FIN5622_Project_4_Neil_Yejjey_tuk12930 (4).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<h2>FIN5622 Project #4\n",
    "    <p class=\"text-primary\">Stock Market Regime Detection with Clustering</p>\n",
    "</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-success\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:black\"><b>Project Introduction</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    In this project, we will run a variety of clustering algorithms in an attempt to identify broad stock market regimes. Specifically, we will use the SPX index to represent the stock market, and cluster the movement of various other macroeconimc variables over time consisting of monthly observations from 1962 to 2018. \n",
    "    </div>\n",
    "</div>\n",
    "<div class = \"panel panel-info\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:black\"><b>Instructions</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    Please code you solutions directly in this Notebook under each task in the cells provided below. Tasks are intended to be done in order with results from previous tasks informing work in later tasks. Include all code used to produce your results, and support your answers with comments if neccessary. <b>Any written text must be typed in a markdown cell to receive credit!</b> You may feel free to add additional Code and/or Markdown cells as needed. Code must be written using <b>Python 3</b>.\n",
    "    <br><br>Please order the cells in your Notebook in the order in which they are intended to run! It is expected that your Notebook is able to run in sequence from start to finish without any errors (before submitting check by: <code>Kernel > Restart & Run All</code>).\n",
    "    <br><br>Once ready, please submit your assignment as an <b>.ipynb file</b> (<code>File > Download as > Notebook</code>). You must name your files in the following format: <b><code>FIN5622_Project_1_Richard_Lee_tuc12345.ipynb</code></b>.\n",
    "    </div>\n",
    "</div>\n",
    "\n",
    "---\n",
    "<div class = \"alert alert-block alert-warning\">\n",
    "    <b>Note</b>: Import all libraries that you plan on using in the code block below.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans, AgglomerativeClustering\n",
    "from sklearn.mixture import GaussianMixture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 1</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    The \"SPX_Macroeconomic_Variables.csv\" dataset contains time series data on various macroeconomic variables ranging from 7/1/1962 to 10/1/2018 as monthly observations. Read in the \"SPX_Macroeconomic_Variables.csv\" file using the pandas <code>read_csv()</code> function and store the dataframe in a variable called <code>macro_data</code>. Use the <code>'Date'</code> columns as the index for the dataframe. \n",
    "    <br><br>\n",
    "    Include this additional argument in the <code>read_csv()</code> to tell python to interpret these values as dates, rather than text: \n",
    "    <ul>\n",
    "        <li><code>parse_dates = ['Date']</code></li>\n",
    "    </ul>\n",
    "    <br>\n",
    "    Aside from the SPX index itself, the dataset contains 14 other broad macroeconomic variables that describes the general state of the U.S. economy. In this analysis, we will try to leverage changes in these macroeconomic variables to identify regimes in the stock market. The definitions for each column in the dataset are as follows:\n",
    "    <ul>\n",
    "        <li><b>SPX</b>: Price of the S&P 500 index.</li>\n",
    "        <li><b>Recession</b>: U.S. recessions as inferred by GDP-based recession indicator.</li>\n",
    "        <li><b>GDP</b>: Annualized percentage change in U.S. GDP.</li>\n",
    "        <li><b>SPDIV</b>: The dividend yield of the S&P 500.</li>\n",
    "        <li><b>SPPE</b>: P/E ratio of the S&P 500.</li>\n",
    "        <li><b>BUSLOANS</b>: Outstanding real commercial and industrial loans.</li>\n",
    "        <li><b>GS1</b>: 1-year Treasury rates.</li>\n",
    "        <li><b>GS10</b>: 10-year Treasury rates.</li>\n",
    "        <li><b>PCEPI</b>: Personal consumption expenditures index.</li>\n",
    "        <li><b>INDPRO</b>: Industrial production index.</li>\n",
    "        <li><b>HOUST</b>: Total new privately owned housing units started in thousands.</li>\n",
    "        <li><b>AAA</b>: AAA-rated corporate bond yields.</li>\n",
    "        <li><b>NONREVSL</b>: Total real outstanding nonrevolving credit owned and securitized.</li>\t\n",
    "        <li><b>UNRATE</b>: U.S. national unemployment rate.</li>\t\n",
    "        <li><b>UEMPMEAN</b>: Average duration of umeployment in weeks.</li>\n",
    "    </ul>\n",
    "    <br>\n",
    "    Check that the shape of <code>macro_data</code> is <code>(676, 15)</code>.\n",
    "    <p style=\"text-align:right;\">[5pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(676, 15)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro_data = pd.read_csv(\"SPX_Macroeconomic_Variables.csv\" , index_col = \"Date\", parse_dates = [\"Date\"])\n",
    "macro_data.shape"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 2</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    Before we can cluster the data, we need to remove any stationarity in our macroeconomic variables by taking the first-order difference. \n",
    "        <br>\n",
    "        <ul>\n",
    "            <li>With the exception of \"<b>SPX</b>\" and \"<b>Recession</b>\", take the first order difference of all other macroeconomic variables in the dataframe <code>macro_data</code>.</li>\n",
    "           <li>Create a list containing the columns names of <code>macro_data</code>, excluding \"<b>SPX</b>\". Store this list in a variable called <code>features</code>.</li>\n",
    "            <li>Drop any rows in <code>macro_data</code> containing NaN's as a result of the differencing (which should only be the first row).</li>\n",
    "        </ul>\n",
    "    <br>\n",
    "    Recall that you can quickly take differences by using the <code>.diff()</code> method for a dataframe. Check that the shape of <code>SPX</code> is now <code>(675, 15)</code>. Your final <code>macro_data</code> dataframe should look like this:\n",
    "\n",
    "![Task_2.JPG](attachment:Task_2.JPG)\n",
    "    <p style=\"text-align:right;\">[15pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "features = macro_data.columns.tolist()\n",
    "features.remove(\"SPX\")\n",
    "features.remove(\"Recession\")\n",
    "features = [\"Recession\"]+ features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "macro_data.loc[:, features] = macro_data[features].diff(1)\n",
    "macro_data = macro_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SPX</th>\n",
       "      <th>Recession</th>\n",
       "      <th>GDP</th>\n",
       "      <th>SPDIV</th>\n",
       "      <th>SPPE</th>\n",
       "      <th>BUSLOANS</th>\n",
       "      <th>GS1</th>\n",
       "      <th>GS10</th>\n",
       "      <th>PCEPI</th>\n",
       "      <th>INDPRO</th>\n",
       "      <th>HOUST</th>\n",
       "      <th>AAA</th>\n",
       "      <th>NONREVSL</th>\n",
       "      <th>UNRATE</th>\n",
       "      <th>UEMPMEAN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <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>1962-08-01</th>\n",
       "      <td>58.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003793</td>\n",
       "      <td>-0.084704</td>\n",
       "      <td>0.283483</td>\n",
       "      <td>0.1966</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.0277</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.5429</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-09-01</th>\n",
       "      <td>58.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.003797</td>\n",
       "      <td>0.043264</td>\n",
       "      <td>-0.402858</td>\n",
       "      <td>0.4502</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.1661</td>\n",
       "      <td>-193.0</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.3832</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-10-01</th>\n",
       "      <td>56.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.003784</td>\n",
       "      <td>0.146515</td>\n",
       "      <td>-0.730073</td>\n",
       "      <td>0.4751</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>0.0277</td>\n",
       "      <td>209.0</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.5906</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-11-01</th>\n",
       "      <td>60.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.006813</td>\n",
       "      <td>-0.212852</td>\n",
       "      <td>0.955516</td>\n",
       "      <td>0.3511</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.1108</td>\n",
       "      <td>89.0</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.6132</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-12-01</th>\n",
       "      <td>62.64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.031214</td>\n",
       "      <td>-0.119487</td>\n",
       "      <td>0.567464</td>\n",
       "      <td>0.2378</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-58.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.4846</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-01</th>\n",
       "      <td>2754.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.008790</td>\n",
       "      <td>-0.024008</td>\n",
       "      <td>0.170520</td>\n",
       "      <td>18.4470</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.6662</td>\n",
       "      <td>-152.0</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>5.6687</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>2793.64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.008742</td>\n",
       "      <td>-0.009928</td>\n",
       "      <td>0.066382</td>\n",
