxu2LIhJeCNuALwGfBrzgfTNVMm5cbFuBahg3A/xURA6KyAeCthl/Txe7KFQzfw9ci397nwD+90IaIyJx4PvAx4wxQwtpSzkVbKuKsTPG5I0xHcBKYAewoVK3y2tV8KFltonIJuCzwHrgJqAJ+MzltktEfgfoMcYcLG2u0PWyj9sEtkEVjFvAG4wxNwJvxnel/uZsLrLYReGsiLQBBM89C2xPEWPM2eCL6wFfw59UFgQRcfEn3X8yxvwgaK6KsatkWzWNXWDPALAPP+7RICKFnGMrgdMLZReMs+3uwB1njDEZ4P+yMOP2BuBtItIFfBvfbfQlqmPcLrJNRP6xSsYNY8zp4LkH+GFgx4y/p4tdFB4D3hO8fg/wLwtoyzgK/5AB7wCOTtR3nu0Q4B+AF4wxf1NyaMHHbiLbqmHsRKRVRBqC11FgJ37M42fArqDbQo1bJdteLJk8BN/3fNnHzRjzWWPMSmNMO7Ab+DdjzO9RBeM2gW2/Xw3jJiI1IlJbeA28KbBj5t9TY8yieACP4LsSsvg+yQfwfZV7gWPBc1MV2fYt4AhwOPiHbVsg296If6t+GHgueLylGsZuEtsWfOyAzcChwIajwJ8H7WuB/cDLwHeBcBXZ9m/BuB0F/hGIL8T/cyV23gY8Xi3jNoltCz5uwfg8Hzx+BfxZ0D7j76mmuVAURVGKLHb3kaIoilKCioKiKIpSREVBURRFKaKioCiKohRRUVAURVGKqCgoVYGI7BOR3y5r+5iIfEVE2iXIICsi7xSRvSV93hhkhXTKr1nS57iI3FDW9iUR+bSI3FaS7fITIvIPJX1+T0T+X/D6j0syUD5VSLwXnG9E5IGS87YGbZ+cxKZ2ETklIlZZ+3MiskP8TK+fDNoeE5F3l/T5moh8KngtIvJ5EekUP1vsQ0H7ewMb7iw57x1B2y4UZQJUFJRq4RH8DUGl7A7aixh/13JaRO4PhOArwIfMhbw4lfh26bWDiXgX8J2yfn8HbBORNwSbu/478JHg2D8bY15n/NQQXwBKN/IdAe4ts/v5SezBGNMFnAT+Q4ld64FaY8z+su4PAX8pIg0icitwM/4uX4D3AquA9cbPFvvtMrvum4ldiqKioFQL3wN+p5BMLEhwtxx4qkLfj+BP2J8DnjXG/GKKa5cLzm8CXcaYE6WdAmH5EPBl/In/YWPMq8Gx0nxPNYzPvfMaEBGRpcGu1ruBn0xhUyW7LhLB4LO78GvtfgFfBB80xmSDwx8E/tL4KT0wfoqDAk/i5zVyg/xQ1+Fv8FOUCVFRUKoCY0wf/o7VQl2J3cB3TIXdlcFE/R3gQaaRfMwYcxjwRGRLybUvmnyDvr/AT0exE38SLiIiHxaRV4L2h8pO/R7wLuBW4Jf4NQum4lHgnhLX172M/6Vfyv/CH5tfGT/VeoFrgXtF5ICI/ERE1pX+OcATwG/jp1B+bBo2KYscFQWlmij95TzhxB24f3YCI8A1M7l2MAG/HT9VQqVrx4Ht+DUGWkuPGWO+bIy5Fl+I/kvZqY/ii8J9E9ldjjHmDH5KgjvFr9aVNcZMlDdnM3620PVlcYgwkDZ+UZWvAQ+XnVdwnU04nopSioqCUk38CH+CvBGImqCATgU+jJ9n5gHgy4HLZioeAf4jvpgcLnOzlPI5/Pw1nwe+OEGfb1NWrCSY4LPAXfg5ZqZLQQinEsGvAO/Gz2HzwZLDp/CzxIKfGXNzmV37gU1AizGmcwZ2KYsUFQWlajB+NbB9+L92J5oglwGfAD5tjPlXoBt4f3Bsh4h8c4JrvwL0AX81ybVfh19V66/xffjXiMhdwbFSt8xb8Sfncv4c+IwxJl923QdF5MFKn4k/ob+FyV1H/wk4ZozZR/C3i0jhLuZH+OmlAX4LqDTxfxb40wmurSjjmHAZn6IsEI8AP+DilUgF/gb4gjHmXPD+Y8CTIvJ9YDWQmuLa/wP/F/U4gruNvwc+boxJB20fAr4ZuHYeFJGd+HcD/VxIR1xkkoD3euDfKx0wxgyIyNP4FbKOV7BrCb676pag/2kR+Vv8uMb78EXun0Tk4/jutPdX+IzpBL0VBUCzpCpXDyLyP4FvBYHlqiHYB/FOY8zYQtuiKFOhoqAoiqIU0ZiCoiiKUkRFQVEURSmioqAoiqIUUVFQFEVRiqgoKIqiKEVUFBRFUZQi/x82bRHm/u2JLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# impute NAs for VIX9D using basic linear regression\n",
    "# plot relationships between fast and med/slow crosses\n",
    "plt.scatter(x = variables['VIX'], y = variables['VIX9D'], alpha = .33)\n",
    "plt.scatter(x = variables['VIX3M'], y = variables['VIX9D'], alpha = .33)\n",
    "plt.scatter(x = variables['VIX6M'], y = variables['VIX9D'], alpha = .33)\n",
    "plt.title('Regress VIX, VIX3M, VIX6M vs. VIX9D')\n",
    "plt.xlabel('VIX, VIX3M, VIX6M')\n",
    "plt.ylabel('VIX9D')\n",
    "plt.legend(['VIX', 'VIX3M', 'VIX6M'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "import statsmodels as sm\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from statsmodels.tools.eval_measures import mse, rmse\n",
    "from sklearn.linear_model import LinearRegression, LassoCV, RidgeCV, ElasticNetCV\n",
    "\n",
    "tmp = variables.loc[:,['VIX9D', 'VIX', 'VIX3M', 'VIX6M']]\n",
    "tmp.head()\n",
    "X_train = tmp.loc[:,['VIX9D', 'VIX', 'VIX3M', 'VIX6M']].dropna()\n",
    "y_train = tmp.loc[:,'VIX9D'].dropna()\n",
    "X_test = tmp.loc[tmp.VIX9D.isnull(),['VIX', 'VIX3M', 'VIX6M']]\n",
    "\n",
    "del X_train['VIX9D']\n",
    "# as we can see, there is high correlation between VIX9D vs. VIX/VIX3M/VIX6M. \n",
    "# We can run a linear regression model to impute these NA values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "To impute VIX9D, we'll use an elastic net CV linear regression on VIX, VIX3M, and VIX6M."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "alphas = [np.power(10.0,p) for p in np.arange(-10,40,1)]\n",
    "def print_train_stats(X_train, y_train, model):\n",
    "  model.fit(X_train, y_train)\n",
    "  y_preds_train = model.predict(X_train)\n",
    "\n",
    "  print(\"R-squared of the model in training set is: {}\".format(model.score(X_train, y_train)))\n",
    "  print(\"Mean absolute error of the prediction in training is: {}\".format(mean_absolute_error(y_train, y_preds_train)))\n",
    "  print(\"Mean squared error of the prediction in training is: {}\".format(mse(y_train, y_preds_train)))\n",
    "  print(\"Root mean squared error of the prediction in training is: {}\".format(rmse(y_train, y_preds_train)))\n",
    "  print(\"Mean absolute percentage error of the prediction in training is: {}\".format(np.mean(np.abs((y_train - y_preds_train) / y_train)) * 100))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha value is: 0.01\n",
      "R-squared of the model in training set is: 0.964917525850025\n",
      "Mean absolute error of the prediction in training is: 0.8466689538347143\n",
      "Mean squared error of the prediction in training is: 1.4231719580657474\n",
      "Root mean squared error of the prediction in training is: 1.1929677104036585\n",
      "Mean absolute percentage error of the prediction in training is: 5.535521842613312\n"
     ]
    }
   ],
   "source": [
    "elastic_cv = ElasticNetCV(alphas = alphas, cv = 5, max_iter = 10000)\n",
    "elastic_cv.fit(X_train, y_train)\n",
    "\n",
    "print(\"Best alpha value is: {}\".format(elastic_cv.alpha_))\n",
    "print_train_stats(X_train, y_train, elastic_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Impute VIX9D, C7, C8 contangos\n",
    "\n",
    "We use elastic net for imputing VIX9D from regressing it on VIX, VIX3M, and VIX6M  \n",
    "We then create a fastcross variable that is the ratio of VIX9D divided by VIX.  \n",
    "Lastly, we impute missing values for C7 and C8 by multiplying the last contract before them by the contango relationship between the two contracts previous to them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# use elastic \n",
    "\n",
    "variables.VIX9D[0:754] = elastic_cv.predict(X_test)\n",
    "variables['fastcross'] = variables['VIX9D']/variables['VIX']\n",
    "# impute missing C7s with C6 multiplied by C6/C5 contango\n",
    "\n",
    "variables['C7'][variables.C7.isnull()] = variables['C6'][variables.C7.isnull()] * variables['C6'][variables.C7.isnull()]/ variables['C5'][variables.C7.isnull()]\n",
    "\n",
    "# repeat for C8\n",
    "variables['C8'][variables.C8.isnull()] = variables['C7'][variables.C8.isnull()] * variables['C7'][variables.C8.isnull()]/ variables['C6'][variables.C8.isnull()]\n",
    "variables['m4m7_contango'] = variables['C7']/variables['C4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>VIX9D</th>\n",
       "      <th>VIX</th>\n",
       "      <th>VIX3M</th>\n",
       "      <th>VIX6M</th>\n",
       "      <th>C1</th>\n",
       "      <th>C2</th>\n",
       "      <th>C3</th>\n",
       "      <th>C4</th>\n",
       "      <th>C5</th>\n",
       "      <th>C6</th>\n",
       "      <th>...</th>\n",
       "      <th>vxxCrush</th>\n",
       "      <th>m1_roll_yield</th>\n",
       "      <th>VX30_roll_yield</th>\n",
       "      <th>contango</th>\n",
       "      <th>adj_contango</th>\n",
       "      <th>m4m7_contango</th>\n",
       "      <th>fastcross</th>\n",
       "      <th>medcross</th>\n",
       "      <th>slowcross</th>\n",
       "      <th>stratcross</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-07</th>\n",
       "      <td>25.607299</td>\n",
       "      <td>23.79</td>\n",
       "      <td>23.22</td>\n",
       "      <td>24.22</td>\n",
       "      <td>24.12</td>\n",
       "      <td>24.81</td>\n",
       "      <td>24.54</td>\n",
       "      <td>24.45</td>\n",
       "      <td>24.37</td>\n",
       "      <td>24.28</td>\n",
       "      <td>...</td>\n",
       "      <td>0.171019</td>\n",
       "      <td>0.013871</td>\n",
       "      <td>0.031596</td>\n",
       "      <td>0.028607</td>\n",
       "      <td>0.017657</td>\n",
       "      <td>0.986912</td>\n",
       "      <td>1.076389</td>\n",
       "      <td>1.024548</td>\n",
       "      <td>0.982246</td>\n",
       "      <td>0.958712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-08</th>\n",
       "      <td>26.572768</td>\n",
       "      <td>25.43</td>\n",
       "      <td>25.49</td>\n",
       "      <td>25.18</td>\n",
       "      <td>25.54</td>\n",
       "      <td>25.90</td>\n",
       "      <td>25.52</td>\n",
       "      <td>25.35</td>\n",
       "      <td>25.17</td>\n",
       "      <td>25.07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074774</td>\n",
       "      <td>0.004326</td>\n",
       "      <td>0.013763</td>\n",
       "      <td>0.014096</td>\n",
       "      <td>0.012844</td>\n",
       "      <td>0.980671</td>\n",
       "      <td>1.044938</td>\n",
       "      <td>0.997646</td>\n",
       "      <td>1.009929</td>\n",
       "      <td>1.012311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-09</th>\n",
       "      <td>24.972582</td>\n",
       "      <td>24.12</td>\n",
       "      <td>24.50</td>\n",
       "      <td>24.48</td>\n",
       "      <td>24.89</td>\n",
       "      <td>25.78</td>\n",
       "      <td>25.28</td>\n",
       "      <td>24.76</td>\n",
       "      <td>24.66</td>\n",
       "      <td>24.49</td>\n",
       "      <td>...</td>\n",
       "      <td>0.318373</td>\n",
       "      <td>0.031924</td>\n",
       "      <td>0.058573</td>\n",
       "      <td>0.035757</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.985057</td>\n",
       "      <td>1.035347</td>\n",
       "      <td>0.984490</td>\n",
       "      <td>0.985294</td>\n",
       "      <td>1.000817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-10</th>\n",
       "      <td>24.272276</td>\n",
       "      <td>23.45</td>\n",
       "      <td>23.95</td>\n",
       "      <td>24.57</td>\n",
       "      <td>24.07</td>\n",
       "      <td>24.95</td>\n",
       "      <td>24.51</td>\n",
       "      <td>24.36</td>\n",
       "      <td>24.27</td>\n",
       "      <td>24.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.301873</td>\n",
       "      <td>0.026439</td>\n",
       "      <td>0.055627</td>\n",
       "      <td>0.036560</td>\n",
       "      <td>0.015800</td>\n",
       "      <td>0.980296</td>\n",
       "      <td>1.035065</td>\n",
       "      <td>0.979123</td>\n",
       "      <td>0.954416</td>\n",
       "      <td>0.974766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-11</th>\n",
       "      <td>24.213251</td>\n",