       "      <td>17.2396</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.4505</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>16.9023</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>2857.82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.248532</td>\n",
       "      <td>-0.025606</td>\n",
       "      <td>0.241861</td>\n",
       "      <td>0.6995</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.9284</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>19.0339</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>2901.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.007412</td>\n",
       "      <td>-0.011826</td>\n",
       "      <td>0.012337</td>\n",
       "      <td>5.5506</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.1968</td>\n",
       "      <td>-43.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>12.7751</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>2785.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.007376</td>\n",
       "      <td>0.092023</td>\n",
       "      <td>-1.380737</td>\n",
       "      <td>19.2187</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.3089</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>0.16</td>\n",
       "      <td>15.8984</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>675 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                SPX  Recession       GDP     SPDIV      SPPE  BUSLOANS   GS1  \\\n",
       "Date                                                                           \n",
       "1962-08-01    58.52        0.0  0.003793 -0.084704  0.283483    0.1966 -0.09   \n",
       "1962-09-01    58.00        0.0 -0.003797  0.043264 -0.402858    0.4502 -0.14   \n",
       "1962-10-01    56.17        0.0 -0.003784  0.146515 -0.730073    0.4751 -0.08   \n",
       "1962-11-01    60.04        0.0  4.006813 -0.212852  0.955516    0.3511  0.02   \n",
       "1962-12-01    62.64        0.0 -0.031214 -0.119487  0.567464    0.2378  0.01   \n",
       "...             ...        ...       ...       ...       ...       ...   ...   \n",
       "2018-06-01  2754.35        0.0 -0.008790 -0.024008  0.170520   18.4470  0.06   \n",
       "2018-07-01  2793.64        0.0 -0.008742 -0.009928  0.066382   17.2396  0.06   \n",
       "2018-08-01  2857.82        0.0 -0.248532 -0.025606  0.241861    0.6995  0.06   \n",
       "2018-09-01  2901.50        0.0 -0.007412 -0.011826  0.012337    5.5506  0.11   \n",
       "2018-10-01  2785.46        0.0 -0.007376  0.092023 -1.380737   19.2187  0.09   \n",
       "\n",
       "            GS10  PCEPI  INDPRO  HOUST   AAA  NONREVSL  UNRATE  UEMPMEAN  \n",
       "Date                                                                      \n",
       "1962-08-01 -0.03  0.025  0.0277   67.0  0.01    0.5429     0.3      -0.1  \n",
       "1962-09-01  0.00  0.083  0.1661 -193.0 -0.03    0.3832    -0.1      -0.4  \n",
       "1962-10-01 -0.05 -0.018  0.0277  209.0 -0.04    0.5906    -0.2       0.0  \n",
       "1962-11-01 -0.01  0.013  0.1108   89.0 -0.03    0.6132     0.3      -0.8  \n",
       "1962-12-01 -0.06 -0.008  0.0000  -58.0 -0.01    0.4846    -0.2       0.3  \n",
       "...          ...    ...     ...    ...   ...       ...     ...       ...  \n",
       "2018-06-01 -0.07  0.122  0.6662 -152.0 -0.04    5.6687     0.2      -0.1  \n",
       "2018-07-01 -0.02  0.158  0.4505    7.0 -0.09   16.9023    -0.1       1.9  \n",
       "2018-08-01  0.00  0.093  0.9284   96.0  0.01   19.0339    -0.1      -0.5  \n",
       "2018-09-01  0.11  0.141  0.1968  -43.0  0.10   12.7751    -0.1       1.5  \n",
       "2018-10-01  0.15  0.211  0.3089  -28.0  0.16   15.8984     0.1      -1.7  \n",
       "\n",
       "[675 rows x 15 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro_data"
   ]
  },
  {
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    }
   },
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 3</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    While there is no \"correct\" choice for the number of clusters, there are several different methods or heuristics we can use to help us select an appropriate number of clusters. One such method is by examining the \"Elbow plot\".\n",
    "    <br>\n",
    "    <ul>\n",
    "        <li>Fit the k-means algorithm on the <code>SPY</code> dataframe using different numbers of cluster ranging from 1 to 10 (use a <code>for</code> loop here!). Remember to set <code>random_state = 5622</code> and <code>init = 'k-means++'</code>.</li>  \n",
    "        <li>At each iteration, store the weighted sum of squares (WSS) metric from the resulting k-means cluster model in a list. The WSS metric for the model is saved in the model object's <code>.inertia_</code> attribute.</li>\n",
    "        <li>Lastly, plot a line plot of the list of WSS, with WSS on the y-axis and number of clusters on the x-axis labeled.</li>\n",
    "    </ul>\n",
    "    <br>\n",
    "    Recall that the <code>.fit()</code> method is used to fit the k-means clustering model on a training dataset. And remember to only train using the features identified in the <code>features</code> list! <b>Closing price of SPX should not be used as a feature!</b>\n",
    "    <br><br>\n",
    "    Your plot should look like this:\n",
    "        \n",
    "![Task_3.JPG](attachment:Task_3.JPG)\n",
    "    <br>\n",
    "    For our analysis, we will select 3 clusters. <b>However, briefly explain why it is likely not a good choice to use 10 clusters for this analysis.</b>\n",
    "    <p style=\"text-align:right;\">[20pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\laksh\\anaconda3.1\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:881: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "distance = []\n",
    "for i in range(1, 11):\n",
    "    kmeans_model = KMeans(n_clusters = i, init = \"k-means++\", random_state = 5622).fit(macro_data[features])\n",
    "    distance.append(kmeans_model.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(distance, marker = \"o\")\n",
    "plt.xlabel(\"Number of Clusters\")\n",
    "plt.ylabel(\"WSS\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reason why it is not a good choice to use 10 clusters for this analysis is that too many clusters will lead to diminishing returns. "
   ]
  },
  {
   "attachments": {
    "Task_4.JPG": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 4</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    With the number of clusters determined, refit the kmeans model assuming 3 clusters, and use it to cluster the daily observations in the <code>SPX</code> dataframe. Remember to set <code>random_state = 5622</code> and <code>init = 'k-means++'</code>. Store the cluster assignments in variable called <code>kmeans_clusters</code>. \n",
    "    <br><br>\n",
    "    Recall that the <code>.predict()</code> method is used to assign clusters using the fitted model.\n",
    "    <br><br>\n",
    "    Using the values in the <code>kmeans_clusters</code> calculate the average of all fields within each of the 3 clusters (this can be done very easily using the dataframe's <code>.groupby()</code> method).\n",
    "    <br><br>\n",
    "    Your final output should look somthing like this:\n",
    "\n",
    "![Task_4.JPG](attachment:Task_4.JPG)\n",
    "    <p style=\"text-align:right;\">[20pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans_model = KMeans(n_clusters = 3, init = \"k-means++\", random_state = 5622).fit(\n",
    "    macro_data[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans_clusters = kmeans_model.fit_predict(macro_data[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SPX</th>\n",
       "      <th>Recession</th>\n",
       "      <th>GDP</th>\n",
       "      <th>SPDIV</th>\n",
       "      <th>SPPE</th>\n",
       "      <th>BUSLOANS</th>\n",
       "      <th>GS1</th>\n",
       "      <th>GS10</th>\n",
       "      <th>PCEPI</th>\n",
       "      <th>INDPRO</th>\n",
       "      <th>HOUST</th>\n",
       "      <th>AAA</th>\n",
       "      <th>NONREVSL</th>\n",
       "      <th>UNRATE</th>\n",
       "      <th>UEMPMEAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>722.589396</td>\n",
       "      <td>-0.002747</td>\n",
       "      <td>0.015919</td>\n",
       "      <td>0.000798</td>\n",
       "      <td>-0.013702</td>\n",
       "      <td>2.747168</td>\n",
       "      <td>0.002473</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.133005</td>\n",
       "      <td>0.129090</td>\n",
       "      <td>1.813187</td>\n",
       "      <td>0.001126</td>\n",
       "      <td>4.398737</td>\n",
       "      <td>-0.005769</td>\n",
       "      <td>0.045879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>631.102692</td>\n",
       "      <td>-0.012821</td>\n",
       "      <td>0.221319</td>\n",
       "      <td>-0.019695</td>\n",
       "      <td>0.103940</td>\n",
       "      <td>3.370214</td>\n",
       "      <td>-0.013654</td>\n",
       "      <td>-0.021795</td>\n",
       "      <td>0.139987</td>\n",
       "      <td>0.175211</td>\n",
       "      <td>141.262821</td>\n",
       "      <td>-0.024872</td>\n",
       "      <td>4.498848</td>\n",
       "      <td>-0.013462</td>\n",
       "      <td>0.012821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>630.226065</td>\n",
       "      <td>0.019355</td>\n",
       "      <td>-0.254721</td>\n",
       "      <td>0.006775</td>\n",
       "      <td>-0.034238</td>\n",
       "      <td>4.349050</td>\n",
       "      <td>0.003806</td>\n",
       "      <td>0.015484</td>\n",
       "      <td>0.140252</td>\n",
       "      <td>0.057017</td>\n",
       "      <td>-147.987097</td>\n",
       "      <td>0.021097</td>\n",
       "      <td>3.642496</td>\n",
       "      <td>0.016774</td>\n",
       "      <td>-0.070323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          SPX  Recession       GDP     SPDIV      SPPE  BUSLOANS       GS1  \\\n",
       "0  722.589396  -0.002747  0.015919  0.000798 -0.013702  2.747168  0.002473   \n",