       "      <td>23.68</td>\n",
       "      <td>24.50</td>\n",
       "      <td>24.96</td>\n",
       "      <td>24.96</td>\n",
       "      <td>26.15</td>\n",
       "      <td>26.05</td>\n",
       "      <td>25.92</td>\n",
       "      <td>25.50</td>\n",
       "      <td>25.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.522062</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.095932</td>\n",
       "      <td>0.047676</td>\n",
       "      <td>0.009141</td>\n",
       "      <td>0.974151</td>\n",
       "      <td>1.022519</td>\n",
       "      <td>0.966531</td>\n",
       "      <td>0.948718</td>\n",
       "      <td>0.981571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                VIX9D    VIX  VIX3M  VIX6M     C1     C2     C3     C4     C5  \\\n",
       "Index                                                                           \n",
       "2008-01-07  25.607299  23.79  23.22  24.22  24.12  24.81  24.54  24.45  24.37   \n",
       "2008-01-08  26.572768  25.43  25.49  25.18  25.54  25.90  25.52  25.35  25.17   \n",
       "2008-01-09  24.972582  24.12  24.50  24.48  24.89  25.78  25.28  24.76  24.66   \n",
       "2008-01-10  24.272276  23.45  23.95  24.57  24.07  24.95  24.51  24.36  24.27   \n",
       "2008-01-11  24.213251  23.68  24.50  24.96  24.96  26.15  26.05  25.92  25.50   \n",
       "\n",
       "               C6  ...  vxxCrush  m1_roll_yield  VX30_roll_yield  contango  \\\n",
       "Index              ...                                                       \n",
       "2008-01-07  24.28  ...  0.171019       0.013871         0.031596  0.028607   \n",
       "2008-01-08  25.07  ...  0.074774       0.004326         0.013763  0.014096   \n",
       "2008-01-09  24.49  ...  0.318373       0.031924         0.058573  0.035757   \n",
       "2008-01-10  24.15  ...  0.301873       0.026439         0.055627  0.036560   \n",
       "2008-01-11  25.50  ...  0.522062       0.054054         0.095932  0.047676   \n",
       "\n",
       "            adj_contango  m4m7_contango  fastcross  medcross  slowcross  \\\n",
       "Index                                                                     \n",
       "2008-01-07      0.017657       0.986912   1.076389  1.024548   0.982246   \n",
       "2008-01-08      0.012844       0.980671   1.044938  0.997646   1.009929   \n",
       "2008-01-09      0.019474       0.985057   1.035347  0.984490   0.985294   \n",
       "2008-01-10      0.015800       0.980296   1.035065  0.979123   0.954416   \n",
       "2008-01-11      0.009141       0.974151   1.022519  0.966531   0.948718   \n",
       "\n",
       "            stratcross  \n",
       "Index                   \n",
       "2008-01-07    0.958712  \n",
       "2008-01-08    1.012311  \n",
       "2008-01-09    1.000817  \n",
       "2008-01-10    0.974766  \n",
       "2008-01-11    0.981571  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variables.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# New, Unsupervised Material\n",
    "\n",
    "Use Kmeans and hierarchical clustering to try and find 3 distinct clusters. \n",
    "We know that there's a bull market, a bear market, and...whatever's in between.\n",
    "\n",
    "The challenge is to find the stuff that's in between. Why? Because it's easy to make money trend following when everything's going up...or down.\n",
    "\n",
    "As the distributions are definitely not normal (not by a long shot), GMMs absolutely do not apply.\n",
    "Furthermore, as TSNE is just an inferior UMAP, we won't use that.\n",
    "Also, we know that we are looking for 3 clusters, so DBScan does not apply."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans, MiniBatchKMeans\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>VIX9D</th>\n",
       "      <th>VIX</th>\n",
       "      <th>VIX3M</th>\n",
       "      <th>VIX6M</th>\n",
       "      <th>C1</th>\n",
       "      <th>C2</th>\n",
       "      <th>C3</th>\n",
       "      <th>C4</th>\n",
       "      <th>C5</th>\n",
       "      <th>C6</th>\n",
       "      <th>...</th>\n",
       "      <th>vxxCrush</th>\n",
       "      <th>m1_roll_yield</th>\n",
       "      <th>VX30_roll_yield</th>\n",
       "      <th>contango</th>\n",
       "      <th>adj_contango</th>\n",
       "      <th>m4m7_contango</th>\n",
       "      <th>fastcross</th>\n",
       "      <th>medcross</th>\n",
       "      <th>slowcross</th>\n",
       "      <th>stratcross</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-15</th>\n",
       "      <td>-0.865168</td>\n",
       "      <td>-0.741185</td>\n",
       "      <td>-0.773535</td>\n",
       "      <td>-0.854960</td>\n",
       "      <td>-0.820306</td>\n",
       "      <td>-0.731267</td>\n",
       "      <td>-0.813094</td>\n",
       "      <td>-0.831955</td>\n",
       "      <td>-0.883363</td>\n",
       "      <td>-0.898249</td>\n",
       "      <td>...</td>\n",
       "      <td>1.081425</td>\n",
       "      <td>-0.073262</td>\n",
       "      <td>1.079228</td>\n",
       "      <td>1.579856</td>\n",
       "      <td>0.691593</td>\n",
       "      <td>0.175329</td>\n",
       "      <td>-2.061873</td>\n",
       "      <td>-0.686448</td>\n",