       "1  631.102692  -0.012821  0.221319 -0.019695  0.103940  3.370214 -0.013654   \n",
       "2  630.226065   0.019355 -0.254721  0.006775 -0.034238  4.349050  0.003806   \n",
       "\n",
       "       GS10     PCEPI    INDPRO       HOUST       AAA  NONREVSL    UNRATE  \\\n",
       "0  0.000385  0.133005  0.129090    1.813187  0.001126  4.398737 -0.005769   \n",
       "1 -0.021795  0.139987  0.175211  141.262821 -0.024872  4.498848 -0.013462   \n",
       "2  0.015484  0.140252  0.057017 -147.987097  0.021097  3.642496  0.016774   \n",
       "\n",
       "   UEMPMEAN  \n",
       "0  0.045879  \n",
       "1  0.012821  \n",
       "2 -0.070323  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro_data.groupby(kmeans_clusters).mean()"
   ]
  },
  {
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    }
   },
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 5</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    Use the code below to visually plot the kmeans market regimes where each cluster / regime is color coded:\n",
    "    <br>\n",
    "    <code>\n",
    "plt.figure(figsize = (15, 7))\n",
    "plt.plot(macro_data['SPX'], color = 'black')\n",
    "for i in range(3):\n",
    "    plt.scatter(macro_data.index[kmeans_clusters == i], macro_data['SPX'][kmeans_clusters == i], label = f'cluster {i}')\n",
    "plt.legend()\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('SPX Price')\n",
    "plt.show()</code>\n",
    "    <br><br>\n",
    "    Your plot should look something like this (although the colors may be swapped):\n",
    "\n",
    "![Task_5.JPG](attachment:Task_5.JPG)\n",
    "    <br>\n",
    "    Based on visually assessing this plot and quantitatively assessing the averages calculated in the previous task, briefly describe the characteristics of each market regime according to the k-means model. Do you believe the k-means clustering algorithm performed well in identifying 3 different market regimes?\n",
    "    <p style=\"text-align:right;\">[15pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 7))\n",
    "plt.plot(macro_data['SPX'], color = 'black')\n",
    "for i in range(3):\n",
    "    plt.scatter(macro_data.index[kmeans_clusters == i], macro_data['SPX'][kmeans_clusters == i], label = f'cluster {i}')\n",
    "plt.legend()\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('SPX Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model did a good job predicting the rises declines in the price, but proved to be ineffecive in predicting the prices of the early flat rates\n",
    "\n"
   ]
  },
  {
   "attachments": {
    "Task_6.JPG": {
     "image/jpeg": 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    }
   },
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 6</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    Repeat tasks 5 - 6, this time using the Gaussian Mixture Model (GMM), keeping consistent the assumption that there are 3 clusters. Use the following parameters when fitting the model:\n",
    "    <br>\n",
    "    <ul>\n",
    "        <li><code>n_components = 3</code></li>\n",
    "        <li><code>covariance_type = \"full\"</code></li>\n",
    "        <li><code>n_init = 100</code></li>\n",
    "        <li><code>random_state = 5622</code></li>\n",
    "    </ul>\n",
    "    <br>\n",
    "    Store the cluster assignments in variable called <code>gmm_clusters</code>. \n",
    "    <br><br>\n",
    "    Your plot and final output should look something like this (although the colors may be swapped): \n",
    "        \n",
    "![Task_6.JPG](attachment:Task_6.JPG)\n",
    "    <br>\n",
    "    Compare and contrast the clusters created under the k-means clustering algorithm vs. GMM. Which algorithm do you feel performed better in identifying 3 different market regimes, and why?\n",
    "    <p style=\"text-align:right;\">[25pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "gmm_model = GaussianMixture(n_components = 3, covariance_type = \"full\", n_init = 100, random_state = 5622)\n",
    "gmm_model = gmm_model.fit(macro_data[features])\n",
    "gmm_clusters = gmm_model.predict(macro_data[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RkUkjlUrRE00zeGY+xm1o/Y9Wep/tJbonir82wL+VlUAy6vQTn6QUBB5DuDnMyodWsuj+Rax8aCXh5vBJ3/OBBx5g0aJFXHjhhXz0ox897PiVV17JcJP7AwcOMHfuXAA2b97MxRdfzOLFi1m0aBHbtm1j1apV7Nixg8WLF3PrrbcCcO+993LRRRexaNEi7rjjDgB27drFeeedx6c//WmWLl2aq3I07L3vfS/GGIwxXHzxxbS0tJz05xQRERERmYx6enoASM70c+ZdZ+IucDP4yiCxPTECswJ0eNzOiX2T9zuz9gQeRbg5TMOzDcTSMQDaI+00PNsAcMIbQDdv3sxdd93FM888Q3l5Od3d3aO+9lvf+hb19fXcdNNNJBIJ0uk099xzD6+88gqbNm0CYP369Wzbto3nn38eay3XXHMNTz75JLNnz2br1q1873vfO2bmMJlM8t///d80Njae0OcTEREREZnMws1h/unRfwLAE3Ljr/QTnBdk4OUBMtEMwdlBKlNp5+Si2nGc6clRJvAoGjc25gLAYbF0jMaNJx4gPf7441x33XWUl5cDUFpaOuprL7nkEv7pn/6Jr3zlK+zevZtgMHjYOevXr2f9+vUsWbKEpUuXsmXLFrZt2wbAnDlzWL58+THf49Of/jRve9vbuOKKK47jU4mIiIiITH7DSaD2znYAXIUesJbg7CDpASfwK6z1Ud/TC94grFgzjrM9OQoCj6Ij0nFc46Nhrc01ljwaj8eT27MXix0MQm+88UYeeeQRgsEgV111FY8//vgR77969Wo2bdrEpk2b2L59Ox//+McBCIVCx3zff/zHf6Szs5Ovfe1rx/uxREREREQmveEk0HDA58n3gDEE5jh9ATFQUuOHvDJ4/32w6PpxnO3JURB4FJWhyuMaH40VK1awbt26XPPJIy0HnTt3Li+++CIADz30UG68ubmZ+fPnc8stt3DNNdfQ1NREQUEBAwMDuXOuuuoqvvvd7zI4OAg4zS3379//pvP6zne+w2OPPcYPf/hDXC79SYiIiIjI9DOc7En1pwBwFzh7/4JznBV4vhk+BkJeGoqChPOPnWCZ6PSN/yjql9YTcAcOGQu4A9QvrT/he15wwQV86Utf4u1vfzsXXnghn/vc5w475wtf+ALf/OY3ufTSS3NNKgEefPBBFixYwOLFi9myZQsf+9jHKCsr47LLLmPBggXceuutrFy5khtvvJFLLrmEhQsXct111x0SJB7NX//1X7Nv3z4uueQSFi9ezNq1k7fSkYiIiIjIiRhO9iQOJMAN3hIvAN4KL+6Qm8BsJzY42S1iE4Gx1o73HMbEsmXL7HCVzWGvvfYa55133qjvEW4O07ixkY5IB5WhSuqX1p9wUZjJ7nh/dyIiIiIik8nwnsBt/76NoeYhzrn3nNyxyOsRvMVefDN8ABgMTTc3jddUR80Y86K1dtkbx1Ud9Bjq5tdN26BPRERERGQ6Gf7ef9Pam/BV+KgKVRFNRemN9xI6+9DlnyezRWwi0HJQERERERERnEDQ3+fnI5d+hPXXrWfVxatO+RaxiWDaZQJHU6FTDjVVlwyLiIiIiIwUiUTYv38/8+bNAw5mB6faFrFpFQQGAgG6urooKytTIDhK1lq6uroIBAJvfrKIiIiIyGTVtI5d3/8HAOa99k1oOhMWXT8lt4hNqyCwtraWlpYWOjs7x3sqk0ogEKC2tna8pyEiIiIiMjaa1sGjt9C8px+Aef5uePQW59gk7gd4NNMqCPR6vbnUroiIiIiICAAb1kIyys7eDADzil2QjDrjUzAIVGEYERERERGZ3vpaAPhDW4YZIcOMkDlkfKpRECgiIiIiItNauKKWd9dU8aM9FnN+Pv+bn20JUTQ1t0RNq+WgIiIiIiIiI4WbwzQU+OhrS5DqTeG+oICG8mJw+6hbsWa8pzcmlAkUEREREZFpq3FjIzGbZPC1QQBC54eIuVw0Vs6akvsBQUGgiIiIiIhMYx2RDgCGXh/CU+LBV+FzxpP94zmtMaUgUEREREREpq3KUCUAiX0JAjWBXD/x4fGpSEGgiIiIiIhMW/VL6/G7/MQ74vhmOFnAgDtA/dL6cZ7Z2FFhGBERERERmbbq5tfR193HTdGb8M/0UxWqon5pPXXz68Z7amNmzDKBxphZxpjfGGNeM8ZsNsbUZ8cbjDGtxphN2Z/3jrhmtTFmuzFmqzHmqhHjbzHGvJw9dp8ZztGKiIiIiIicpHnJeQB8+8Zvs/669VM6AISxXQ6aAj5vrT0PWA58xhhzfvbY1621i7M//wuQPfZh4ALgauAbxhh39vxvAp8Ezsr+XD2G8xYRERERkWki3BzmL3/wlwDcveNuws3hcZ7R2BuzINBa226t3Zh9PgC8BtQc45IPAD+y1sattTuB7cDFxpgqoNBa+ztrrQUeAK4dq3mLiIiIiMj0EG4O0/BsA/v37AcDvfm9NDzbMOUDwdNSGMYYMxdYAvw+O/Q3xpgmY8x3jTEl2bEaYO+Iy1qyYzXZ528cP9L7fNIY84Ix5oXOzs5T+RFERERERGSKadzYSCwdI7E/gbfci8vjIpaO0bixcbynNqbGPAg0xuQDPwb+zlrbj7O08wxgMdAO/MvwqUe43B5j/PBBa79trV1mrV1WUVFxslMXEREREZEpbLhHYGxPDH+V/7DxqWpMg0BjjBcnAPy+tfYnANbafdbatLU2A/wHcHH29BZg1ojLa4G27HjtEcZFREREREROWGWokmRvknhbnNC5oUPGp7KxrA5qgP8EXrPWfm3EeNWI0/4EeCX7/BHgw8YYvzFmHk4BmOette3AgDFmefaeHwN+NlbzFhERERGRqS/cHGYo2kvk1UEA8s/PB6Z+j0AY2z6BlwEfBV42xmzKjv0D8BFjzGKcJZ27gL8CsNZuNsasA17FqSz6GWttOnvdp4D/AoLAL7I/IiIiIiIixy3cHKbh6duJ2SSRLUO48lwEZvkpTmdYNefqKd8iwjgFN6eeZcuW2RdeeGG8pyEiIiIiIhPMFT+6gp5ID3u/tZeBlwbIvzCfObfMoSqZYv2AGz77ypvfZBIwxrxorV32xvGxzASKiIiIiIhMKOHmML3xXnp/30v/C/2UvK2E8veUA9DhcUNfy5vcYfJTECgiIiIiItNG48ZGrLV0/bILf62f6v9TjVN6BCpTaSiqfZM7TH6npU+giIiIiIjIRNAR6SC2K0Zsb4yyd5flAkCspb5/CFasGd8JngYKAkVEREREZNqoDFUS2RIBoODCgtx4kYW6d90Li64fr6mdNgoCRURERERkemhaR/2+NqKvDeKr9OEt9gJOW4jVb//KtAgAQXsCRURERERkOmhaB4/ewlXxIZKvR6i4uAhjLZW+YuqXr57ybSFGUhAoIiIiIiJT34a1kIyyqSPDUMzynxVxPrxrLxQB0ygABC0HFRERERGR6SDb+uG3u1IAvH2O+5Dx6URBoIiIiIiITHnhilpW1lbzj/u95M30sbEy3zkwDVpCvJGWg4qIiIiIyJQWbg7TUOAjmk4QeX2IorcW0VBeCm4fddOgJcQbKRMoIiIiIiJT2j3P30PMJontiZGJZgidEyLmctFYOWvaVAQdSUGgiIiIiIhMWeHmML3xXgC6f9MNBkLnhgDoSPaP48zGj4JAERERERGZshqfuxuA/pf66fltD+XvKcdb4vQHrAxVjufUxo32BIqIiIiIyNTUtI6ORC82DR0/7MBf42fGB2fkDtcvrR/HyY0fZQJFRERERGRq2rCWylSanqd7SOxPUPmnlbg8TghU5CuaVg3iR1IQKCIiIiIiU1NfC/U9vUQ29eOr9JF/odMWIpDJsPqtq8d5cuNHQaCIiIiIiExNRbXURYYI7I5SMsuPC6hKpmgYMtM2CwgKAkVEREREZKpasYaBjJ99PWn+Nj9B0669rN/XQ90V06834EgKAkVEREREZGpadD2vnu0Uf1kwww1Fs+D9903L3oAjqTqoiIiIiIhMSeHmMH//zA8AuPfyJXjeu3paLwMdpkygiIiIiIhMOeHmMA3PNrCveR/GZ+gN9dLwbAPh5vB4T23cKQgUEREREZEpp3FjI7F0jOjuKIGaAMZliKVjNG5sHO+pjTsFgSIiIiIiMuW0R9pJDaQY2jZE6PxQbrwj0jGOs5oYFASKiIiIiMiUMrzks39jP2Sg6KKi3LHKUOV4TWvCUBAoIiIiIiJTyvCSz/4/9OOt8BKYE8gdq19aP17TmjAUBIqIiIiIyJTSEenAWsvQ9iEKFhVgjMkdU3VQBYEiIiIiIjLFVIYqyQxlyMQy+Mp9ufGqUNU4zmriUBAoIiIiIiJTSv3Sekyfk/3zlnkBCLgDWgqapSBQRERERESmlLr5dVw/83oAvCVeqkJVNFzaoKWgWZ7xnoCIiIiIiMipVpVyln4+8VdPMGvWrHGezcSiTKCIiIiIiEw5e/fuxeVyUVWlfYBvpEygiIiIiIhMbk3rYMNa6GuBolpYsYaWlhaqq6vxeBTyvJEygSIiIiIiMnk1rYNHb8H27qEvloG+vfDwp9n79I+opR2+vsA5R3IUBIqIiIiIyOS1YS0ko/zNy4bSrw9ybv5MVlZXsKU3yaxC4wSFj96iQHAEBYEiIiIiIjJphVPdXD6rhu81JckkLJ2/7KLN46a9P0OyLNsjMBl1gkUBFASKiIiIiMgkFW4O01BRSmdfmuiOKK6gi57f9tD3uz4yCcuL55YQDuU5J/e1jO9kJxAFgSIiIiIiMik1bmwkZgz9L/YDMOvTTiuIlu+04C5wU3BREQ3lpU4gWFQ7nlOdUBQEioiIiIjIpNQR6QAguiOKt8JLwcICKt5fARkovbIUl89FzOWisbQEVqwZ59lOHKqXKiIiIiIik1JlqJL2SDuJ/Qn8M/0AlNeV4wq4KLm8JHdeh8cNi64fr2lOOMoEioiIiIjI+Gla57RxaCg+7nYO9Uvr8bv8xDvi+GY6RWBcHhflV5XjDrlz51WG1DB+JAWBIiIiIiIyPrI9/n6ePMA7SmewqARW/qGB8BO3j+ryuvl1fP78z5OJZvDP8FPkK8Lr8h5yTsAdoH5p/VjMftJSECgiIiIiIuMi/NRaLq8s5RN/sPz289sZ3D5Eu8fNql0/5YofXUG4Ofym95ifmg/At2/8Nk9/5Gm+fNmXqQpVYTBUhapouLSBuvl1Y/1RJhXtCRQRERERkdOraR13/m4tD4YgNWDp/PkBbMqy95t7mVM/h+CcIL3xXhqevh1+8UXqOluc6p4r1hyyty/cHGbVD1YBcPeOuzHNhrr5dQr63oQygSIiIiIicvo0rSP861t50A8Yw/6f7icTz1D717XYpGVHww6Gtg8BELNJGv1pwELfXnj0Fuf65jCX//ByVj21iv179oOB3lAvDc82jCp7ON0pCBQRERERkdNnw1oaC/MY3BKhY10H3b/ppuxdZRQvL+asu84CC4OvDuZO3xPJcKkpc3r9JaOEn1pLw7MN9CX6AIh3xPGWenF5XcTSMRo3No7XJ5s0tBxUREREREROn74WOkpq6fhRB7HdMbxlXmZ8aAYAnkIP3gov8ZY44AR4O+/ZybZYhtu/fjYAjf40sXSM9u+345vpo//FfoqWF+VuP9w7UI5OQaCIiIiIiJwW4eYwjbNryWQs8fY4RZcUUXVTFe7AwXYOgdoAsZYYAO0/aCc9mMamLAdejtC4pJgOj5t0JE3Xr7qcC9ww4/0zctdXhipP62eajLQcVERERERExlz4idtpePKLtCQyJDqT2IQl/7x8PPkH81LLK5dTPruIeEecTDJDsjtJ/oJ8PEUe+n7fR4fHjT+VlwsSASrqKvDNcHoEBjIZtYMYBWUCRURERERkbDWto3HHjznwepTdX99NcG4QAH+tH6ylyldM/fLV1A1G+JH/f/hIBuLtcdKDaTzzPRQuK6Tntz1440F6uq4htef/AnDO187BW+oFaynOZFgVdaky6CgoCBQRERERkbG1YS2tPsue+/Zgk5ahbUNgIFATAGNYf+PTznlfX8DCshQAmb1R0oNp3CE33lIvNmXp3vMubHoJ3m0FePL24y12U5VMUd/TS13CwvvvG8cPOXloOaiIiIiIiIytvhby26JkYhkKlhYA4Kvw4fI74UiurUNfC2eXuXAZuHBLLzZl8YRclAWcw4nOeXgKX6Kjax++2gBV1lDf00edp8wJAEf0EJSjUxAoIiIiIiJjq6iW97Q7LR3KVpRhvIbArEDucK6tQ1EtXrdhZsgw1OZUCF0b6+fvhywAxvUi/sofE2sbJFAboN1taKiqIfyBrygAPA5jFgQaY2YZY35jjHnNGLPZGFOfHS81xvzKGLMt+1gy4prVxpjtxpitxpirRoy/xRjzcvbYfcYYM1bzFhERERGRU2zFGmb3ZgDwzfAxu342Mz54sKJnrq3DijXgDVJVYHh5n3N+cb6P3xa8FwCP/xnS/VEy0Qz+Gj+AegOegLHMBKaAz1trzwOWA58xxpwPrAI2WGvPAjZkX5M99mHgAuBq4BvGmOFasd8EPgmclf25egznLSIiIiIip0C4OczKh1ay6KU7udvr5H48hR4KFhQ4+wGzcm0dFl0P77+P6pI82ged7N+Md3+Wv//8FwFIx/pI9Tt7Bj1FB8ubqDfg8RmzINBa226t3Zh9PgC8BtQAHwDuz552P3Bt9vkHgB9Za+PW2p3AduBiY0wVUGit/Z211gIPjLhGREREREQmoHBzmIZnG2iPtGOx9PQO4gq48Af9h5wXcAcObeuw6HqqL78x97L0og9RWloKQCgZIh1JAxzSWkK9AY/PadkTaIyZCywBfg/MtNa2gxMoAsN54Bpg74jLWrJjNdnnbxw/0vt80hjzgjHmhc7OzlP6GUREREREZHTCzWH+4el/IJaOEWuN0fKfLSS7k3gKPeR58qgKVWEwVIWqaLi04bC2DlVVVbnnZWVlFBcXA7C0YCmuISeEcec7iwYPCyLlTY15iwhjTD7wY+DvrLX9x9jOd6QD9hjjhw9a+23g2wDLli074jkiIiIiIjJ2hjOAGevs6eve0E3vU724Q258VT76E/08/ZGnj3mP6urq3PPS0lI8Hg9FRUWU23KunnE13+AbePI9VIWqqF9ar96Ax2lMg0BjjBcnAPy+tfYn2eF9xpgqa217dqnn/ux4CzBrxOW1QFt2vPYI4yIiIiIiMsE0bmwklo6RGkxhU5aBTQMApCNpPIWeUS3dHA4C8/LyCAScvYOlpaX09PRwzoxzAPjjp/6Iz+cbo08xtY1ldVAD/CfwmrX2ayMOPQLcnH1+M/CzEeMfNsb4jTHzcArAPJ9dMjpgjFmevefHRlwjIiIiIiITyHCRlr3/vpfX//51kt3J3DF/oW9USzeHl4OWlZXlxkpLS+nu7qarq4uCggIFgCdhLPcEXgZ8FHinMWZT9ue9wD3Au40x24B3Z19jrd0MrANeBX4JfMZam87e61PAd3CKxewAfjGG8xYRERERkRNU5C8icSBBZEsEm3B2aHkKnQWI7wq4RrV089XkqwB00snKh1YSbg4fEgSODA7l+I3ZclBr7dMceT8fwIqjXHMXcNcRxl8AFpy62YmIiIiIyKkWbg4zmBik7/dOY/hZn5pFaiBFZEuE/hf62VTqItwcPmYgGG4O0/h6Ixin+Et7pJ2GZxsI+oJ0d3dz4MABBYEnacwLw4iIiIiIyPTQuLGRlE3R/2I/wTOCFL21CCDX2y9S7KPh2QaAowaCjRsbids4niJPrgJoLB2jI9lBrDtGcXGxgsCTdFpaRIiIiIiIyNTXEenAZiyxvTHyzszLjfurnd6AniIPsXSMxo2Nx7wHQPWfV1PxvorceNwX50DXAV7e/TJxf3yMPsH0oCBQREREREROicpQJckDSWzS5gI/gMKlhVTeWJkLDIcDvaPdA6BwcSHBOcHcuDvkBgvR/VFei71GuDk8Rp9i6lMQKCIiIiIip0T90noy7U5/wEBNIDfu8rkoX1mOcTslQ47VJqJ+aT0Bd+Cw8eGloViweZbGJ74ITetO4eynDwWBIiIiIiJyStTNr+Ny7+UABKoCFPmK8Lq8h5wTcAeO2Saibn4dDZc2UBWqwoyoMxmcezAr6Mn30OECHr1FgeAJUBAoIiIiIiKnRtM6Mo//lJoCw+a44ekLbuHLl305F9BVhapouLThTdtE1M2vY/1162m6uYmqkNMzMDArQOiCEADGZ6hMpSEZhQ1rx/xjTTWqDioiIiIiIievaR08eguvtg1wfoUL+vbCo7dQ9/77qLtu/Qnftn5pPQ3PNhBLx5j1qVns+599lC8IUd/T65zQ13Jq5j+NKBMoIiIiIiInb8NaokNDbN6f4YKK7P69U5Cpyy0PTVu8ITfL/mwm/5QeoC4y5JxQVHuSE59+lAkUEREREZGTEm4Oc3dhhj3bioimBvj12yoJhxJOoHYKMnV18+uoG4w4ewCT0YMHvEFYseak7z/dKAgUEREREZETFm4Oc9vTt5F0u+h9tg9PqQd7XgG3Z2u61HlOUWP3Rdc7jxvWOoFlUa0TAA6Py6gpCBQRERERkRPWuLGRoX1DbL9jO5lohvL3lGNchiTQWFpC3UWnMFO36HoFfaeAgkARERERETlhHZEOIlsjZKIZZlw7g9J3lR485nEraJuAVBhGREREREROWGWoktjeGMZnqLimAk++Z8SxqnGcmRyNgkARERERETlu4eYwK39wOe2DbcT2xAjUBDCug83dvS7vMZvCy/jRclARERERETku4eYwDU/fTswmsUBsb4zCtxSAtWAMxf5iVl286k2bwsv4UBAoIiIiIiLH5Z7n7yFmk/Q+10vHgx2kI2kCs4NUpdKsH3DDZ58a7ynKMSgIFBERERGRUQs3h+mN95LsTdL2QBtYwEDeWXlOIZhT0BdQxpaCQBERERERGbXG5+4GoPNnndiE5cw7z8Sd78aT76EymXL698mEpiBQRERERERGp2kdHYleMglL73O9FF1chL/S7xyzlvr+IXjXveM7R3lTqg4qIiIiIiKjs2Etlak0/Rv7yUQzFF9RnDtUZKHuXfeqL+AkoCBQRERERERGp6+F+p5eIi/04S31EjonBEAgk2H127+iAHCS0HJQEREREREZnaJa6vr2UtASxTM7gMtAZTJFfdytdhCTiDKBIiIiIiIyOivWkHYH6DiQ4v/kJWjatZf1+3qou2LNeM9MjoMygSIiIiIiMjqLrmd3yz4S6Vs4u8wNRbNgxRotA51kFASKiIiIiMiove46C4Czb30MrrhinGcjJ0LLQUVEREREZFTCzWH+bt3fAXDb1tsIN4fHd0JyQhQEioiIiIjImwo3h2l4toHOPZ24gi66PF00PNugQHASUhAoIiIiIiJvqnFjI7F0jHhHHH+lH2MMsXSMxo2N4z01OU4KAkVERERE5E11RDqwGUtsVwx/rf+QcZlcFASKiIiIiMibKvIXEd0VJR1Jk39Bfm68MlQ5jrOSE6EgUEREREREjincHGYwMcjgy4NgyAWBXpeX+qX14zw7OV5qESEiIiIiIkcVbg7zD0+tJoNl8OVBgnODeAqcMCLPk0fd/LpxnqEcL2UCRURERETkiMLNYRqevp0MlqHmIYa2D1GwpCB3vD/RP46zkxOlTKCIiIiIiBxR48ZGopkEkc0R9v1kH+5CN2XvLssd137AyUlBoIiIiIiIHFFHpIOh14fY9dVdYKDmEzW4g24AApmM9gNOUloOKiIiIiIih2paB19fQGUySXRXFICzv3I2JZeVAOCyloYho/2Ak9SogkBjTNAYc85YT0ZERERERMZZ0zp49Bbo20t9Ty/JPVE8RR58M3yAkwH8p+4B6q5YM84TlRP1pkGgMeb9wCbgl9nXi40xj4zxvEREREREZDxsWAvJKE/uTvGTH3RRsCNCUa0fYy1VyZSTAXzXvbDo+vGeqZyg0ewJbAAuBp4AsNZuMsbMHbspiYiIiIjIuOlrAeCfX4fwpiQAcy8s5O7ObupubRvPmckpMprloClrbd+Yz0RERERERMZfUS3hUB5PDRwMFVJz8mioKCXcHB7HicmpMpog8BVjzI2A2xhzljHm34Bnx3heIiIiIiIyHlasobG0hFhnAuM1AATnB4kZQ+PGxnGenJwKowkC/xa4AIgDPwD6gL8bwzmJiIiIiMh4WXQ97S4XyQNJSleUcuZdZ+Kv9ANOywiZ/N50T6C1dgj4UvZHRERERESmuJJEKTZl8c/wE6gJ5MbVHH5qGE110F8ZY4pHvC4xxjw2prMSEREREZFxEW4O09PaA4B3hjc3HnAH1Bx+ihhNddBya23v8AtrbY8xZsbYTUlERERERMZDuDlMw7MN9LQ7QaCvwukNWOwvZtXFq9QcfooYzZ7AjDFm9vALY8wcwI7dlEREREREZDw0bmwklo6R6EyAAW+ZkwkMeoIKAKeQ0WQCvwQ8bYz5bfb124BPjt2URERERERkPLRH2gGIvBbBN9OHy+PkjFQQZmoZTWGYXxpjlgLLAQN81lp7YMxnJiIiIiJyCoWbwzRubKQj0kFlqJL6pfXKbo0w3AMw8nqEodeHqLqpKndMBWGmlqMGgcaYc621W7IBIEBb9nG2MWa2tXbj2E9PREREROTkDe91i6VjgJPxani2AUCBYNZwD8Cux7pwF7gpeVtJ7pgKwkwtx8oEfg5n2ee/HOGYBd45JjMSERERETkFRmb+jDGk02kO/O8BSt5WgqfQQywdo3Fjo4LArI5IBzZjGXxtkKKLinD5D5YP0e9oajlqEGit/aQxxgXcZq195jTOSURERETkpNz53J08uPXB3GtrLdHmKPse2ofL76Ls3WWA9rqNVOktZOfufWSGMuSdmZcbrwpVHeMqmYyOWR3UWpsBvnqa5iIiIiIictLCT9zOg1t+BEB6KE1ifwKAoR1DACS6Erlztdctq2kd9R17SW2LAJB3hhMEBoxXS0GnoNG0iFhvjPmQMcYcz42NMd81xuw3xrwyYqzBGNNqjNmU/XnviGOrjTHbjTFbjTFXjRh/izHm5eyx+453HiIiIiIyjTSto3HHj0nHM/Q808Nrn36N17/4OjZjc0Fg8kASUPPzQ2xYS11/L2dv7sWT58I/00tVMkXDQEJLQaeg0bSI+BwQAlLGmBhOhVBrrS18k+v+C/h34IE3jH/dWntIdtEYcz7wYeACoBr4tTHmbGttGvgmzt7E54D/Ba4GfjGKeYuIiIjIdLNhLR0lLvb+624GXxl0xiwku5NEd0QBSBxwMoEfOPMDCnCG9bUAsHtHnHdXu/jfPS3ZA8q/TEVvmgm01hZYa13WWp+1tjD7+s0CQKy1TwLdo5zHB4AfWWvj1tqdwHbgYmNMFVBorf2dtdbiBJTXjvKeIiIiIhNH0zr4+gJoKHYem9aN94ympr4WZsRSRLZEKH1HKXP/fi4Aka0Rkl1JjMfkMoEPbn2QlQ+tzLVGmM7CFbVc7itna1eG5mWlhEPZPYFFteM7MRkTRw0CjTFnGWN+Zox5xRjzA2NMzSl6z78xxjRll4sO152tAfaOOKclO1aTff7G8aPN+ZPGmBeMMS90dnaeoumKiIiInLhwc5iVP7icRRvX8u78FOFQEPr2wqO3KBAcC0W1XPv6AWzKkndOHr6ZPgD6ftcHQP7CfNKDadKxNHCwVcR0DgTDzWEaCnzs2Owsl7VLi2koLyVcWAwr1ozv5GRMHCsT+F3g58CHgJeAfzsF7/dN4AxgMdDOwfYTR8oz22OMH5G19tvW2mXW2mUVFRUnOVURERGRkzPcm6492cfgtiF+u6qZv+n0s3DuLFbOLCH81NrxnuKUE17yJ3yr1wn88mYH8BZ7MF7D4OZBjMdQ+BZnQVt0R5R01AkEh1tFTFeNGxuJ2SQDfxzAX+3HN8NHzOWisXIWLLp+vKcnY+BYQWCBtfY/rLVbrbX3AnNP9s2stfustels1dH/AC7OHmoBZo04tRanOX1L9vkbx0VEREQmrHBzmJUPrWTVU6uIpWMke5Ls/fe9JLuTtP+wA2uh3euhIc9O6wzUqRZuDtPQ8ks6WhIYn8Fb6SdoDBUVXrAQmB3AX+kHYNe9u2j51sEFZ9O5VURHpAObtgxtHSJ/Yf7B8WT/OM5KxtKxgsCAMWaJMWapMWYpEHzD6+OW3eM37E+A4cqhjwAfNsb4jTHzgLOA56217cCAMWZ5tirox4Cfnch7i4iIiJwOw9m/toE2bMaSSWXY83/3kIlnqPhABfGWOP1/cL5cx1yuaZ2BOtUaNzYSS8eI7o4SnB3EuAwxl8HOcDKDFfMCeMsO1kUcah7KPZ/OrSIqQ5UkOhPYlCVQGzhkXKamY1UHbQe+NuJ1x4jXFnjnsW5sjPkhcCVQboxpAe4ArjTGLM5evwv4KwBr7WZjzDrgVSAFfCZbGRTgUziVRoM4VUFVGVREREQmrHuev4dYOsaef98DachfkE90e5RZn5lF4VsKORA+QHRXlKK3FgHTOwN1SjWto2OwDQvE9sYovqQ4d8hW+WHTIF8pjhGK93FNdtxT6HwVDmQy06pVRLg5TOPGRjoiHVSGKnlb7dtofqYZAH+NkylV+4yp7ahBoLX2HSdzY2vtR44w/J/HOP8u4K4jjL8ALDiZuYiIiIicDuHmML3xXoa2DzGwcQDjNeACb4WXooucoM9b5iXZlcxdo2zLKdC0Dh69hZkVxeyNQCaawV/rzx2urPHT54LLZrmZFY3yo4YZfP7xBB0bB6hKpqiPu6dNq4jhTHUsHQOcwjgPbn2Qkl6nXmOgKkBVqIr6pfXT5ncyHY2mT6CIiIiIHEO4Oczdv7+bvoRTgbLzUadKuU1aBpoGKLqoCJstbect9ZLodvrUKdty8sLNYRpfWMueQBHbP7uNkmyGNVDjLGsMZDKsOTPNhbfkM6vI2Ql1g43xuivOmsE0P2/txvcnp6L+4eQwvGR2oGmA/T/bz4wPzKBgUQHbtm7DW+qlprxGAeA08KZ9AkVERETk6MLNYW57+jZ6BnoAsBnL4GuDFFxY4JyQhrz5Qc5vX0xm30cIVRSQ7EpSFaqi4dIGfdk+CXc+dyernlpFu9tw4FddpIcydD3VC0CgykdVMkVDzyDvj0aZnQ0AcXkhWEplvvN6/6UN06oC5vDy497f9RLdEWX313eT2J8g3h7HX+1Xy4xpQkGgiIiIyElo3NjIwK4BXv3rV9l22zYGXxnEJiwFSwpwF7gBqJgb4P74Y9x/Xhmfesdfke5NE/5AWAHgSQg3h3lw64MAJHuT9Pw2G4SnLL4CN692tfNwl6XuXfdC0SzAOI/XfgO+uJPKTzrXdpRcNF4fYVwMLz8e2j6Ev9oPFiJbI8TbnCAQ1DJjOjhWs/gPHWXcZ4y5feymJCIiIjJ5dEQ6GHx5ECwkOhK0/6AdcJYj5s3Pw3gM/5gfJUici3b8G7Nnz8ZaS2tr6zjPfHIbDlIS+xM0r23Gpm2u2I63xs+Q9bEm8iEeTl8Gn30FGnqdx2zWr7LSCYY6OkYU5mlaB19fAA3FzmPTutP5kU6L+vK3kuxJkOxMUnJFCa6gi56nerCJQyuDqmDR1HasTOAnjTG/yLZsAMAY8x6gCSgb85mJiIiITHRN66hMZ4hsjeCv8ZN3dh6JDme/n7/az4z3lfO3f1LAtfGoc35fC7NTThXGPf+4YMoGGqfDcJDS9asuUv0p5n9pPiVvd4qbFFf5WZX8BA8lLuXex7Ye8frDgsBscRn69gLWeXz0lkn732e4V+Wi+xex8qGVzvLOpnXUPfMfLHm5G4C8s/IIzg8y9PoQGMhfdLBHoAoWTW3Hqg56lTHmI8CvjTE/wKnQWQHcYK394+maoIiIiMhEcUhpfW8h9R17+ZtEnA2vD1F8WTGeEg+RVyN4ij0E8lzcWxSl7vwRNwiWMPu1bwGwuzd9MNCAabUv7VSo9BbSFu+l74U+8hflE5wbJBPP4C33kqq6mkcylwPQ1hs94vUzZswARgSBG9ayrWOQK743xOM353F+hRuSUdiwdtL9tzlSBdBVT63inoxllc9Q81o/Xg/Mq/YwMC9IZHOEvLPz8BZ7AQgYrwoWTXFvtidwHfBD4LPARcBfKAAUERGR6Wj4i3V7pB2LpT3ZR0NJPs1tKTLxDOVnBsk/LwRAfrWfL3d2URc52IwcbxCAWXlOpnBPX7ZcaDIKv/jiaf0sk17TOuo79pLeHiHVk6JombMM1OVzMe8fbiYw66bcqdXFwSPe4tetv8ab7+Xrv/m6kylLdfPj11Lsi1ie3pM+eGJfy5h+lLEwXAHUZiyRrRHSEefz9LoMDeWlhNssF1e5+fW+Dr5bEAFg1tICjLVOMZ2BhParTnHH2hN4OfASztLPWcDfAI8aY9YaY/xHu05ERERkKhr+Yh3fF899qY65XPxHn5M9+WWgj63mABV5hk+VJg8NAItmwfvvg2gPQa/hzFIX//XHJH2xbCAY7Z60yw7HxYa11PX3ctZv92M8hsIL86lKpvj7/XHi+67NnRb0urn1qnMOu3w4oHcVuUj1pZxMWUUpX25xCvn8om/EV+Si2rH+NKfc8FLZ/o397Lx7J1vqtxDd6WREh1KwtTXFpbOcz3rVGR7+9So/L84domnXXta3tFHXOfkCXzk+x8oE/ivwCWvtp6y1Pdbah4ElgB9QNlBERESmlY5IB9Zamu9qZt+P9+XGD/Q7AWFNocHjMrz8qRBr3j7i38uLZh0sSJINKL73gQC7ejN8YX3s4Hkb1p6WzzEVhFPdvKuyiv/9Y5wZF+bzz5Fe1re08WdD+6kpDmKAmuIgd39wIdcuqTns+uGA3lPsIXHAycymYxmGtjuB+2/6XIRDeU72dsWa0/nRTonh/XyR1yIYn8FmLP0b+wGI7Y5hU5b1iysIh/Lwug31y/2EfObgDSZh4CvH51jN4i+21mZGDlhrh4AvGmP+a0xnJSIiIjLBVIYq2dO6h3R/muiug/vMvN1JKvIMPrfzJXpm/oh/Y39jELFiDfzkL7l8tocPnufh1ztTB49NwmWH4yHcHKahopT9GwdID6YJXFFKQ7mzDLfOU8Yzn33nm95jOFOWf0E++9btI9YWI7E/AWnwFHuIdiRoLCujbtmaSbcfEJwKoA2DPyWyNULeWXmH/M0OB7rxcwpoKHCDy0PdQP/Biydp4CvH51iZwDOMMT8zxrxijPmhMSb3zyjW2tdOw9xEREREJoz6pfXY/c7yzViLs98qYLxUd6eoKcz2oFv28UN70r3/vkODiEXXQ7AUgKWVbnb1Wrqj2SWhyr6MSuPGRmLG0Pd8H+5CN/kL84m5XDSWlow6eBnOlJVcXoJxG3p+20Nsd8z5z/bWIhKdCVozlpWvf2fyNU3PVgD9/O5O4i1xQmeHCMwNEN0VxVrL0PYhfDN8eIo8xFyGxqo5x/6blSnpWJnA7wIPAE8C1wD/BnzwdExKREREZCI4WA20ncq05ZxtB3gdsAlL8WAxq+tWc/u/3k7NW6rhsz8f3U3f8xV49BaWVjlZwJWpYhJzC6n0FVPfrAbyb2Y4izfUPETo7BAmm4Ht8LhHHbzUL62n4ckvEiv0ULCkgN5newmdE8JX4SM4J+j0fNyfoN3bTsOzDQDj/t8l3Bym8bm76Uj0UplKUx93U3fFETKVG9ZCMkpoaxKAz85IcX+en96nekkeSBJ5PULBooLc6R3Jfme5skwrx8oEFlhr/8Nau9Vaey8w9zTNSURERGTcHVoNFNrdht/3HDz++ac3UvezL9K6u5nq6urR33jR9fD++3h88SwAWluTWGOcaqPPNky+zNNpVhmqJDWYItmZJDg3OGK8atT