       "      <td>-0.558709</td>\n",
       "      <td>-0.301367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-16</th>\n",
       "      <td>-0.863355</td>\n",
       "      <td>-0.751852</td>\n",
       "      <td>-0.795847</td>\n",
       "      <td>-0.882866</td>\n",
       "      <td>-0.861526</td>\n",
       "      <td>-0.764329</td>\n",
       "      <td>-0.841836</td>\n",
       "      <td>-0.862717</td>\n",
       "      <td>-0.915666</td>\n",
       "      <td>-0.931930</td>\n",
       "      <td>...</td>\n",
       "      <td>0.978097</td>\n",
       "      <td>-0.345272</td>\n",
       "      <td>1.044683</td>\n",
       "      <td>1.755381</td>\n",
       "      <td>0.516132</td>\n",
       "      <td>0.245894</td>\n",
       "      <td>-1.974771</td>\n",
       "      <td>-0.643606</td>\n",
       "      <td>-0.531692</td>\n",
       "      <td>-0.297797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-17</th>\n",
       "      <td>-0.924088</td>\n",
       "      <td>-0.775321</td>\n",
       "      <td>-0.800805</td>\n",
       "      <td>-0.885657</td>\n",
       "      <td>-0.867415</td>\n",
       "      <td>-0.764329</td>\n",
       "      <td>-0.841836</td>\n",
       "      <td>-0.855027</td>\n",
       "      <td>-0.899515</td>\n",
       "      <td>-0.915089</td>\n",
       "      <td>...</td>\n",
       "      <td>1.313252</td>\n",
       "      <td>-0.148701</td>\n",
       "      <td>1.345339</td>\n",
       "      <td>1.820366</td>\n",
       "      <td>0.294819</td>\n",
       "      <td>0.242328</td>\n",
       "      <td>-2.424087</td>\n",
       "      <td>-0.794834</td>\n",
       "      <td>-0.631411</td>\n",
       "      <td>-0.322770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-21</th>\n",
       "      <td>-0.711976</td>\n",
       "      <td>-0.695315</td>\n",
       "      <td>-0.743786</td>\n",
       "      <td>-0.860541</td>\n",
       "      <td>-0.820306</td>\n",
       "      <td>-0.744492</td>\n",
       "      <td>-0.820280</td>\n",
       "      <td>-0.855027</td>\n",
       "      <td>-0.911629</td>\n",
       "      <td>-0.923509</td>\n",
       "      <td>...</td>\n",
       "      <td>0.811113</td>\n",
       "      <td>-0.547780</td>\n",
       "      <td>0.845411</td>\n",
       "      <td>1.473739</td>\n",
       "      <td>0.024955</td>\n",
       "      <td>0.213847</td>\n",
       "      <td>-0.907874</td>\n",
       "      <td>-0.508083</td>\n",
       "      <td>-0.336285</td>\n",
       "      <td>0.013589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>-0.743702</td>\n",
       "      <td>-0.688914</td>\n",
       "      <td>-0.730151</td>\n",
       "      <td>-0.836821</td>\n",
       "      <td>-0.856227</td>\n",
       "      <td>-0.731267</td>\n",
       "      <td>-0.791538</td>\n",
       "      <td>-0.816574</td>\n",
       "      <td>-0.867212</td>\n",
       "      <td>-0.889828</td>\n",
       "      <td>...</td>\n",
       "      <td>1.214982</td>\n",
       "      <td>-0.933401</td>\n",
       "      <td>0.944598</td>\n",
       "      <td>1.969039</td>\n",
       "      <td>-0.333445</td>\n",
       "      <td>0.056568</td>\n",
       "      <td>-1.249735</td>\n",
       "      <td>-0.535517</td>\n",
       "      <td>-0.373059</td>\n",
       "      <td>-0.041149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               VIX9D       VIX     VIX3M     VIX6M        C1        C2  \\\n",
       "Index                                                                    \n",
       "2020-01-15 -0.865168 -0.741185 -0.773535 -0.854960 -0.820306 -0.731267   \n",
       "2020-01-16 -0.863355 -0.751852 -0.795847 -0.882866 -0.861526 -0.764329   \n",
       "2020-01-17 -0.924088 -0.775321 -0.800805 -0.885657 -0.867415 -0.764329   \n",
       "2020-01-21 -0.711976 -0.695315 -0.743786 -0.860541 -0.820306 -0.744492   \n",
       "2020-01-22 -0.743702 -0.688914 -0.730151 -0.836821 -0.856227 -0.731267   \n",
       "\n",
       "                  C3        C4        C5        C6  ...  vxxCrush  \\\n",
       "Index                                               ...             \n",
       "2020-01-15 -0.813094 -0.831955 -0.883363 -0.898249  ...  1.081425   \n",
       "2020-01-16 -0.841836 -0.862717 -0.915666 -0.931930  ...  0.978097   \n",
       "2020-01-17 -0.841836 -0.855027 -0.899515 -0.915089  ...  1.313252   \n",
       "2020-01-21 -0.820280 -0.855027 -0.911629 -0.923509  ...  0.811113   \n",
       "2020-01-22 -0.791538 -0.816574 -0.867212 -0.889828  ...  1.214982   \n",
       "\n",
       "            m1_roll_yield  VX30_roll_yield  contango  adj_contango  \\\n",
       "Index                                                                \n",
       "2020-01-15      -0.073262         1.079228  1.579856      0.691593   \n",
       "2020-01-16      -0.345272         1.044683  1.755381      0.516132   \n",
       "2020-01-17      -0.148701         1.345339  1.820366      0.294819   \n",
       "2020-01-21      -0.547780         0.845411  1.473739      0.024955   \n",
       "2020-01-22      -0.933401         0.944598  1.969039     -0.333445   \n",
       "\n",
       "            m4m7_contango  fastcross  medcross  slowcross  stratcross  \n",