3qJtfR0NnN1XJFAWL8kkPpBl8eZDArAC+Sh9Art9jLB3LNaYfL+HmMA1P3057so8M0GpcrArBwo1rWfmDyw/9m8kuK36pI4MB/rY0yQPBQQB6nu4hPZAmdG4od7r6AU5PxwoCA8aYJcaYpcaYpUDwDa9FREREpqzh4iGpgRQt/9lC16+7GNwbw1/lAwN/Fw+xsMCyv7uPSPLIDcmPJpwf4uflXrylXroe76LlP1uw1k6IgGOie1vt25ylm0BwnhMEBtyB4+5rV+cpZX1LG2vKnGAvE8/gr/Hjn+kU9UnsT+TOHc4+joumdTQ+8UViNkl8X5zXv/A6zV9uBmPgSP94kF1W/Mr+NPNLDCGfoXV+Pq6gi65fdgHkgkCvS/0Ap6tjBYEdwNeAf8n+jHz91bGfmoiIiMj4Gf7iP/DSAL1P9dL+/9oZ2jqEvzqAr8JH1+tREr3Oks5n2H5cGbzhQC/vzDySnUl6n+rNBTbjGnBMcOHmMD/b/rNcu4PAnAAAHzjzA8e/XHPFGsKFxXz73ArcBU67hMCsAK48F66Ai2R3MnfquGXLmtbBo7fQ4QJrLS3fbiHZlSS2O0aiK0H3b7oP/8eDFWvAG+SV/RkWzHA+1zcqSih/TzmZeAZvuRdfhZPtzPPkjfsyVxkfRw0CrbVXWmvfcZSfNy+7JCIiIjKJDX/xj+2NYXyG6j93lnwG5gQoubKEyGsR9vz7HufkEi93//7uUd97ONCrvrmaMxrOAAP9LzoVGiu9hafwU0xe4eYwKx9ayaL7FznN3LP7M2PpGNFdUbwVXjz5TnmLJ1uePP43WHQ9jZWziLvdhM52MmOBWQGMMXhLvCR7nCDwRLKMp8yGtdjEEDsbdtDyrRaiO6KELnDmuqNhB233t+UC4uG/qXB+iHfVzue17gxNZxQRLplBh8dN+VXleEo8h+wH7E/0H/6eMi0cq1n8RcaYyhGvP5atFnqfMab09ExPREREZHzUL60n4A4Qa4kRqA1QemUpZ951JuVXl1P+nnLKVpblsneeEg99ib5RZwOHA0x3yE1wbpDQOSGnj5u11HfsnfaN4+987k5WPbUqux/T0h5pp+HJLzqvM9ZpfXBmXu78E82ediSdIKjo4iKC84L4ZjgZMk+ph2R3kqpQFQ2XNoxftqyvhe/G/QzujtH3+z4AKq9z/nbSA05/Spt0qstWhipz+1h37dkPGUjNyaOhrJgiTx4uv4uz7jqLyo8czGpqP+D0dazloP8fkAAwxrwNuAenWmgf8O2xn5qIiIjI+KmbX8cdl9xBoiVBoNZZdhioCeDyuTDGMOPaGblzvcVegFHv5xsOMIcVLCkg3hrnPXt6qOvvndaN48PNYR7c+iAAA00DtP5XK5lEhpgxuKxlaNsQ6YE0hUsOZkxPNJgZvq7orUWccccZGJdTabSooojSeCnrr1s/rsslwxW1NHQ6gSkG8s7OIzgviLvQnTsnHUnnspXDmdJ4SxwAf62fWDqGdfsIuAO489y4vM7X/3HNcMq4O1YQ6LbWdmef3wB821r7Y2vt7cCZYz81ERERkfETbg5z7+P3khxIUjy3GK/Le8hxd56bimsq8JZ6c3vKRpuRqptfR8OlDVQlUxhrmVnqXH9Di5Ptma6N48PNYf7hqdUA9P2hj93/upueJ3po/0E7ABlgaGMfxm3IX5gPQMCceHGTNwbj4ARHV1xwBe3tbaS+egE0FMPXF5z27Gy4OUxDgY/BrUO4893M+dwcqm92liQHZx+sipqOpHN7Iof//mItMYzb5Irc9Cf6nb+3UBUGM/4ZThl3x+oT6DbGeKy1KWAF8MlRXiciIiIyqQ0vq+vc2gmArbJYayn2F9MXdwI1i2XmB2cy409mYIyTQTqejFTd/DrqfvZF6NvLM0MpLge6hhvHG5cTfBTVOoU+pkHz7uE2CBksmUSG9h+0E5wTJO/MPLp+1UXZijLmznTT9UIfpefm4Qm4qEymqI/bEw5mhq9zekF2UBmqpH5pPS1bH+bBjOW1HbuZV+Iiv28vPHqLc9Fp+m/RuLGRmHX6+oXOCVGw8OBevsCsAIOvOG0f0pF0bk9kZaiS9kg78dY4viofxnPw77Jufp2CPsk5Vibwh8BvjTE/A6LAUwDGmDNxloSKiIiITEnDy+qG9/wFZgVI2RRBT5Cmm5u4+4q7cxmk4QDwhJbXZSs5lgade3QPB4E2DVgYDj6mwR7Be56/h5hN0nZ/G9tv306qJ8XMP51J+XvKARjcPEDn3jjtXWlmvKWAuzu7WN/SRl3nyWVN6+bXsf669TTd3JRb/tl+YD0Ai/8rylkbDOFQHiSjp3WZbkekg2R3kmRnkryz8w45VrqilKo/qwLjBIHDGcA37mMFLfuUIztWddC7gM8D/wVcbq21I67527GfmoiIiMj4GP5SHXk9gr/Kn6tCOTyeW855ssvrso3jS2c6vd26o5BMW977/SGe2u20nzjdwcd4CDeH6Y33Mrh5kO7fdIOB4suKCZ0XwlvqxTfTR+S1CC3PDzjZrbeW0FBe6gRn2b54p3Iu/1Pp/PfOxDL0tMQPvtdpXKZbGapkaPsQAHlnHRoE+sp9lL2rDHeem/RQOpeBrptfx98v/HuSXUkCtQEt+5SjOuayTmvtc0cYe33spiMiIiIyvsLNYQyQyThFSIreWpQ7NnK55ylbXrfoekrOvRbu8NMdzbC7z/KL7SmKA3DFnOxXtSm+R7BxYyPWWjp+1IG3wsuZa8/E5cvmKqwldF6Ivuf6iO6Okb8oH3fITQxoLC2h7qI1p3wumXJf7nVif4IohsaSYuo8Zaf0vY7lstKPsnH75zFeQ2C2k9XzGA/GGJIZp32FO+SGIXKZvnBzmHt/fi8AVWdUUb+0XgGgHNGxloOKiIiITCvD+9KSsRT9G/vJRDOEzsn2kDuJAiRvxufzUVBQQFc6xN6+DAD/05xhYW0tK2urCVec2mzXRNMRaSfaHCW2N0bF+yoOCQCLM5b88/PJxDKkBlKUvetgINbhcZ/yPXodkQ7cITfuAjeeYg82ZUl2JZ33WnFqA86jefilVn70mwoGtwQIzC7AuF3YZDHX1n6OL1/25VwGOlAQYJ53HnXz63L7WFu3twIwVD5Ew7MNo25bItOLCryIiIiIZA0X4+j4YQc9v+0BnLL8LmtpGEyMaValtLSU7qI5PJp8BRgiNZRhcPsQ7efl0+DzQnN46mR1mtYx9Is1BKId9GRCzJxdyMZnejE+Q9HFBzOvRRb63C7yF+VTcmUJJZeXHNIfsDJUdcqnNlxc5aw7zyLWGmPXP+8ivi/OnOqK01YU5t7HtjIUjRFvbafwLdcwuOUvAFh/IMgd73xn7u9g5fdX0t/v9DpsfO5uZx9rSwxXwIW3zEssHaNxY+PU+buRU0aZQBEREZGs4T1/0V1R/DV+aj5eg6/Mh4WTLkDyZkpLS+m2hTySdFofGI9h4I8DAMRsctQ9CCe68BO3s/IPDSyf6eHq2iqeK8jwNwd66Pt9L4VLC3EHnXYZAWtZ/favUBmqwh1wU/PnNYcEgMCYZGaHi6t4ijz4q50WC/H2ND37r+Hhl1pP+fsdSVtvlMS+HZBO4as+55DxkUpLS+nu7oamdXQkesnEM/T/oZ+8M/NyPQ9H27ZEphcFgSIiIiJZlaFKbMYSb4+Tf0E+JVeUOOOp9CkvQPJGw1/oew4M4i5wk3dWHpHNkdzxqfBlPtwcpmHXT2lzu0j0pti6eYi/ec3FrvYU6UiG2QtDGGupSqZo6Oymbn7dEXv5Adxwzg1jkuEaLvpT5J2Bu9CDy+8m2lxLZ8cFrP7Jy6clEKwuDhJv2wKAv/rcQ8ZHKikpoaenBzaspTKVpvvxblJ9KSreX5E753jalsj0oeWgIiIiIln1S+tZ/fAXsQmbywIFMhnq+4fgXfeO6XuXlpbS1NSEyTN4S73kX5DPvof2kepL4SnyUOgrHNP3HxNN65zKpn0tUFTLPRV5xIxh/0/20flIZ+60f1rq9MB7ONjP/F1O/zuKZgFH7+U3lksc6+bX8U/rgkR6o3iK64l3OIVYosk09z62lWuX1IzZewPcetU5fOyBrbgLK/AUOHsgg143t151ziHnDQeBP0966I8X0hnuJH9B/sF9rJmM2kPIESkIFBEREcmqm1/HiyUv8jJ3EKjyUZVMUR93U/eue8d8P1hZWRnd3d3k23ySZUlC5ztf5AdfHaT4kmKGUkOEJ8G+wHBzOBuwtVOZSvM2d4Qna6to9wCpIVKDaQ788gAFFxZQdlUZu/9lN/tfGsCd7+avFtTwd7191CXsIUVYxqPR+fDSS0/RTJJdew8bP1EPv9TKvY9tpa03SnVxkFuvOufQoLJpHdc+sZai1ld556wQc1xP80Lhuw8/D+cfDtLpNGsKimn9dTfpwTQzPjgjW1Anw6qoa8L/vcj40HJQERERkaxwc5jvP/F9AGadOYf6d36Vus+8MvYFQZrWUbr9Ibq6OmnduxdPiYfg3CDukJvBV5zMWDIz8fcFDleobOtrI9GbpN3j5v4BL23GDcaAMXRv6MYmLDNvmEn++fkEzwyCheC8IB0+Lw0VZYQv+8vTVoTlaIaXXhpfkEwydtj4iXj4pVZW/+RlWnujWKC1N3roEtOmdfDoLbTs3U1Lv+UdtUkaQ9/jmfceOGL2sWRwOwCRwQxdj3VRsKSAvPl5YAxBC3VXnJ5qpjL5KAgUERER4WAA07mrE3eBmy531+kpsZ/94t/hj5PJQCqawVvmxbgM+YvynVYVCadtREekfWzncpIaNzYSS8fo/EUnr9/6Ot2Pd7P9th1su20bic4ENm3p/k03+YvyCVQ7+/zyL3AK4QTnOcFVzBgaD/x+3D7DsFuvOoeg143x+rGpBHDkJZnH497HthJNpkl2tdDyrY+T6tuXW2IKOEtnk1Ge3J0G4JJZHkhGnfE3alpHyes/AmDglQHSkTTFlxTnDo9F+wyZOhQEioiIiHAwgIm3xfFXOfsBh0vsj6nsF//nKkK5IV+F06y85LISMtFMrkpoZSrtBI0T1HCQ2v9CPzZpaXugDU+ph0Rngu7fdDPwxwFSfSlK31Gau6ZwSSG4IHReaMR9xr8IzrVLarj7gwspCOVhk3FqioPc/cGFJ7UfcHgpaXTni6T79hFve/2QcfqcZacPbk5SXWB4S1X2q3rfESrTblhLqc/Zq9j3+z4w5JYQw9i0z5CpQ0GgiIiICE7gYTOW6J4owTnBQ8bHVPYLfvKsfAKzApS/r5zCpU4RmND5ITzFHnqf7XWKfHT3HDkrNBE0raMylSbRlSC2O4an2Ck9MfODMwmdG6J/Yz/dT3TjKfZQsMgpBIO1BGYFOPe+c8k/Lz93q4lS0fLaJTV8/MpzMekET3/xHSddEGZ4KWm83Qn+Un37Do43rQMMPVHLL7aluOECL+5sm4cjVqbta2F3bQjjMQxtHSI4L4gn3/mde11eFYSRY1JhGBEREZl2DhYvOVhtsjJUyc7Xd2ITlsDcgy0JxjwgKaqFvr3MneHB/+UzDzlkXIb880PEtkRoONBNXWQIOFiY5E2LjJxOG9ZSn+rhL192Apc5fzeHeEecoouLyMQytP+/dhIdCWZeNxPjguJ0mqsGh/hZaQWx/IN77gLuwIQKYILBIJlMhmQyic/nO6l7vePcCr7/3B4S7dsAJwjMLTHdcAtgeXhLkmQGPrLAm73KHFIkJ6eolu8XpKn+cx+t32klf+HBIDrPk6eCMHJMygSKiIjItDK896890o7F0h5pp+HZBt4WqCa10wmwgnOdjM1pCUhWrAFvkPoeJ9s3UiCT4e3uJO6BVDYABIIl8PUF2IZiLnr4bbyl/1dHLjJyKjWtg68vgIZi5/FIS1L7WqiLDJHcEsFb6iUwJ0Dx8mKMy1CwxMn8BWYFKLu6zClckrHcls6n4dIGqkJVGAxVoSoaLm2YUAFMMOj8LQwNDZ3UfR5+qZUfv9hKKjZIqqcNgHTffj70lhoncM9mhH/XkqY0aFhWPfw13R55b9+KNXR43JRcXsK81fMov7o8d6g/0X9Sc5WpT5lAERERmVaG9/71/aGPjnUdnPVPZxEjxpP7nmfhaz3s9RsClT6qUmnq51w99gFJ9gt+3Ya1cKCbxrJSOtyGSm8R9R17ecmT5mdJiKcsfp8PEoMQ7cYANeYAS179VzKe3/DzM28fmz522cI1JEfsW3v0lkPmDuQymoHdEVJz8jDG5A75ynzU/lUteWfk4fI4wU2Hxw0r1oxL+4fjkZeXB0A0GqW4uPiE7zNcFGY4C+gKFpLs28dvtmT7JWZ/f3/cl+bCma6Dv79sv8TDLLqeylfuoz3Zl+sLOGyiLKeViUuZQBEREZlWhvf4DfxxgGRnkmS3U1yjw+2ia0+cS2a6eHlPC+v3tlL30k9Pz6QWXQ+ffYW6W9tY/xev0HTzy6y/8Wnq3nUvpaVOEZUebxVxd4h4PM4dv4nx6+YU1lr+8Yk4Tzz5B65xPQ2cfB+7Nwo/tZaVM0s421vBUnc5V8yuYWFNORduXMvC+xey8qGVTgXVFWuIEqCtM817yjJUJVMYa3HhBDPFlxTjm3FwOWWlr3hSVK88VZnA4f8uif07nfue+VbS/ftp7cned8Ua0u4AL+/LsLjS7Yx5g0deCppVv3w1AXfgkLGJtpxWJiYFgSIiIjKtDGdJYnucfWjJLicIrEyleb0rw4KKEV+PjlSV8XRadD2l130NgP9Z8C28iT6+9rsEa59M8O7/HuKrzybY3WfZ3Jnhr1I/BE6uj90bhZvDNORZmluTbF/bzMv/uofutCEVSZMxhnQ0TXuknduevo0rXv03Ftp8MhbmVnlZP+Cmaeka/umKu48cqCxffcrmOZZGZgJP1MMvteLKZvZSve24Avn4K8/EphKkIj1cds/j/GFXD9t6PURTcOFMFwRL4f33HTNQrptfN+GX08rEpOWgIiIiMi0MF4Npj7STSWWIt8YBSHYnCVjL3xzo4dohy8z8g8sYj1iV8TQbzgR++1dNXBos4stP9vGBczxs787wL79L5M57X9qSf/Y9rJz/yVP23o0bG4m5XOz91l6M25DqTvH6F1/HJi2zPjWLXf+yi/m3zSdvfh698V6ie53A+qEFFeRd8mFuW3Q9dSPuNbIQz2QJVE42E3jbwy/z/ef2kLEWgFRvB57iKjxFMwBI9+3jLYWvsuDF7/DI7ggAF1a6ITW6oHOiL6eViUmZQBEREZnyRhaDAYi3xrFp50u5u89Nw9w/4a19aSwwI5T9evQmS/FOl+EgcH9nF1/auZhoCta83c875rrZF7G58yLboxhvLz9vu++UNbjviHSQGkyR6EhQ8f4K/NV+Uj0p0oNp2v67DTLQ+0wvAPF9cQabBnEFXPgqfDy49cHcPOrm17H+uvU03dzE+uvWT6qg5WQygQ+/1Mr3n9uDBXo2fJuO//eFbBBYiafYyUgne9r4e886gibBpo40XhecX+E6epN4kVNAQaCIiIhMecPFYHqf7eXALw7kmq9jYGh/BGYvZ/+yLwJQkedyinG8yVK806WsrAyAQleM3+3z43YZzq9wccWc7IIuF/ir/QxtczJVp7LBfWWoMrdsNjA7wOz62cxbPQ+X30Wiw8lC9v2hj0wqw857dtL/Yj+BOQFMtr/dqZrHeDqZTOC9j23FApGtzzDw4qMk2raS6m3HU1KFp6Qa4wsSb3udanMAgN/uTrO0yo3Pnc1Gj/dyZJmyFASKiIjIlNcR6cCmLG0PtNHxYAf7f7IfV9BFYHaAWFecxo2NdJYsBWDG3/wvfPaVCREAwsFM4OWzg6S7duMqm8ODvJvLZjnFQ3wzfITODzG0Y4hMwmkxcaoa3NcvrSfdmgYgOCuIf6af0Dkh8hc4PekKlhSQ7k/T+XAnqZ4UM6+byezPzM5df6rmMZ5OJhPY2hvFZtL0/Oa7ucwfgLe4EuNy468+l3jra/SST2/M8vvWNCvPcB+8wQRYjixTk4JAERERmfKK/EUM7RwiE8sw809nMuszs5i3eh6+ch/J7iQdkQ72798PwIwZM8Z5tocqLCzE5Xbz65d2ENu3E3/FXFa4NlFT6CJU7SM4J0jBggJswjL0upOtqvQWnrL3j++N4y504yl0E8xkKE6nKb68GG+5l+qbq/GUeuj8eSfGayhdUYqn8GDJianQquBEM4EPv9SKAaI7N5Lu20fx2/8cT9FMgFxA6K85l1TnTjLxIX6zM0XGwrvnZ39/bt+EWI4sU5OCQBEREZnSws1hBhODRDZHwEDJ20souqiI4Owg3lIvye4kM1Np9r/0GAAVFRXjPOND/WxTG8YforujhXR/J56KuVSbLgC+9fEi5n20ktB5IYzHMPDKAIFMhvqOvUdu6H4chvdR9u/uJzArAMZggVVdPewu7uXKu8/AW+xl5oecwCZ/YT7u4MEs1lRpVTAcBB5vJnB4KejgS/+LO1RC3lnLCcxbAoCnuAoAf815WGt5qTXBT7ekyPfB8trs79CXP2Gy0TL1KAgUERGRKa1xYyMpm2Lw1UGCc4N48g9mqrylXjKxDJ9oPUDn8/+Dy2Vyyy8ninsf24oJFBDf+woAvoo5tFlnn+CfueLcOdRHjStD6Ow8Yk0DNBzopq6/96SLijRubCQajxJvjROc5QRCMZeLxtISAOp7eglkMhRfUkz5e8upeF8FWAvWUpVMTZlWBSe6HLS1N0rywF6iO/5A/uKrMW4PhcuupejSj+AuKAfAX30OXhfc8NAQ/92U5M8WevEO7weM9pzSzyEykoJAERERmdI6Ih1kUhmGdgwROi908IC1zCh2vgqd0xZhf3+C8jwXLtfE+nrU1hvFFcgnPehk/7wz5vHPqesZsk7j9brIEOtb2vj7GSkibQmu7HHaDJxsUZGOSAeRbRFsyhI882DvwQ6PG4pmUReJ0tDVS3U6TdWfzuSMWT7u6ezi5V17WT/gnhIBIJzYctDhpaB9z/8E4/FTsPR9AHjLaim+4iZMtmegyx/i/o9U8s55Hu5e4ef/1o3op6j9gDKG1CdQREREprTKUCU7X98JaQjUHvySXZVK8y+mj0XAq50Z9g9ZZuTZo99onNyc/zxP5e3hceCsci9XF23hkczllHp9NIR+DH17ATin3Aled/RkWDTTfdJBRKW3kLY/tmE8hvwL8g+Oh6rg5vUA1DWto+7RW5x2BsMmSGuNUyUQcP5mjicT+I+PbiY11Efk1d+Qv+gq3HlFh51jAAv8bvaf88AZ3yHPHOz5GMVPcAr9DmXimVj/1CUiIiJyitUvrcfuc4I7f7UfgIC11Pf0ck65C48LXtmfpjNimVEUPNatTr+mddxmv0WRJwnA+8803OP9Dtf5nmVx3SedKqYf/A/wBjmr1Plat60rc1KBWLg5zMofXE57opeBTQOEzg3l9voFjPfQfX6LrndaaRTNAsyEaq1xqhhjCAaDo84EPvxSKz1DSQZf3gDpFAVLD82IGqCmOMhNy2fjdRkeyVzOquQnaMmUk7GGVlvOK0u/PKV+hzLxKBMoIiIiU1rd/Doe9DzIDnbgr/RTFaqivvyt1LX+B7gN55a7+HWXYXPCjbfKw8qHVlK/tH5iLGfcsBZPOsYf9zltGt5zloc8k2Bt3o/JW3KXc042WDgzfAewhW1DBScciA0Xg4mlYwy+GiHRkaBsRamzzy+Vpj5uD/+9LLp+ygcseXl5o84E3vvYVqy1DDY9hr/mfHzlB1tm1BQHeWbVO3Ovl80ppeGRzTwSvZxHEpdTkufljvdfwLVLak75ZxAZSUGgiIiITG1N67C/fYRZhYbNQwYu+YQTtJQuhA1rMbU72bQ7SXooTaDQTXuknYZnGwDGPxDM7uv767f4+Ptfx7litpORy4u+of/eouspWHQ9lfdVsW3me084KLvn+XuIpWMkOhPs+b978Nf4Kb68hKpUmvUtbTh5rOnneDKBbb1REvt2kOpupeg9Hzrk2K1XnXPI62uX1Cjgk3Gh5aAiIiIydTWtg0dv4bW2Ac4tdzn75x69