       "Index                                                                  \n",
       "2020-01-15       0.175329  -2.061873 -0.686448  -0.558709   -0.301367  \n",
       "2020-01-16       0.245894  -1.974771 -0.643606  -0.531692   -0.297797  \n",
       "2020-01-17       0.242328  -2.424087 -0.794834  -0.631411   -0.322770  \n",
       "2020-01-21       0.213847  -0.907874 -0.508083  -0.336285    0.013589  \n",
       "2020-01-22       0.056568  -1.249735 -0.535517  -0.373059   -0.041149  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variables = variables.dropna() \n",
    "\n",
    "scaler = StandardScaler() \n",
    "scaled_vars = scaler.fit_transform(variables)\n",
    "scaled_vars\n",
    "scaled_vars_pd = pd.DataFrame(scaled_vars).dropna()\n",
    "\n",
    "scaled_vars_pd.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "kmeans = KMeans(n_clusters = 3, random_state = 123)\n",
    "kmeans_fits = kmeans.fit_predict(scaled_vars_pd.dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn import datasets, metrics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Highly uneven clusters, these methods probably do not work too well.\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "dendrogram(linkage(scaled_vars_pd, method='complete'))\n",
    "plt.show()\n",
    "plt.figure(figsize=(20,10))\n",
    "dendrogram(linkage(scaled_vars_pd, method='ward'))\n",
    "plt.show()\n",
    "plt.figure(figsize=(20,10))\n",
    "dendrogram(linkage(scaled_vars_pd, method='average'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Defining the agglomerative clustering\n",
    "comp_cluster = AgglomerativeClustering(linkage='complete', \n",
    "                                       affinity = 'cosine',\n",
    "                                      n_clusters=3)\n",
    "\n",
    "# Fit model\n",
    "comp_clusters = comp_cluster.fit_predict(scaled_vars_pd)\n",
    "\n",
    "# Defining the agglomerative clustering\n",
    "ward_cluster = AgglomerativeClustering(linkage='ward',\n",
    "                                       affinity = 'euclidean',\n",
    "                                      n_clusters=3)\n",
    "\n",
    "# Fit model\n",
    "ward_clusters = ward_cluster.fit_predict(scaled_vars_pd)\n",
    "\n",
    "# Defining the agglomerative clustering\n",
    "avg_cluster = AgglomerativeClustering(linkage='average',\n",
    "                                      affinity = 'cosine',\n",
    "                                      n_clusters=3)\n",
    "\n",
    "# Fit model\n",
    "avg_clusters = avg_cluster.fit_predict(scaled_vars_pd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index\n",
       "2020-01-15   -0.003415\n",
       "2020-01-16   -0.009618\n",
       "2020-01-17   -0.003993\n",
       "2020-01-21    0.001187\n",
       "2020-01-22   -0.063023\n",
       "Name: Adj Close, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = reg_response[scaled_vars_pd.index]\n",
    "y_clf = clf_response[scaled_vars_pd.index]\n",
    "y.tail()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted Rand Index of the Complete Agglomerative Clustering solution for return values: 0.0\n",
      "Adjusted Rand Index of the Complete Agglomerative Clustering solution for sign values: 0.0010595677176385965\n",
      "The silhoutte score of the Complete Agglomerative Clustering solution: 0.25426932996798546\n",
      "Adjusted Rand Index of the Ward Agglomerative Clustering solution for return values: 0.0\n",
      "Adjusted Rand Index of the Ward Agglomerative Clustering solution for sign values: 0.0022527350722210703\n",
      "The silhoutte score of the Ward Agglomerative Clustering solution: 0.2848088574186514\n",
      "Adjusted Rand Index of the Average Agglomerative Clustering solution for return values: 0.0\n",
      "Adjusted Rand Index of the Average Agglomerative Clustering solution for sign values: 0.0004232004386310067\n",
      "The silhoutte score of the Average Agglomerative Clustering solution: 0.17715029792192732\n",
      "Adjusted Rand Index of the Kmeans 3 Clustering solution for return values: 0.0\n",
      "Adjusted Rand Index of the Kmeans 3 Clustering solution for return sign values: 0.0010991280196096243\n",
      "The silhoutte score of the Complete Agglomerative Clustering solution: 0.350193744147409\n"
     ]
    }
   ],
   "source": [
    "print(\"Adjusted Rand Index of the Complete Agglomerative Clustering 3 solution for return values: {}\"\n",
    "      .format(metrics.adjusted_rand_score(y, comp_clusters)))\n",
    "print(\"Adjusted Rand Index of the Complete Agglomerative Clustering 3 solution for sign values: {}\"\n",
    "     .format(metrics.adjusted_rand_score(y_clf, comp_clusters)))\n",
    "print(\"The silhoutte score of the Complete Agglomerative Clustering 3 solution: {}\"\n",
    "      .format(metrics.silhouette_score(scaled_vars_pd, comp_clusters, metric='euclidean')))\n",
    "\n",
    "print(\"Adjusted Rand Index of the Ward Agglomerative Clustering 3 solution for return values: {}\"\n",
    "      .format(metrics.adjusted_rand_score(y, ward_clusters)))\n",
    "print(\"Adjusted Rand Index of the Ward Agglomerative Clustering 3  solution for sign values: {}\"\n",
    "      .format(metrics.adjusted_rand_score(y_clf, ward_clusters)))\n",
    "print(\"The silhoutte score of the Ward Agglomerative Clustering 3 solution: {}\"\n",
    "      .format(metrics.silhouette_score(scaled_vars_pd, ward_clusters, metric='euclidean')))\n",
    "\n",
    "print(\"Adjusted Rand Index of the Average Agglomerative Clustering 3 solution for return values: {}\"\n",
    "      .format(metrics.adjusted_rand_score(y, avg_clusters)))\n",
    "print(\"Adjusted Rand Index of the Average Agglomerative Clustering 3  solution for sign values: {}\"\n",
    "      .format(metrics.adjusted_rand_score(y_clf, avg_clusters)))\n",
    "print(\"The silhoutte score of the Average Agglomerative Clustering 3 solution: {}\"\n",
    "      .format(metrics.silhouette_score(scaled_vars_pd, avg_clusters, metric='euclidean')))\n",
    "\n",
    "print(\"Adjusted Rand Index of the Kmeans 3 Clustering solution for return values: {}\"\n",
    "      .format(metrics.adjusted_rand_score(y, kmeans_fits)))\n",
    "print(\"Adjusted Rand Index of the Kmeans 3 Clustering solution for return sign values: {}\"\n",
    "      .format(metrics.adjusted_rand_score(y_clf, kmeans_fits)))\n",
    "print(\"The silhoutte score of the Kmeans 3 Clustering solution: {}\"\n",
    "      .format(metrics.silhouette_score(scaled_vars_pd, kmeans_fits, metric='euclidean')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Dimensionality Reduction With Clustering\n",
    "\n",
    "From just K-means and agglomerative clustering, the Ward Agglomerative clustering had the highest ARI score on sign values (.002), and Kmeans had the highest silhouette score (.35).\n",
    "\n",
    "We'll try clustering techniques on PCA and UMAP next.\n",
    "\n",
    "Use UMAP with parameters of nearest neighbors of 5 - 35, in steps of 5, and components 2-6\n",
    "Use a kmeans clustering algorithm with 3 clusters on the results\n",
    "Look at ARI score for return sign values and silhouette scores, since ARI for return values is most likely zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ilya\\Anaconda3\\lib\\site-packages\\umap\\spectral.py:229: UserWarning: Embedding a total of 2 separate connected components using meta-embedding (experimental)\n",
      "  n_components\n",
      "C:\\Users\\Ilya\\Anaconda3\\lib\\site-packages\\umap\\spectral.py:229: UserWarning: Embedding a total of 2 separate connected components using meta-embedding (experimental)\n",
      "  n_components\n",
      "C:\\Users\\Ilya\\Anaconda3\\lib\\site-packages\\umap\\spectral.py:229: UserWarning: Embedding a total of 2 separate connected components using meta-embedding (experimental)\n",
      "  n_components\n",
      "C:\\Users\\Ilya\\Anaconda3\\lib\\site-packages\\umap\\spectral.py:229: UserWarning: Embedding a total of 2 separate connected components using meta-embedding (experimental)\n",
      "  n_components\n",
      "C:\\Users\\Ilya\\Anaconda3\\lib\\site-packages\\umap\\spectral.py:229: UserWarning: Embedding a total of 2 separate connected components using meta-embedding (experimental)\n",
      "  n_components\n"
     ]
    }
   ],
   "source": [
    "import umap\n",
    "\n",
    "# Using UMAP\n",
    "\n",
    "neighbors = [5, 10, 15, 20, 25, 30, 35]\n",
    "components = [2, 3, 4, 5, 6]\n",
    "\n",
    "k_sil_scores = []\n",
    "k_ARI_scores = []\n",
    "w_sil_scores = []\n",
    "w_ARI_scores = []\n",
    "for i in neighbors:\n",
    "    k_component_sil = []\n",
    "    k_component_ari = []\n",
    "    w_component_sil = []\n",
    "    w_component_ari = []\n",
    "    for j in components:\n",
    "        \n",
    "        umap_results = pd.DataFrame(umap.UMAP(n_neighbors = i, n_components = j, min_dist = 0.3,\n",
    "                                metric = 'correlation').fit_transform(scaled_vars_pd))\n",
    "        \n",
    "        # k means and append\n",
    "        kmeans_umap = kmeans.fit_predict(umap_results)\n",
    "        k_component_sil.append(metrics.silhouette_score(umap_results, kmeans_umap, metric = 'euclidean'))\n",
    "        k_component_ari.append(metrics.adjusted_rand_score(y_clf, kmeans_umap))\n",
    "        \n",
    "        # ward hierarchical and append\n",
    "        ward_umap = ward_cluster.fit_predict(umap_results)\n",
    "        w_component_sil.append(metrics.silhouette_score(umap_results, ward_umap, metric = 'euclidean'))\n",
    "        w_component_ari.append(metrics.adjusted_rand_score(y_clf, ward_umap))\n",
    "        \n",
    "    # bind rows to matrices    \n",
    "    k_sil_scores.append(k_component_sil)\n",
    "    k_ARI_scores.append(k_component_ari)\n",
    "    w_sil_scores.append(w_component_sil)\n",
    "    w_ARI_scores.append(w_component_ari)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           2         3         4         5         6\n",
      "5   0.001764  0.000992  0.001176  0.001295  0.001397\n",
      "10 -0.000174  0.001792  0.001277  0.000980  0.001599\n",
      "15  0.000136  0.000070  0.001870  0.001109  0.002006\n",
      "20  0.000701  0.000640  0.001104  0.001206  0.001669\n",
      "25  0.001687  0.000443  0.000806  0.000875  0.001468\n",
      "30  0.000346  0.000691  0.002713  0.000665  0.000842\n",