xRlfdD3hD3yF/fMLSHYlyUQzuR53sXSMxo2N4zx5cvv6br3MT3pNAX6POWR8pHBzmFhJjB89/SNWPrSScHP4uN4q3BymN94LwP5H9mOTljl/Nwd30O0UgznK+04Hx5MJrC4OEtv1EgDB+RflxouDXgV8MmEoCBQREZGpa8NaMokhthzIcF55NpBJRnPtExo3NuKv9edOL7roYAGPjsgbsm3jYcUaZ38f4MpWlDzSfr/hZZyUQ6IjkctmHk8gOBz0JroS9D3bR8nbSvBVOBVIK1PpKVfw5XiMNhP48EutROIpojtfwlsxF3e+004j6HXTcM0FYz1NkVEbsyDQGPNdY8x+Y8wrI8ZKjTG/MsZsyz6WjDi22hiz3Riz1Rhz1YjxtxhjXs4eu88M19QVEREReTN9LbT2WyJJOK/Cdcg4OIFe3vw8PKUeaj9Zi7/qYEBYGao83bM93CgLrzRubCSWjuGf6SfVnyIdTR93NnM46O15sgebsZS/x+llh7XUx91TruDL8RhNJvDhl1pZ/ZOX6e4fIN76KsF5SwEoyfNy9wcXKgsoE8pY7gn8L+DfgQdGjK0CNlhr7zHGrMq+/qIx5nzgw8AFQDXwa2PM2dbaNPBN4JPAc8D/AlcDvxjDeYuIiMhUUVTLazt2AjjLQUeMgxPotdPOuV8797BLD6mCOZ5GUXhlOIDzlnsBSPYknWWco8hmhpvD3P37u7E4FVQHNg6Qd1YevnInC1jkL6buz58+mU8w6Y0mE3jvY1uJJtPEW7dAOkVgzoUA5Pk8CgBlwhmzTKC19kmg+w3DHwDuzz6/H7h2xPiPrLVxa+1OYDtwsTGmCii01v7OWmtxAsprERERERmNFWvY0uMsAz1vOAgcsayxfmk9AXfgsMtuOOeG8S8KcxyGs5bDexrT/elDxo8m3Bzmtqdvoy/Rh81Y4u1xYntjFC4tBJyWEKvfunoMZz45BIPBN80EtvU6x1O97QB4s1VBh8dFJpLTvSdwprW2HSD7OCM7XgPsHXFeS3asJvv8jeNHZIz5pDHmBWPMC52dnad04iIiIjIJLbqe1/KWUxJ0MyPkOmw5Zd38OhoubaAqVIXBUBWq4p4r7uG25beN88SPz3Aw6ylwgsDUQIqAO/Cm2czGjY2kbIq+F/rY+vmtbPuHbQAUvqUQl7U0DCQmVTA8VkazHLS62Nm7merdBy4P7vzSQ8ZFJpKJ0iLiSPv87DHGj8ha+23g2wDLli076nkiIiIytYWbwzRubKQ90s7Ol3eSqfFx1cK3HrH/X938ukkf6AzP/5+j/8x2thOKh2i4tOFNP9fwctH2B9px57spWFiAK+DCV+HDWktdZ8sxr58OHn6plSe299Ld2sVl9zzOrVedc8TlnbdedQ6rf/IynX378BRWYFxugl73YW0hRCaC0x0E7jPGVFlr27NLPfdnx1uAWSPOqwXasuO1RxgXEREROaKRDc8B4m1x8i/Mn1j9/8ZA3fw6Vs5aie+TPj4y6yOj+oyVoUr2tu0l1Z+ivK6c8qvKDx5LpadtS4hhw8VeotaDTSVo7Y2y+icvAxwWCA6//uh/deIpmklNcfCoAaPIeDvdy0EfAW7OPr8Z+NmI8Q8bY/zGmHnAWcDz2SWjA8aY5dmqoB8bcY2IiIjIIcLNYf7h6X8glo6RSWTo+lUXqf4UgWpn39+E6f83RrxeLyUlJezfv//NT8ZZRppsSQIQmHVwb6TXWur7h6ZtS4hhw8VeXN4ANhHFWks0mebex7Ye8fxrl9QQjHfx0ZUX8cyqdyoAlAlrzDKBxpgfAlcC5caYFuAO4B5gnTHm48Ae4E8BrLWbjTHrgFeBFPCZbGVQgE/hVBoN4lQFVWVQEREROcydz93Jg1sfBMBmLN2/6abjhx24Ai7yzsnLnTch+v+NoYqKCkZbG6Fufh0/cf+EHexwgkBrKc5kWBV1Ufeue6dtS4hhw0Vd3Pkl2FQcmxjC+ENHLfYyODhIZ2cn8+bNO53TFDluYxYEWms/cpRDK45y/l3AXUcYfwFYcAqnJiIiIlNMuDmcCwDbf9jOwKYBAILzg8y/fT4j2wxPiP5/Y+h4gkCa1pH89Y+pLjC8lnTD0jXTPvAbqbo4SGtvFHeBs0w21X8AX0XoiMVeHn6plYb7fwnAfzVFOO+lVmUCZcI63ctBRURERE6pcHOYf3jKaWMw+NogXY91kdiXILEvQcnbSw4JAEdTMXOyG3UQ2LQOHr2FP+4d4MKZbujbC4/e4owL4BR7CXrduSAwPXDgiMVebnv4ZT774CbaWvYA0O8pYfVPXubhl1pP+5xFRkNBoIiIiExa4eYwDU/fTgZLbG+Mvd/Yi2+mj+r/U03e2XkUXVyUO7cqVDWqipmT3YwZM9jfthe+vgAaip3HIwV2G9YSjw7xWmeGC2dmvxImo7Bh7Wmd70R27ZIa7v7gQmpqnAI5Bek+7v7gwkMyfA+/1Mr3n9tDJpMm8srjgMFTUnXMvYMi422itIgQEREROW6NGxuJZhI039lMdEcUT5GHOZ+dg7/ST+nbS3PnVYWqWH/d+nGc6WnStI6KbQ/S1dNHpjeDyxgnw/eTT8JP/tLpk7giu+Szr4U/7suQzMCyavfBe/SpLcRI1y6poW7BdfjvvYk/W1hw2BLPex/bigX6n/8JQ1ufpvjtf447z/nHBzWKl4lKmUARERGZtDoiHSQPJInuiFJ8eTHz18zHX+k/5JzpsAQUyC3vrPDFSFsIv54ilXHaJv88L8BlNdUsLIGFG9dyxf+7mHBFLX9oderwXVQzIgic5m0hjsTr9VJZWUlLy+EBcmtvFJtJM7AxTGDuEoqWX5c7pkbxMlEpCBQREZFJqzJUSWyP0w+w9J2l+Mp8zgFrwdppswQUcJZxJqNUhJyvd9f8KMp/vJgkHMrjz3+R4MU7dpKOZcAYetNR/iHfxdo+P55CNx8/v4ZwKA+8wWnfFuJoamtrDwsCH36pFQNEm18kPXCAgiXvyR0zoEbxMmEpCBQREZFJq35pPck9CTAQqHH63AUyGe7pHuDlpWtYf9366REAQm4Z57zig4Vwnm1J01hSTNcTPcTb4+z+2m4GXxsEIIOlb1eM4Lw8OnxeGirKCF/2l6oOeiRN66iNbWHvxl8dssdyeCloZPNvcIWKCZ5xce6Sm5bPVnVQmbAUBIqIiMikFm9J4K/y4/IZitNpGobM9Oxxl13GubzWTfMt+XzgHA/Pt6Zpyxgw4C33Eu+Is/uru0kNpEhH08Tb4wTnO0sWY8bQeOD34/kJJqbsMtvaYJyW/szBPZY//1xuz19ifzOBmvMx7oPlNu68duF4zVjkTSkIFBERkUkp3Bym4dkGBnYNOI3OjSHmC8F7vjL9AkBwlnF6gxhjmFfiomhegNe7MkS2D4GFyhsqqf1kLTZtibfFie6KgoXgvIP71joiHeP4ASao7DLbWYWG/jg8sycFWHjhP3kp8Fe8N/MEqZ52vOWzc5fUaC+gTHAKAkVERGRSatzYyGDfIMmupBMEArF0jMaNjeM8s3Gy6Hp4/31QNItwKMRzC8sA6H2mDwB/jR9/tVM0J94WJ7rTyWKNDAIrQ5WnedKTQHaZ7bXneqguMFzxvSEe3pIEoJgBPtb3HbCZXBB4pD6CIhONgkARERGZlDoiHQcDmfnKZgFOIPjZV2ictwD3vDwAep/txXgM/hl+vKVeXH6XEwQ2R/GWe/EUOEsYp00V1eOVXWZ7VpmbLZ/JZ1m1i4/9NEpzTwaAHZ1xALzlcyjJ8x7WR1BkIlIQKCIiIpNSZaiS6I4oGGWz3qgj0oE7z03RxU6/OpuyGI/BGIOvykesLUZ0ZzQXPBf7i6dPFdXjtWINTq1PKPAbfvChPAYS8OhWJxu4uTONxwXe0mpiycw4TlRk9BQEioiIyOTTtI76fW3Etkfw1/hxB50+d8pmOYYD4aqPVQFQfFlx7pi/ys/Q1iGSXUnystnCoCeoAPBoFl0Py/6C4UDwjBJDSQC2HHACvs2dGeaXejFuL9Fkmnsf2zqOkxUZHQWBIiIiMrlkqzW+Z/8eMjuGKJ8XwFhLlbdI2ays4UDYk+/hvG+cR/WfV+eO+av92JTTRD7vbCcInNZLaEfjfV+DD34bgqUYYzi33M1r2SDw9S5LqnRe7tThiqEiE5mCQBEREZlcstUat3VlGIxa1pbEadq1l/X7ehUAZtXNr+OGc24AwJ3nxuXNfuWzlkCtU0Sn9B2l5J3hBIFaQjsKi66HL+6ED/4H51YXsOVAhpZMOc0DHjoLDhaCqVZlUJkEFASKiIjI5JKt1ri508nELK50HzIujtuW38Y9V9xDVagKg6EqmeKG/gEqFoaYf9v83FLRQCajJbTHY9H1nHdZHfsilsxAJ4l4nAUF/YAqg8rk4XnzU0REREQmgKZ1ThYQZynjcHXGM0qy/6adreIoB9XNrzuYHf36AujrZUk8QeOcYjqAymSK+rhbGdTj0bSOczvDADyxyykO8xfFL9KS/zyL6z6pyqAyKSgIFBERkYkvuw8w7DM01lbT4XHT89tWCvISFAUMeIPZKo5yVCvWwKO3UBcZoi4y5Ix5g05vQRm9DWs5t9gJ/jbsTAMwvzDDJ0I/hiX/OJ4zExk1LQcVERGRiW/DWsI+w2pTwJZNETIWeg+kSM/wE66Y5QQyi64f71lObCOayYNxHvV7O359LcwrceFzw4bmFADVBUbLkWVSUSZQREREJr6+Fhprq9j1jVYGXhqgcFkhic4EgdkBGmdWU6dAZnQWXa+g72QV1eLp28t55S7+uM9ZklxT4NJyZJlUlAkUERGRCS9cUUsbLiJbIgD0v9BPYl8CX4WP9kj7OM9OppUVa8AbZOFMpyBRvg8K8vO0HFkmFQWBIiIiMqGFm8M0FPgY2hElE81QfXP1cN9ufDN8uXNETovsstpFNflANgvoUVsImVwUBIqIiMiE1rixkZhNMvDHAYzbULS8iND5IQB8Fb7cOSKn08IKp0ptTaGBaDc8eotTwEhkElAQKCIiIhNae6Qdm7b0PtdL6PwQ7qCb4kuLwQ3+aj8AHZGO8Z2kTC8b1rKofLgoTPbrdDKabWEiMvGpMIyIiIhMWOEnbgdrGdg0QKonRemflQJQfGkx+efn4y3xAlAZqhzPacp009dCVb7hLVUuLq11HzIuMhkoCBQREZGJqWkdjTt+TCZt6PzfTjylHgoWFwBgjMkFgAF3gPql9eM5U5luimoxfXt54ZP5h42LTAZaDioiIiIT04a1tLsMO+/dSbQ5yswPzcS4Te6wwVAVqqLh0gbq5teN40Rl2slWCD2EN6gKoTJpKBMoIiIiE1NfCwX9pUS3R6n8SCUll5XkDlWFqlh/3fpxnJxMa8O9FjesdZaAFtU6AaB6MMokoSBQREREJqRwRS3tT3YCULisMDcesFbLP2X8LbpeQZ9MWgoCRUREZMIZ7g144NUh/FV+fGU+sJbijGXV/A9q+aeIyEnQnkARERGZcBo3NjIUjxPZGiF/Qbb4hjEEAyXUXfnl8Z2ciMgkpyBQREREJpz2SDuDLw9ik5aCCwty4x3J/nGclYjI1KDloCIiIjLuws1hGjc20hHpoNDn7P/r/X0v7gI3ofNCufPUD1BE5OQpCBQREZFxFX7idhp2/ZSYcdo/9CX6SMfSDGwaoOTykkPaQqggjIjIyVMQKCIiIqdf07psef29NNZWE/W46fltNzZhKXt3GR0/7MAmLMWXFR9ymQrCiIicPAWBIiIicno1rYNHbyHsM9w9q4Y+t4v2B9rp/k23U63ADT2/7aH8feXknZGXu6wqVDV+cxYRmUJUGEZEREROrw1rCfsMt7T72NeepO/5frp/003R8iLIQPt/t+Ov9jPzT2bmLgm4A1oKKiJyiigTKCIiIqdXXwtfzpTQ/PVduaHArAA1n6ghsT9BtDnKjD+Z4ewFtJaqDNRf0aCloCIip4iCQBERETm9imrZ9WwnANUfqwYDBUsKcHlczPiTGfS/0E/hW5wKoQZYv2wNKAAUETllFASKiIjIaRFuDnP37++mr9Qw+GoEf62f0neWHnJOwcICChYe7AtY6SuGRdef5pmKiExt2hMoIiIiYy7cHOa2p2+jN95LfF+codeHyL8g/5jXBNwB6pevPk0zFBGZPpQJFBERkTHX+NzdpGyKrvVddPywA+CwIDDoDuL3+OmL91EZqqR+ab32AYqIjAEFgSIiIjKmwk/cTnuil0zSciB8gOCZQcpXlpO/wAkCDYamm5vGeZYiItOHgkAREREZM3c+dycP7vopGEP3hi5S/SlmfXoWoXNDuXMqQ5XjOEMRkelHewJFRERkTISbwzy49UEwhqHmIfb9eB8FFxaQd87BBvBel1f9/0RETjNlAkVERGRMND53NwD7HtpHZ7gTT7GHmo/XYIzT/6/Ywqorvqx9fyIip5mCQBERETnlhvcBxtrjdIY7KVxWSNVNVXgKna8eVekM6y9qUP8/EZFxoCBQRERETqlwc5iGXT8lcSDJvv/Zh/Eaqj9anQsAsZb6Mz6k/n8iIuNEQaCIiIicUvc8fw+9rXF2NOzApiwV76s4JAC84dwPU7f8tvGdpIjINKYgUERERE6ZcHOY3ngv+x/ej/EY5t8+n8DsgHPQWu6JoABQRGScqTqoiIiInBpN62h84otEd0bpf6GfsqvKCM4JOoVgcPYB1l2xZpwnKSIiCgJFRETk5DWtg0dvoR1L2wNteIo8lF9VfvC49gGKiEwY4xIEGmN2GWNeNsZsMsa8kB0rNcb8yhizLftYMuL81caY7caYrcaYq8ZjziIiInJ04afWsnJmCd2/7SG6M0rlhytx57lzx4v8xdRd+eVxnKGIiAwbz0zgO6y1i621y7KvVwEbrLVnARuyrzHGnA98GLgAuBr4hjHGfaQbioiIyOl353N3sioEe6PQ8dA+QueFKFpelDsecAdY/dbV4zhDEREZaSItB/0AcH/2+f3AtSPGf2StjVtrdwLbgYtP//RERETkjcLNYR7c+iAYQ8e6DmzcUvXRqlxD+Kq0peHSBjWEFxGZQMYrCLTAemPMi8aYT2bHZlpr2wGyjzOy4zXA3hHXtmTHREREZJw1Pnc3AJHXI/Q+3UvZ1WUEqp1qoAZYv2yNAkARkQlmvFpEXGatbTPGzAB+ZYzZcoxzzRHG7BFPdALKTwLMnj375GcpIiIyDYSbwzRubKQj0k5l2lLf1U2dpxRWrDl2IZemdXQkeklHM7T8RwveMi8zrpmRO1zpK1YhGBGRCWhcgkBrbVv2cb8x5qc4yzv3GWOqrLXtxpgqYH/29BZg1ojLa4G2o9z328C3AZYtW3bEQFFERGS6CzeHufv3d9OX6AOb/d9lto1Du9uwqqKUVUDVHxqo736ZutKFhJ9aS6M/TbvHjcsYMtZSlM6A20Xbf7eR7Eoyb/U8XP6Di4zql2sfoIjIRHTag0BjTAhwWWsHss9XAmuBR4CbgXuyjz/LXvII8ANjzNeAauAs4PnTPW8REZGpIPzE7dy266ekskEfxmCtpeuXB+ja0IW31EvVR6oIzg3S7nGzatdPWbXrpxACm3aT7EzirfBijKHP42Zo+xB9v+uj4n0VhM4KOfe0lhvO/bCWgYqITFDjkQmcCfw02zjWA/zAWvtLY8wfgHXGmI8De4A/BbDWbjbGrANeBVLAZ6y16XGYt4iIyKTxxmyfYcReCmOIt8XZ/+h+kp1JUv0pEvsT5J2dR2J/gp337GTe6nkE5wRzGcLe3/XS9kAbmWgGb5mXwrcUUnJFCW33Z3sCvs/pCeiyln+KQN3y28blc4uIyJsz1k7NVZPLli2zL7zwwnhPQ0RE5LR5Y+CXSVmw4PIdXKKZHkrT8WAHPU/24PK5CMwN4PK7KF5eTNElRaR6U2y/Yzv+Sj/VH6um58kebNLS80wPgdoAxZcUM7h5kMFXBrEpC26YUz+HgkUFABhraVr6JnsJRUTktDDGvDiiJV/OeBWGERERkVMo3BzmtqdvI2VTAKT60+z8552kB9KUXV1GtDlKdHeUTCxDOpKm7F1lVLy/Ak/hoV8FvCVeZn5wJm3/1cb227ZjPE4m0J3nZvYts/EWeyl7dxmJrgT7H95P4eLCXAAIKgYjIjIZKAgUERGZpA5W9ezAABkskS0R4h1xOh/tJDWQwlviZd+6fXhKPOSdmYcxhrKVZeSdmXfkm1pLydtKiLfG8ZR4KLmiBOM22IzFk3/wa4OvzEftx2sPu1zFYEREJj4FgSIiIhNQuDlM43N305HopTKVpj7upm7OSti2nnCqm7vLSuhzmdyePQv0v9jPnn/bA4Bvpo95q+YRmBUg1ZvCW+4UcznMiP2CVb5i+mySodQQVTdVHXli1jrvOfw4wg3n3KBiMCIik4CCQBERkQkm3Bym4enbidkkGEO718Mqj2XVgfVQAlAKxpAaTBHbG8Pld+HJ99D63VYCcwLM+qtZeGd4cXmcvYC+Ct8R3ydgLQ1z/4S6K7985Pcelq0fUOzJY9Wld1A3v+6QLGRlqJL6pfUKAEVEJgkFgSIiIhNM48ZGYjZJ77O9tP+wncK3FFLx/grIQNv9bQTPCJLYl6Dv+T7IONe4Ai6M2zDrU7PwV/oP3iwbwA1n+1zGRQZLVajqiIHb8OvDspBXHFrspW5+nYI+EZFJStVBRURExlvTOsJPreXuQIY+d7aSpzHsuHMHifYEmUQGm7ZgwLgMNulU5Sx/dzn5C/KJ7orS+0wvNZ+oIe+MvIOZu0yGVVHXYQGciIhMD6oOKiIiMgE8/FIrm8LfZr7vh3y7zHMw6AsBxp07L3EgQXR7lJnXzaTorUX0/LYHm3GKtiQPJHHnuwnODQKQvyCfivdV5K6tSqVZv68H3n+fgj8RETmMgkAREZHT5B8f/29+3vxvxGqHAF+usEqiK8G+/9mHO+TGV+HDHXIz+MogAEUXF+Gr8DHzupm5+xyy3PMNApkM9XG3AkARETkqBYEiIiKnQfiJ23l090+Jew0DmwYZah7CW+YlPZCm+zfdpAZSGGPIxLOb/AyUvK0E34wjF3XJsZY8C1GXofIo+/xERERGUhAoIiJyksLNYe7+/d30JfoAKPYXs+riVQeDsaZ1NO74MXGPm30/3kfno52HXO+v8jN/9XwCcwJkohnSQ2nceW7cIfcb3woAg8Eeo7iLiIjIsSgIFBEROZJssZZGf5oOjxtX2kfa+MA9RNWIlgjhJ27ntl0/JdqbItmTJFAToJdebn/mdiBbbXPDWjpKXHRt6KLz0U6Kryim6sYqJ9gLHBrsuUOHB38B46Xh8i8r2BMRkVNC1UFFRETeqGkd4V/fSkNJPjGX69jnWkvk9SF2fXUXNmkpXFbI7L+ZDUBVqIr1162HhmLeXV3Jbz63ncDcAHM/PxfjOnLj9mEjG7jXL1+tAFBERI6bqoOKiMjk1rQONqyFvhYoqoUVY9P2INwcpvGFtbSGQhx49ADJ7qQT3F1USHRXlHQkTdWHqzAeJ4iLdyTY/a+78ZX78Nf4GfjjAOmYk+HriLQ795tdy849zrUll5UcDACHgz5jDl9CKiIiMkYUBIqIyMTXtA4evQWSUed1317nNZzSQPDO5+7kwa0Pgtuw70f76fpVF54iDzZj6X22N3desitJ5Q2V9DzZQ//GfozbMOcLc0h2Jul/oZ/BpkGKLi6iMJ2h4enbibkNkS1DAITOdfr4VaUz1J/xIequ/PIpm7+IiMhoKAgUEZGJb8NaSEYZSlrW7nbxwuWV7A94qHxhLfX5oVOSPQs/cTsP7vopGMPgq4N0/bqL0neWUv2xajLJDJ0/68RT5AED7f+vnYE/DgDgzndT+9e1+Mp8eEu8uAvc9L3QR9FFhfS7DJlMgoE/DtD/Uj/+Kj/eUh9YqwBQRETGjYJAERGZ8MKpbhprq3np4QMcCB+gZK+L6v9TTbvb0PBsA8DJBYLZ6p143Ay8PMCe+/bgr/Yz80+d3nwur+uQPn0uv1PkpfbjtQRmBXLjxmUovqSYrl91EX1vjODcIN2Pd9H+QDsApe8szZ5oaDzwe7TwU0RExsOb7HYXEREZX3c+dyerKkrZG4fuDd14ijz0PNnD4B+dZuqxdIzGjY0n9R7hp9bS7nYx8McB9jTuwV/lZ96qebiD2Sqd1hJMZ3J7+EquKOHMhjMPCQCHz5vxgRl4Cj20fq+VwdcG2ffgPkLnhaj6syrK68pzp3ZEOk5qziIiIidKQaCIiEw44ebw/9/enYfHddf3Hn//zuwzWkabJdlOvKRZIGQDA06ajYaERZQkhZBAC+1t6XIvtG7pggMuCQnPdXLbh1vT3JYuQNN7e5P4ttACykZTSjaS2NmcxWRzHFuxJC+SRtLs55zf/eOMpNEytuR4k/R5PY+i0Zk5Z86Z+caaj34bF95xIWfdfhZ3/exOfM+y5zt78As+K/5gBSZiGN0+Ov74ww5U2zbztb95B+tTUOwtsfuvdxNbFmPVF1cRrp/oLHPLyqt5ImO4Zd8BOssuWItj7cR3oDPSCARLPCz9taUUegrsvHVn0F30N5fT8v4Woi0TC793pDoO75xFRETeInUHFRGRE8rYunuuCWbQtMCbf/8mw08O0/HJDhIrEyRWJ8i9nBvfpyPScFjPs3HHdxnwfcp7XN74izcwEcOKdSsm1umzlmvPuI6utRvg0pvpAroOMkvpFf/3QnrLGRrOa2DlH6xk6NEh2j/RTiQdmfTc8VCcde9cd1ivj4iIyFulECgiIieOyti8QtayY+MOwvVhwukww1uGWfJLS2j9QNCdMnVain3d+/AKHqmoYV3f7mAG0aqZQrt3dLPpqU30ZfvoSHVw8fKLeXDHvfSVhoj7lrxjGH05x65Nu/ALPk7CYeUfryTSXAls1nLLyquDAFjt7E/UnJF03drrWf/QegDq3lFH3Tvqxu9zrMUaQ0eqc3yheRERkeNBIVBERE4Y3Q/dxJ6koefvdlM+UMaJOmRfzJL++TRtH2kbf1zy1CT4kHhplBs7y3Rlc3Q/dBMbX/gGmVJm2nF7s73c9bM7sYCf98knQww/Nczuv9pNdEmUpkuaqDuzjvjyiTF+ndH0nGfv7FrdxdN7nw6WmagSt5YbV16t2UBFROSEoBAoIiInhO4d3dyYtGQeH2b0+VE6P91Jy2Ut0x9oLZ9u99ho4INb+um6LE53KsmGpMWdEgBzO4Iuo4mTEwDs/uZuRp4Zof7ceoafGiaxMsGKL6wgXDf91+G6tdcf1nVsWLuB85acN6kVUi1/IiJyIlEIFBGR4657RzdfevhLlEuWvrv6SKxK0Py+5skPspa0b1k/UqIrN8x/LA/xox0uF3w0yZfaWvCNYeT5ETI/zZB6e4poa5TXb3kdLETbo4SSIfKv50msTjD85DBNFzfR8ckOQvHQtPO59vRr31Jo61rdpdAnIiInLIVAERE5qrp3dLPpsY30lYZo8HxKjkPeMePLLYwzhn0/3Ic75HLy50/GOMHEMEH481mfd+i66IZg2w9+j8tXF7jpwRJ/nEzjAAfu20/fnX2YsGHokSFwINIcof1j7ey7ex9e3mP5by2n8fxG/Jw/MfnLGGtJh5Osv+AGBTgREVnQFAJFROSo+dpjX5sYH2cMmXCI4p4i0SVR3FGXUCKEE3PwXZ/hJzIcuPcA6Z9Pk/y55PgxOqNp7v/Uw5MPvOsxUnv+EfuTA4xuz5L9WZaBBwZoeFcDy35zGSNPj7D33/ay9DNLqXt7HekL0pN2nxYAgWvPuI4NUyeBERERWYAUAkVE5Kjo3tE9HgC9nMfIsyOMPj/K0CNDRFoilIfKRNIRlly1hMEHB8m9kiPaEaX9mvbxY8RD8ZnH5r1yP/ed1UooNUTP3/RgXUvLB1vo+EQHxjGkz0+TPj89vbVxjLUkLeQdzdYpIiKLj0KgiIgcFZse2wjA6PZRev66B3fYBQPN72umsKdA3Vl1ZLdnefNbb2KiJuiqubZxvBuoYxxuvODGmcNZpoe9TctZ+cWV7L9nP7HOGG2/2IYxk7uQfmA0y4PJJL3hEA7gE7Qsrlt7vUKfiIgsWgqBIiJyRHXv6GbjI18l4+Vwsx493+zBSTqs+vwq4svik7pi+q5PaW+JcCpMuHHiV1LcWm686L/XDmqNy+lwPXpPTnDSb580+T5ruaXuLLp2PweZDHj1kxZ0FxERWewUAkVE5Ijp/s8/ZcPO7+Eag7Ww5zt7cEddTvnCKSRWJKY93gk7xJdOrM03PgPo6l86eEvdZV9h3b//MTc21VFwnEl3XXvGddMXeBcREZFxCoEiIjJ32zbT/dBNbIp59IVDdLge67Ium+rClEMhBn50gMzWDLmXcnR8smMiAFpLxEJ5htlBJ80AeqhWu7M/QRfAQzexKeYG56BuniIiIrNibK1B8/PcmjVr7NatW4/3aYiILAzbNsMDN9HtDrCpOU1vqNL6NjYGD8ZD3b579tO/uZ9oR5T0+WnaPloZq2ctt2Sh63PPH4cLEBERWXyMMU9aa9dM3a6WQBERqal6jb94oyXvNI8Hv/zOPAP/MUB5qEzzJc3Un1dP5vEM/Zv7aXxvI8t/e/nEWn9Ap+fTddGNx+lKREREZIxCoIjIQlDpnrkx7pMJOZNb6ICEbwFL3hgwhnQszfr3rJ/cdXLqMcZUxvf1bxvFOAZ31A2WdHgph4kaQnUhdv3lLkJ1IbysR/L0JMs+u2xSAIxYy7pTPqbJWURERE4A6g4qIjLPdf/nn7Jxx3fJOGZa+Duoyr//ad9n/UgZ3AIbmhtwp0y0Yn3Lntv3MPiTwfFtkZYILZe30HRxE07MYfipYYafGibWGaPl/S2EkqHx50iHk6y/4AaN1RMRETnG1B1URGSh2baZr/30Ju6KASGH0RdHGXl2hNLeEpHmCLHOGMNPDuOXfBKrEkRbo5QPlEmdkaLhXQ3jgXEoFGJ9owPEGN2eZXTbaHB8B/yCT7G3SHZ7ltauVurPqSeUDBHrjGFCE4Gz8d2NNL67cdopdkbT3P+ph4/BiyEiIiKzpRAoIjKPdO/oZuPjG8mUMkFLXgzcrEffnX0MPTyEiRii7VFGnxvFupbY8hihVIjBBwexJQsOHPjRAdIXpQnFQ+BAtDVKw7saKO0rsfPPdk6EOwsmarCeZemvLaXpkqaJxdjHjPUmmaEFMh6Ks27t9Uf5FREREZG5UggUEXkrtm2Ge74I+YGJbcahOxlnU3MzfSETLJ8wOESXH4cP3Xp44+K2bab7x19mQ0N0vLumV/TJPJah/5/78XIebR9po+2jbThRh/JQmfJAmcSqBMYYrG/xch5OxOHNb73J8JZhcMC6Fluy9P5TLyZsiLZFOeWrp+DEHLAEj/EsTtiZfk7Wcm0Rzjv9ajbtf5zebC+OcfCtT2eqk3XvXKcuoCIiIicgjQkUEZmrSvCzleA3tQ2sO5XkxtbmSYuYx32fG/cP0JXNQTQFZ19H9xv3T6yzd7A17n74Bdj6La5YvpQe15B7LcfwE8MMPTKE9SzJ05Is/fRS4ifFp+87C8X+IsNbhinsKtDa1Trjou5jkuEkeTdPR6pDIU9EROQEV2tMoEKgyIlm2gyNBt9Lkhz5GF++5Je56rxlx/sMF4fKunhkeqBxOZx6BbkX7iae7wULYxNfbuv3+NIDRXZlfM5uD7EybbhnTTuvPzlKeaBM/KQ4TsIh1hHjpKUR7hndR0PMHDooAiSa4cyrYeu3ActJvfX03tGHN+Jhwoami5toWNNA6m2pyd00Z/h3PVHZlh973GwmkLEWQ9Ag2KmF2EVEROYdhUBZkLp3dLPpqU30ZfvmRcvEtPFcBzNt7FXwn7GZHLsKLt1OkY2tzcGskDDztP9SWyVwV7fGXbz6g9z7yvfJeEEQS/s+6w8M8uFsbrzF70DO547nXf7x2RJb9vik47BmaYhXB3x2ZyyeBSfhEG2LUnizAN7EU7YkDI99NsVnV3ey4+UC7rBL/Xn1RFujAHSWXe7v2TP+eEvQ0vj/XijziX/JkzwlSdtVbSRWJgjXzdyj/9qWd7Hhla0TAfayr0x0QT3IMhDVIk6Em3/+ZtWSiIjIPKYQKPNTrQ+s1aa0gCRssBaaA/gcnRaMqWFurLXEMQ4+dmJcVKSRdfv66RrcS3cqyYbW5mnT70/luz6Zn2YYfX6U0v4SsaUxmi5sIrEqgRN1xq9zxusnWI/t5pVX09V81uSWrOogcCQd4j1KWEvMQsYxNHg+JcdMtEZVXcOkdeyohK+8Q9dFNc57aoALJbg4l+fBUOVn1+NiL8SDyQS9Xn68Hhq9CBdm8zyccibOd2ormgkmQxl5doTY0hh17VFu3neAiwayfPU/i9y2pUTJg3PaHX71nAifOSdCSzI41s4hn49k6vDf3UQoFcIv+QDkXsuR7CvQv7mfomcZLVMJ9uDEHVre30LjextxB0pc+Wgf7SnDurVRkhHDc/0e7/n7LKuXRwivPwU/PiX8VerhcGt9Uj2jPyaIiIgsFAqBMt3U7m6XfYXuulTNlqqEtcQwZBxDxywnfajZ8lVrhsGKtO/zgYLHvfEQGWf6wtcA7qhLqa9EeahM3dvqCKVCE4dzbTCZRWwiNEWspTxD+Jj1B95tm8nd8xUecDJ8ZRZhrvrarh0e4cehODv7XPI78xR2F3BiDiZsMGFD8c0i+V15oq1RSvtLlPeVCafDxDpi5HbkglkdQ5BYkaD+nHpSb0thPUu0LYo34jH40CAmYmj/eDtOxAFr6RybjCSbozuVZFNT+tBjz6qulQduotsdmN7S2HEpXU9/L6ibSJLuqJ0x3Fpr8fM+JmQo9hWJLYvhhB28vIdf8Ak3hictJl7rtQMmuiR6lnUDg3SVoDtqp3Wn9EseWDPpfZ+x2+NY0LOW0edGyW7PYsKG9PlpcGDwJ4NknshQPhC8D6s3rKa+L8/Qt3rYMWj5L+dG+L33RjmnIzT92Bx8TGDbyyPc9kSJR1Y0wHmNOHGHvjv7GH1+NEipQCIMeRdObjSsTDu8fMDHWnj2d1Jsba9jY3PTeIDVGnwiIiJSi0LgItG9o5tNj22ktzQ0aftES5XBn2s3xFqqpoYfCwe88D02xTx6w6HgOLU+hFf4RR/rW0KJEGO1OD62yVrcUY/s9izZl7L4BZ9oWxTrWoafHqb4ZnH8OE7cIbY0RqwzhvWDD/Z+yafl8hbaPtw2HhCttXhZDyfuTJ7t0FqMcbDY8VkNgcmtI57H+gODbGpKszsPmS0ZMo9lKB0oEWsPnjvWGSPSGqG0t0SoLkT9WfWUh8rs/de9jDwzMv4hP1QXwpaDoGo9S6guROr0FO6Qi4kZWi9vpe6cOowxeFmP7EtZcjtyZF/Mkn89P96CNP6WhQ3WtSR+LsHJnzsZJ+rgJB3oyfOxRJm7m+snBxIT4cYLZ+jqt20zxR/8EdFyhrtrtVxWWltj1pJxHHIvZxn4aYbyQBlv1AuWFzBQ7CviDU/0gwzVh4i1x8jvzGNdi5NwSJ6aJNwYJtoWDYrUB3fEJdIUIXlqksTKqtbPsXOvhKlNTWl256DwZgHjGEZfGGXf3fvAg8SqBK0fbqX+7HpGnhmh2FfESTgUdhaCQNoZw8t6FHoKlPeXg9fPt+PvDyGoO7OO+nPr6d/cj18I7ji50fBPv5TgwpOnd8OculLCpOBdFcjHTA2K7qhL9uFBfrGY53+eaXnwDY8/f7RI0YNoCG64JMYFJ1U9bzQFH/mLo9O6KyIiIguCQuA8NTbmrXrq9XFT3rvxlq5DhDhrLX7Oxx1xcYddIi0Roi3BeCS/5JN7LUfupRzloTIA7rCLn/eJLomSPC1JeaBM+oL0+D7Tzqfq+f2yT7GnSP71oPXLL/mYiKHUV6LYW8TNuABEO6L4JR930CXSGqH50mb8vB98qPeDkOckHdxBFwykTk9Rd1YdsaUxQvEQQ48OUTpQorArCASpM1JgIPN4BhMyOHEHDPg5H+tZwukwze9rJtYZI3laMri+9uhEy9QM12J9y+jjGQ48OsToC0GrTWxpjPiKOKX+EsW+In6u6v0BTMSMn3/TRWmSp6dIrEgQbgqPh13rV8LvoVrFKtxhl9xrOZyoQ6k/CJupM1JkX8rS83c9QashQejyRjwizRGaLm0i1hmca2xJDIBO1+f+d98wESJ++AXs1m+Nj3v7hdZ2XtqSJfdaDutZmi5sou4ddXgFj/yOPN6oR+6VHAd+dAAn7hDtiAZh2w+uKdoSJbY8hi1bIq0RRp+vTJSyPE5saRAGCzsLQR0OuePX58Sd8dBlQoZIWwTrWkKpEPXn1pM8JUmr47PjiRGGHs1g3Yn/DxrXNhLrjJF5PENxz8QfCcaEG8JEO6MUe4uE68PEOmPUn1NP+vw05Uw5WFuvbGlY00CkKQIEIXPkmRGaovDUihyJiJlUIhgYtHV8I/JZus5eyrtf+8vpXXC3bYYf/D6Us5PO51BBsaZEM3zx9UM/TkRERBY1hcAT0MSkJr00+JZS1XioSWbRMmc9izfqUTpQIvti0LXNL/uUD5Qp7y/jF3xCqRCFngLukIv1qrtmQuKUBLZsKe4pYssWTBAisBBKhQjXhYMQV/TH9wmlQjgJh4ZzG2j9cCuRpgh+MQiR5YEyI0+PMPLcyHgocZIOTtzBlizRjmjQctYeBJLsS1mcmEOsMxa0er0cfBBuPL+RlstaSKxMYMIGLxe0LIWSM3fDm6qwu8DgI4PBNfngpBzC9WGGtw6Te2Xyh+1QfYjlv7mc+rPrJ/bvKQSBoq9IaW+JwhsFIi0RGtc2kl6bJrY8NhHmrMUb9ijtKxFpjeBmXAZ+HCwh0P7xdsKpEGFr59SNdPzFnmXjbKGnQGZLJuhiurtIYlWC4acmrtWJO6z+8mriJ8Ux1rJt5+7J+7uWO54r88DrHnfttrhDLuGGMJbg2hKrExR7i/j5ibDb/P5mOq7pmOiCeRj8ciX0OQYTMkHQfTVH7pUcpX0lTMRQHigHdTE2ji5iSF8UzI4JQT2OLW1gPcvgQ4O4GZfkqUmSpyTx8l7QBfVQ/z/N0HodsZab9x0YD2jWwnedDxL66NfnNlvr1DUFx2b/fPbOaQGxFjcUJ3zlX6oFUERERA5JIfA4mzrxQsS3lA2zarVzh1wKbxbw8z5+wcfLexTeKFDaW8IdcfFGPLysN+P+ofoQkZYITszBG/GInxwn0hwh3BAm3BAmVB8iuz1LfkceEzbElsVInZEidUaKUGJy0PKyQcBxkg5DjwzhjXi4GZfhp4fBh+iSKO6IOx4QwukwDe8Mpq9PrEwQaY0c+gN4RXmojJf1iC+bxbpnh+hyWotf9CnsKgStagmHgX8foNhfpOHcBghBeV+Z3Ks5CEG0LYoTc2i5ooX0+Y1EYPZhrqKz7LJucGjSeK4ZTRmnOOO4ylrLAczwOjjWUsy4lPeX2XXbLvy8T8N7Gjj34238eKCfgbwlHYfXBy1X35Xjub0+y+oNhZOTNF7VTuLnEtiSZd8P9pF7LUekOQjB4YYw+EHXy2PFHXYp9hdJj5b5w8Yit6xacljv/Yw1Y4OZVz8wmuXeVGpizF1ldtBJLXRrfgM+8vW3cCUzqB6jm2gKTik/SIY6fGtJmyyFRAfJD92kACgiIiKzohB4HHXv6GbDwxtwrTvtvvJAEDTcYRdbtoy+MEqhp4Bf8IMujH7wwXeqUH2I+LI4ofoQ4frw+PdwY5jU6SlwwIk4b6l1ZraKfUUyT2Qo9hRx4g4N7w660sWWxmbdxXFWZqjVtO/zgbLDg8kEfV6eBs/HGsPw2PPOISC4wy49f9tDaX8p6DLaGKb+rHqaL2ueNBX/TGFufMwlE8PKqp970vpviWb40K2H/UG+ugW5w7OsOzBQc6KUuO9z5cgo/1ZfR8FxKPYV2X/3foYeGaSlziGF5Y2M5bQWh90Zn3gY/uGqBL94Wpi761Kzms10XI1/Sw53dtBa7131a/m15jR3NdTPLhRXtk+bHdQY0qFEEPQG91ZOuvIewfSWu7fw3omIiIgcSwqBx9EV/3wFvdle+jb34UQdIm0RSv0lhrcMU+ydPG4p2h4leWqSUDKEl/fABjNCxk+KB90v40GXylBdaNatanNWqyZmmEb/sI95iNlBp95/OFPWH8nutmPGugV+eDQHBvKJzsktM1Wzam5qbqIvZIKxXsVQ7aUOjpSpSyZ4lnUDA3Rl83SnEpPGnr1vaz8P3D9Ma9JwZluI7ldcVjUZbn1/nJMbJ0LfD5NJNrY0MxyaRTDLmSN2jbWW4OisMcHKpo7l9PnFibUiR7PT1v/TQuciIiKy2CgEHkdn3342vuuz/XPbJ42pS709Rf07gun+I00RcDiy4a7qva05O+iUYFdrbbZJi7JHGrh4ZIh7w/7M66xVjmUqU/B3zjEEHbMF4KtCU2+4qutrjYCa9m3QWhRuPnpr7h0t2zbDv/438Muz3mXSuLfQI5NCVYPnYxxnTsuFHBEzjalTy5yIiIjIjBQCj6OxlkAI1q8r9gfdJqfNrnkwVa0htRzOOn5v2dSFwuey7t6JZoZFzw+5YPl8UmOGypqOxrg3ERERETlmFAKPo4ONCRxXazFzqoLIiivglfunTz8vMhfjE5Dshpn+tKDWNREREZEFoVYInL7isRxxYy1iX330q+S9/OQ7rSUdTrL+ghvmX8uZzE9nf0IBT0RERGQRO/pTRx4hxpgPGmNeMsa8aoxZf7zPZ666VnfxxK88wS0X3UJnqhODoTPVyS0X38pDv/KEAqCIiIiIiBwT86I7qDEmBLwMXA70AFuAT1prX6y1z4nUHVRERERERORYq9UddL60BL4HeNVau8NaWwLuBK48zuckIiIiIiIy78yXELgM2F31c09l2yTGmN8yxmw1xmzdt2/fMTs5ERERERGR+WK+hMCZFs6b1o/VWvu31to11to1bW1tx+C0RERERERE5pf5EgJ7gJOqfl4O7DlO5yIiIiIiIjJvzZcQuAU41RizyhgTBa4Dvn+cz0lERERERGTemRfrBFprXWPM54H7gBDwbWvtC8f5tEREREREROadeRECAay1dwN3H+/zEBERERERmc/mS3dQEREREREROQIUAkVERERERBYRhUAREREREZFFRCFQRERERERkEVEIFBERERERWUSMtfZ4n8NRYYzZB7xR+bEV2H8cT0dOXKoNORjVh9Si2pCDUX1ILaoNOZijUR8rrLVtUzcu2BBYzRiz1Vq75nifh5x4VBtyMKoPqUW1IQej+pBaVBtyMMeyPtQdVEREREREZBFRCBQREREREVlEFksI/NvjfQJywlJtyMGoPqQW1YYcjOpDalFtyMEcs/pYFGMCRUREREREJLBYWgJFREREREQEhUAREREREZFFZV6GQGPMt40xe40xz1dtO8cY81NjzHPGmB8YYxqq7ju7ct8Llfvjle3vqvz8qjHmG8YYczyuR46sudSHMeaXjTHPVH35xphzK/epPhaYOdZGxBhze2X7dmPM9VX7qDYWoDnWR9QY853K9meNMZdW7aP6WGCMMScZY35c+bfgBWPMusr2ZmPMj4wxr1S+N1Xtc32lBl4yxnygarvqYwGZa20YY1oqjx81xtw25ViqjQXmMOrjcmPMk5U6eNIY8wtVxzqy9WGtnXdfwMXAO4Hnq7ZtAS6p3P514ObK7TCwDTin8nMLEKrcfgI4HzDAPcCHjve16evY1seU/c4CdlT9rPpYYF9z/LfjU8CdldtJYCewUrWxcL/mWB+fA75Tub0EeBJwVB8L8wvoBN5ZuV0PvAy8HfgfwPrK9vXArZXbbweeBWLAKuA1ffZYmF+HURsp4ELgd4DbphxLtbHAvg6jPs4DllZuvwN482jVx7xsCbTWPggMTNl8OvBg5faPgI9Vbl8BbLPWPlvZ94C11jPGdAIN1tqf2uCV/UfgqqN+8nLUzbE+qn0SuANA9bEwzbE2LJAyxoSBBFAChlUbC9cc6+PtwAOV/fYCQ8Aa1cfCZK3ttdY+Vbk9AmwHlgFXArdXHnY7E+/1lQR/RCpaa18HXgXeo/pYeOZaG9barLX2YaBQfRzVxsJ0GPXxtLV2T2X7C0DcGBM7GvUxL0NgDc8DH63cvgY4qXL7NMAaY+4zxjxljPmTyvZlQE/V/j2VbbIw1aqPatdSCYGoPhaTWrXxz0AW6AV2AX9urR1AtbHY1KqPZ4ErjTFhY8wq4F2V+1QfC5wxZiXBX+sfB9qttb0QfNgjaBWG4D3fXbXbWB2oPhawWdZGLaqNBe4w6uNjwNPW2iJHoT4WUgj8deBzxpgnCZpbS5XtYYJm91+ufL/aGHMZQVPqVFovY+GqVR8AGGPeC+SstWNjgVQfi0et2ngP4AFLCbpz/aExZjWqjcWmVn18m+CX8FbgL4BHARfVx4JmjKkD/gX4fWvt8MEeOsM2e5DtMs/NoTZqHmKGbaqNBWKu9WGMORO4FfjtsU0zPOwt1Uf4rex8IrHW/oyg6yfGmNOArspdPcBPrLX7K/fdTTDm4/8Ay6sOsRzYgyxIB6mPMdcx0QoIQd2oPhaBg9TGp4B7rbVlYK8x5hFgDfAQqo1Fo1Z9WGtd4A/GHmeMeRR4BRhE9bEgGWMiBB/i/sla+93K5n5jTKe1trfSXWtvZXsPk3ucjNWBfrcsQHOsjVpUGwvUXOvDGLMc+B7wGWvta5XNR7w+FkxLoDFmSeW7A2wAvlm56z7gbGNMsjK25xLgxUrT64gxZm1ldp3PAP92HE5djoGD1MfYtmuAO8e2qT4Wj4PUxi7gF0wgBawFfqbaWFxq1Ufld0qqcvtywLXW6nfLAlV5L78FbLfWfr3qru8Dv1q5/atMvNffB66rjOVZBZwKPKH6WHgOozZmpNpYmOZaH8aYNNANXG+tfWTswUejPkxltpl5xRhzB3Ap0Ar0AzcAdQSztQF8l+DFs5XH/wpwPUGz6d3W2j+pbF8D/APBpA/3AL9r5+MLIpMcRn1cCtxirV075TiqjwVmLrVR6brxHYIJQAzBTJB/VjmOamMBmmN9rCT4I6MPvAn8hrX2jcpxVB8LjDHmQoJeAM8RvOcAXyIY27MZOJngD0fXVMYOY4z5MkF3YpegC9g9le2qjwXkMGtjJ9AARAkmlbrCWvuiamPhmWt9GGM2EGSWV6oOc4W1du+Rro95GQJFRERERETk8CyY7qAiIiIiIiJyaAqBIiIiIiIii4hCoIiIiIiIyCKiECgiIiIiIrKIKASKiIiIiIgsIgqBIiIis2SM8YwxzxhjXjDGPGuM+UJlDcGD7bPSGPOpY3WOIiIih6IQKCIiMnt5a+251tozgcuBDxOsJ3gwKwGFQBEROWFonUAREZFZMsaMWmvrqn5eDWwhWGB+BfC/gVTl7s9bax81xjwGvA14Hbgd+AZwC8HC9DHgf1lr/+aYXYSIiCx6CoEiIiKzNDUEVrYNAmcAI4BvrS0YY04F7rDWrjHGXAr8kbX2I5XH/xawxFr7NWNMDHgEuMZa+/qxvBYREVm8wsf7BEREROY5U/keAW4zxpwLeMBpNR5/BXC2MebjlZ8bgVMJWgpFRESOOoVAERGRw1TpDuoBewnGBvYD5xCMuS/U2g34XWvtfcfkJEVERKbQxDAiIiKHwRjTBnwTuM0GYysagV5rrQ98GghVHjoC1Ffteh/wX40xkcpxTjPGpBARETlG1BIoIiIyewljzDMEXT9dgolgvl6576+AfzHGXAP8GMhWtm8DXGPMs8A/AJsIZgx9yhhjgH3AVcfm9EVERDQxjIiIiIiIyKKi7qAiIiIiIiKLiEKgiIiIiIjIIqIQKCIiIiIisogoBIqIiIiIiCwiCoEiIiIiIiKLiEKgiIiIiIjIIqIQKCIiIiIisoj8f0u3q4cGjd8CAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 7))\n",
    "plt.plot(macro_data['SPX'], color = 'black')\n",
    "for i in range(3):\n",
    "    plt.scatter(macro_data.index[gmm_clusters == i], macro_data['SPX'][gmm_clusters == i], label = f'cluster {i}')\n",
    "plt.legend()\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('SPX Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SPX</th>\n",
       "      <th>Recession</th>\n",
       "      <th>GDP</th>\n",
       "      <th>SPDIV</th>\n",
       "      <th>SPPE</th>\n",
       "      <th>BUSLOANS</th>\n",
       "      <th>GS1</th>\n",
       "      <th>GS10</th>\n",
       "      <th>PCEPI</th>\n",
       "      <th>INDPRO</th>\n",
       "      <th>HOUST</th>\n",
       "      <th>AAA</th>\n",
       "      <th>NONREVSL</th>\n",
       "      <th>UNRATE</th>\n",
       "      <th>UEMPMEAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>869.437778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.090296</td>\n",
       "      <td>-0.037055</td>\n",
       "      <td>0.264085</td>\n",
       "      <td>-6.103375</td>\n",
       "      <td>-0.02250</td>\n",
       "      <td>0.032222</td>\n",
       "      <td>0.161917</td>\n",
       "      <td>0.056642</td>\n",
       "      <td>-0.777778</td>\n",
       "      <td>0.009444</td>\n",
       "      <td>3.656644</td>\n",
       "      <td>0.038889</td>\n",
       "      <td>0.547222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>605.777041</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.047287</td>\n",
       "      <td>0.007935</td>\n",
       "      <td>-0.038212</td>\n",
       "      <td>2.962274</td>\n",
       "      <td>-0.01618</td>\n",
       "      <td>-0.002022</td>\n",
       "      <td>0.135693</td>\n",
       "      <td>0.080759</td>\n",
       "      <td>5.670412</td>\n",
       "      <td>-0.006142</td>\n",
       "      <td>3.873767</td>\n",
       "      <td>0.023970</td>\n",
       "      <td>-0.005243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>715.369274</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.040424</td>\n",
       "      <td>-0.006764</td>\n",
       "      <td>0.017785</td>\n",
       "      <td>4.378010</td>\n",
       "      <td>0.01207</td>\n",
       "      <td>-0.003978</td>\n",
       "      <td>0.134226</td>\n",
       "      <td>0.160101</td>\n",
       "      <td>-4.642473</td>\n",
       "      <td>0.002957</td>\n",
       "      <td>4.574227</td>\n",
       "      <td>-0.025269</td>\n",
       "      <td>-0.028226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          SPX  Recession       GDP     SPDIV      SPPE  BUSLOANS      GS1  \\\n",
       "0  869.437778        0.0  0.090296 -0.037055  0.264085 -6.103375 -0.02250   \n",
       "1  605.777041        0.0  0.047287  0.007935 -0.038212  2.962274 -0.01618   \n",
       "2  715.369274        0.0 -0.040424 -0.006764  0.017785  4.378010  0.01207   \n",
       "\n",
       "       GS10     PCEPI    INDPRO     HOUST       AAA  NONREVSL    UNRATE  \\\n",
       "0  0.032222  0.161917  0.056642 -0.777778  0.009444  3.656644  0.038889   \n",
       "1 -0.002022  0.135693  0.080759  5.670412 -0.006142  3.873767  0.023970   \n",
       "2 -0.003978  0.134226  0.160101 -4.642473  0.002957  4.574227 -0.025269   \n",
       "\n",
       "   UEMPMEAN  \n",
       "0  0.547222  \n",
       "1 -0.005243  \n",
       "2 -0.028226  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro_data.groupby(gmm_clusters).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two models are very similar, and perform better than each other in the opposite categories.  This model does a much better job at predicting the prices of the early flat rates, howeever it does a poor job of predicting increase/decrease in price.  The \"better\" model so to say would be the one that is most relavant to the needs of whoever is utilizing them.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Extra Credit</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    Repeat tasks 5 and 6 under hierarchical clustering, also using 3 clusters. \n",
    "    <br><br>\n",
    "    Recall that we can build a hierarchical clustering model by calling <code>AgglomerativeClustering()</code> from the <code>sklearn.cluster</code> module, and using the <code>fit()</code> method. Additionally, set the following optional parameters for <code>AgglomerativeClustering()</code> to the values listed below:\n",
    "    <ul>\n",
    "        <li><code>affinity = 'euclidean'</code></li>\n",
    "        <li><code>linkage = 'average'</code></li>. \n",
    "    </ul>\n",
    "    Does the hierarchical clustering algorithm outperform k-means and GMM in identifying 3 different market regimes? If it did not, what about this dataset do you think is the reason? \n",
    "    <p style=\"text-align:right;\">[20pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SPX</th>\n",
       "      <th>Recession</th>\n",
       "      <th>GDP</th>\n",
       "      <th>SPDIV</th>\n",
       "      <th>SPPE</th>\n",
       "      <th>BUSLOANS</th>\n",
       "      <th>GS1</th>\n",
       "      <th>GS10</th>\n",
       "      <th>PCEPI</th>\n",
       "      <th>INDPRO</th>\n",
       "      <th>HOUST</th>\n",
       "      <th>AAA</th>\n",
       "      <th>NONREVSL</th>\n",
       "      <th>UNRATE</th>\n",
       "      <th>UEMPMEAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>682.756310</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001520</td>\n",
       "      <td>-0.002747</td>\n",
       "      <td>0.009328</td>\n",
       "      <td>3.254950</td>\n",
       "      <td>-0.002411</td>\n",
       "      <td>-0.00253</td>\n",
       "      <td>0.135958</td>\n",
       "      <td>0.124891</td>\n",
       "      <td>1.776786</td>\n",
       "      <td>-0.001488</td>\n",
       "      <td>4.253956</td>\n",
       "      <td>-0.002232</td>\n",
       "      <td>0.011756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>115.786667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.061073</td>\n",
       "      <td>0.038026</td>\n",
       "      <td>-0.116104</td>\n",
       "      <td>4.166267</td>\n",
       "      <td>0.326667</td>\n",
       "      <td>0.28000</td>\n",
       "      <td>0.209000</td>\n",
       "      <td>-0.255767</td>\n",
       "      <td>-478.333333</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>2.963033</td>\n",
       "      <td>-0.033333</td>\n",
       "      <td>-0.033333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          SPX  Recession       GDP     SPDIV      SPPE  BUSLOANS       GS1  \\\n",
       "0  682.756310        0.0  0.001520 -0.002747  0.009328  3.254950 -0.002411   \n",
       "1  115.786667        0.0 -0.061073  0.038026 -0.116104  4.166267  0.326667   \n",
       "\n",
       "      GS10     PCEPI    INDPRO       HOUST       AAA  NONREVSL    UNRATE  \\\n",
       "0 -0.00253  0.135958  0.124891    1.776786 -0.001488  4.253956 -0.002232   \n",
       "1  0.28000  0.209000 -0.255767 -478.333333  0.266667  2.963033 -0.033333   \n",
       "\n",
       "   UEMPMEAN  \n",
       "0  0.011756  \n",
       "1 -0.033333  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agglo_model = AgglomerativeClustering(affinity = 'euclidean', linkage = 'average')\n",
    "agglo_model = agglo_model.fit(macro_data[features])\n",
    "agglo_clusters = agglo_model.fit_predict(macro_data[features])\n",
    "macro_data.groupby(agglo_clusters).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 7))\n",
    "plt.plot(macro_data['SPX'], color = 'black')\n",
    "for i in range(3):\n",
    "    plt.scatter(macro_data.index[agglo_clusters == i], macro_data['SPX'][agglo_clusters == i], label = f'cluster {i}')\n",
    "plt.legend()\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('SPX Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This method does not outperform the GMM or K-means method.  I believe that this is due to the fact that this model has less means and less data to create a model out of, inherently making it more innacurate due to lack of data. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}