      "35  0.000361  0.001074  0.002362  0.001132  0.000626\n",
      "           2         3         4         5         6\n",
      "5   0.001708  0.001385  0.001016  0.001067  0.001354\n",
      "10  0.000821  0.000766  0.001484  0.001912  0.001251\n",
      "15  0.000762  0.001130  0.001083  0.001148  0.001168\n",
      "20  0.000677  0.001581  0.001016  0.001431  0.001254\n",
      "25  0.001272  0.001672  0.001245  0.001782  0.001469\n",
      "30  0.001220  0.001204  0.001361  0.001591  0.001225\n",
      "35  0.000916  0.001153  0.001393  0.001353  0.001916\n",
      "           2         3         4         5         6\n",
      "5   0.448005  0.418587  0.419291  0.430321  0.427029\n",
      "10  0.483061  0.466128  0.475936  0.446628  0.445714\n",
      "15  0.480396  0.411686  0.443651  0.448936  0.445425\n",
      "20  0.473370  0.441271  0.441405  0.443205  0.437386\n",
      "25  0.457421  0.429290  0.425225  0.444328  0.435607\n",
      "30  0.469711  0.434036  0.419311  0.434125  0.425098\n",
      "35  0.469284  0.422972  0.429991  0.423610  0.430270\n",
      "           2         3         4         5         6\n",
      "5   0.453092  0.427104  0.424172  0.435320  0.432257\n",
      "10  0.505164  0.470448  0.465480  0.462241  0.453665\n",
      "15  0.505721  0.447327  0.455012  0.456402  0.454508\n",
      "20  0.480947  0.460029  0.455153  0.455051  0.454667\n",
      "25  0.480366  0.445208  0.447663  0.451301  0.448801\n",
      "30  0.477878  0.444420  0.450741  0.453016  0.440823\n",
      "35  0.479910  0.438805  0.447511  0.445276  0.451721\n"
     ]
    }
   ],
   "source": [
    "k_sil_scores_df = pd.DataFrame(k_sil_scores)\n",
    "k_ARI_scores_df = pd.DataFrame(k_ARI_scores)\n",
    "w_sil_scores_df = pd.DataFrame(w_sil_scores)\n",
    "w_ARI_scores_df = pd.DataFrame(w_ARI_scores)\n",
    "\n",
    "\n",
    "k_ARI_scores_df.index = k_sil_scores_df.index = w_sil_scores_df.index = w_ARI_scores_df.index = neighbors\n",
    "k_ARI_scores_df.columns = k_sil_scores_df.columns = w_sil_scores_df.columns = w_ARI_scores_df.columns = components\n",
    "print(w_ARI_scores_df)\n",
    "print(k_ARI_scores_df)\n",
    "print(w_sil_scores_df)\n",
    "print(k_sil_scores_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "k_sil_scores = []\n",
    "k_ARI_scores = []\n",
    "w_sil_scores = []\n",
    "w_ARI_scores = []\n",
    "\n",
    "components = [2, 3, 4, 5, 6]\n",
    "for i in components:\n",
    "    pca = PCA(n_components = i)\n",
    "    pca_results = pca.fit_transform(scaled_vars_pd)\n",
    "    k_pca = kmeans.fit_predict(pca_results)\n",
    "    w_pca = ward_cluster.fit_predict(pca_results)\n",
    "    k_sil_scores.append(metrics.silhouette_score(pca_results, k_pca, metric = 'euclidean'))\n",
    "    w_sil_scores.append(metrics.silhouette_score(pca_results, w_pca, metric = 'euclidean'))\n",
    "    k_ARI_scores.append(metrics.adjusted_rand_score(y_clf, k_pca))\n",
    "    w_ARI_scores.append(metrics.adjusted_rand_score(y_clf, w_pca))\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kmeans_PCA_sil</th>\n",
       "      <td>0.429035</td>\n",
       "      <td>0.396447</td>\n",
       "      <td>0.378062</td>\n",
       "      <td>0.365913</td>\n",
       "      <td>0.357494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ward_PCA_sil</th>\n",
       "      <td>0.362719</td>\n",
       "      <td>0.282536</td>\n",
       "      <td>0.318818</td>\n",
       "      <td>0.317109</td>\n",
       "      <td>0.274166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kmeans_PCA_ARI</th>\n",
       "      <td>0.001352</td>\n",
       "      <td>0.001401</td>\n",
       "      <td>0.000983</td>\n",
       "      <td>0.000983</td>\n",
       "      <td>0.000974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ward_PCA_ARI</th>\n",
       "      <td>0.002029</td>\n",
       "      <td>0.002874</td>\n",
       "      <td>0.001929</td>\n",
       "      <td>0.003238</td>\n",
       "      <td>0.001249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       2         3         4         5         6\n",
       "Kmeans_PCA_sil  0.429035  0.396447  0.378062  0.365913  0.357494\n",
       "Ward_PCA_sil    0.362719  0.282536  0.318818  0.317109  0.274166\n",
       "Kmeans_PCA_ARI  0.001352  0.001401  0.000983  0.000983  0.000974\n",
       "Ward_PCA_ARI    0.002029  0.002874  0.001929  0.003238  0.001249"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_df = pd.DataFrame([k_sil_scores, w_sil_scores, k_ARI_scores, w_ARI_scores])\n",
    "pca_df.columns = [2, 3, 4, 5, 6]\n",
    "pca_df.index = [\"Kmeans_PCA_sil\", \"Ward_PCA_sil\", \"Kmeans_PCA_ARI\", \"Ward_PCA_ARI\"]\n",
    "pca_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Visualization\n",
    "\n",
    "We have two clustering procedures -- the best ARI score -- PCA 5 ward clustering (with 3 clusters), and best silhouette score -- 2 component UMAP with 10 nearest neighbors, with K = 3 clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "umap_results = pd.DataFrame(umap.UMAP(n_neighbors = 10, n_components = 2, min_dist = 0.3,\n",
    "                                metric = 'correlation').fit_transform(scaled_vars_pd))\n",
    "        \n",
    "# k means\n",
    "kmeans_umap = kmeans.fit_predict(umap_results)\n",
    "\n",
    "pca = PCA(n_components = 5)\n",
    "pca_results = pca.fit_transform(scaled_vars_pd)\n",
    "w_pca = ward_cluster.fit_predict(pca_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.12543835806984438\n",
      "-0.06803094389942743\n",
      "0.23265702197245353\n",
      "0.27905113597765085\n",
      "-0.11330755422866556\n",
      "0.1399991924638928\n"
     ]
    }
   ],
   "source": [
    "np.unique(kmeans_umap)\n",
    "np.unique(w_pca)\n",
    "\n",
    "km_dummies = pd.get_dummies(kmeans_umap)\n",
    "km_dummies.index = y.index\n",
    "\n",
    "w_dummies = pd.get_dummies(w_pca)\n",
    "w_dummies.index = y.index\n",
    "\n",
    "\n",
    "y3 = pd.concat([y, y, y], axis = 1)\n",
    "\n",
    "km_dummies.columns = w_dummies.columns = ['c1', 'c2', 'c3']\n",
    "\n",
    "def annualized_returns(r, periodsInYear = 252):\n",
    "    return ((1+r).prod()**(periodsInYear/r.shape[0])-1)\n",
    "\n",
    "def annualized_vol(r, periodsInYear = 252):\n",
    "    return r.std(ddof=0)*np.sqrt(periodsInYear)\n",
    "\n",
    "def drawdown(return_series: pd.Series):\n",
    "    \"\"\"\n",
    "    Computes wealth index, drawdowns/max DDs for a time series\n",
    "    \"\"\"\n",
    "    wealth_idx = (1+return_series).cumprod()\n",
    "    previous_peaks = wealth_idx.cummax()\n",
    "    drawdown = (wealth_idx-previous_peaks)/previous_peaks\n",
    "    return pd.DataFrame({\n",
    "        \"Wealth\": wealth_idx,\n",
    "        \"Peaks\": previous_peaks,\n",
    "        \"Drawdown\": drawdown\n",
    "    })\n",
    "\n",
    "\n",
    "km_umap_c1 = km_dummies['c1'] * y\n",
    "km_umap_c2 = km_dummies['c2'] * y\n",
    "km_umap_c3 = km_dummies['c3'] * y\n",
    "\n",
    "w_pca_c1 = w_dummies['c1'] * y\n",
    "w_pca_c2 = w_dummies['c2'] * y\n",
    "w_pca_c3 = w_dummies['c3'] * y\n",
    "\n",
    "\n",
    "print(annualized_returns(km_c1))\n",
    "print(annualized_returns(km_c2))\n",
    "print(annualized_returns(km_c3))\n",
    "\n",
    "\n",
    "print(annualized_returns(w_pca_c1))\n",
    "print(annualized_returns(w_pca_c2))\n",
    "print(annualized_returns(w_pca_c3))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>C1</th>\n",
       "      <th>C2</th>\n",
       "      <th>C3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-07</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.996326</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-08</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.033750</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-09</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.988750</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.021352</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.011250</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             C1        C2   C3\n",
       "Index                         \n",
       "2008-01-07  1.0  0.996326  1.0\n",
       "2008-01-08  1.0  1.033750  1.0\n",
       "2008-01-09  1.0  0.988750  1.0\n",
       "2008-01-10  1.0  1.021352  1.0\n",
       "2008-01-11  1.0  1.011250  1.0"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 12))\n",
    "km_wealth = pd.DataFrame(pd.concat([drawdown(km_umap_c1)['Wealth'], drawdown(km_umap_c2)['Wealth'], drawdown(km_umap_c3)['Wealth']], axis = 1))\n",
    "km_wealth.columns = ['C1', 'C2', 'C3']\n",
    "plt.plot(km_wealth)\n",
    "plt.title(\"K-means/UMAP clusters equity curves\")\n",
    "plt.legend(['C1', 'C2', 'C3'])\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "wp_wealth = pd.DataFrame(pd.concat([drawdown(w_pca_c1)['Wealth'], drawdown(w_pca_c2)['Wealth'], drawdown(w_pca_c3)['Wealth']], axis = 1))\n",
    "wp_wealth.columns = ['C1', 'C2', 'C3']\n",
    "plt.plot(wp_wealth)\n",
    "plt.title(\"Ward/PCA clusters equity curves\")\n",
    "plt.legend(['C1', 'C2', 'C3'])\n",
    "plt.show()\n",
    "\n",
    "km_wealth.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Conclusions\n",
    "\n",
    "While the clustering doesn't simply lose money no matter what, the results still aren't as satisfactory as I'd like.\n",
    "The UMAP/Kmeans simply says that the top cluster stopped in 2014, and we definitely had good short vol opportunities past that (such as in 2017). \n",
    "\n",
    "Meanwhile, the middle cluster sees all of 2017 in that middle cluster (AKA stay on the sidelines). This is definitely incorrect. \n",
    "\n",
    "Lastly, while the bottom cluster correctly identifies the financial crisis as uniquely bad, there were other times to make money in long volatility that it doesn't pick up.\n",
    "\n",
    "Furthermore, this is all with the benfit of hindsight. Certainly, I would not trade on either of these clustering segmentations.\n",
    "\n",
    "Frustrating aspect is the temporal inconsistency. EG the ward/PCA C1 skyrockets until 2013 and then becomes inconsistent, while C3 simply has all of 2017 under its umbrella.\n",
    "\n",
    "There may be a way to fine-tune the clustering in the future depending on the exact nature of the features fed to it, but that will do it for this project."
   ]
  }
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