Cluster_Model_Code/Clustering_Stocks_corrected.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_datareader as pdr\n",
    "from math import sqrt\n",
    "from sklearn.cluster import KMeans\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy.spatial.distance import cdist\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#read in the url and scrape ticker data\n",
    "indx = 'SP500'\n",
    "source_table_name = 'Data/'+indx+'.csv'\n",
    "data_table = pd.read_csv(source_table_name)\n",
    "# data_table = data_table.head(4)\n",
    "# data_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ############################ run this once And save the results to a csv. Taked approx. 22 mins \n",
    "# ################### Comment after it has executed once. \n",
    "# # tickers = company_list\n",
    "# tickers = data_table['Symbol'].str.strip()\n",
    "\n",
    "# closing_prices_list = []\n",
    "# for ticker in tickers:\n",
    "#     try:\n",
    "#         prices = pdr.DataReader(ticker,'yahoo','10/24/2019')\n",
    "#         # print(prices)\n",
    "#         prices = prices['Close']\n",
    "#         prices = pd.DataFrame(prices)\n",
    "#         # print(prices)\n",
    "#         prices.columns = [ticker]\n",
    "#         closing_prices_list.append(prices)\n",
    "#         # print(closing_prices_list)\n",
    "#     except:\n",
    "#         pass\n",
    "#     # print(closing_prices_list)\n",
    "#     prices_df = pd.concat(closing_prices_list,axis=1)\n",
    "\n",
    "# prices_df.sort_index(inplace=True)\n",
    "# prices_df\n",
    "# prices_df.to_csv(\"Data/\"+indx+\"_Close_prices_df.csv\", index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(505, 503)"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# the data gathered in the above dataframe and saved to csv file is being retrieved here\n",
    "fromcsv = pd.read_csv(\"Data/\"+indx+\"_Close_prices_df.csv\")\n",
    "fromcsv=fromcsv.set_index('Date')\n",
    "fromcsv\n",
    "\n",
    "prices_df = fromcsv\n",
    "prices_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MMM', 'ABT', 'ABBV', 'ABMD', 'ACN', 'ATVI', 'ADBE', 'AMD', 'AAP',\n",
       "       'AES',\n",
       "       ...\n",
       "       'WLTW', 'WYNN', 'XEL', 'XLNX', 'XYL', 'YUM', 'ZBRA', 'ZBH', 'ZION',\n",
       "       'ZTS'],\n",
       "      dtype='object', length=503)"
      ]
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     "execution_count": 5,
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   ],
   "source": [
    "prices_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MMM</th>\n",
       "      <td>0.126259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABT</th>\n",
       "      <td>0.554449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABBV</th>\n",
       "      <td>0.415461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABMD</th>\n",
       "      <td>0.971716</td>\n",
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       "      <th>ACN</th>\n",
       "      <td>0.929405</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>YUM</th>\n",
       "      <td>0.153649</td>\n",
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       "    <tr>\n",
       "      <th>ZBRA</th>\n",
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       "      <th>ZBH</th>\n",
       "      <td>0.122699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZION</th>\n",
       "      <td>0.387497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZTS</th>\n",
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       "<p>503 rows × 1 columns</p>\n",
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      "text/plain": [
       "             0\n",
       "MMM   0.126259\n",
       "ABT   0.554449\n",
       "ABBV  0.415461\n",
       "ABMD  0.971716\n",
       "ACN   0.929405\n",
       "...        ...\n",
       "YUM   0.153649\n",
       "ZBRA  1.514829\n",
       "ZBH   0.122699\n",
       "ZION  0.387497\n",
       "ZTS   0.716917\n",
       "\n",
       "[503 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_change = (prices_df.diff().sum())/prices_df.iloc[0]\n",
    "per_change = per_change.to_frame()\n",
    "per_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Returns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MMM</th>\n",
       "      <td>0.126259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABT</th>\n",
       "      <td>0.554449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABBV</th>\n",
       "      <td>0.415461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABMD</th>\n",
       "      <td>0.971716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACN</th>\n",
       "      <td>0.929405</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YUM</th>\n",
       "      <td>0.153649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZBRA</th>\n",
       "      <td>1.514829</td>\n",
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       "      <th>ZBH</th>\n",
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      "text/plain": [
       "       Returns\n",
       "MMM   0.126259\n",
       "ABT   0.554449\n",
       "ABBV  0.415461\n",
       "ABMD  0.971716\n",
       "ACN   0.929405\n",
       "...        ...\n",
       "YUM   0.153649\n",
       "ZBRA  1.514829\n",
       "ZBH   0.122699\n",
       "ZION  0.387497\n",
       "ZTS   0.716917\n",
       "\n",
       "[503 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_change = per_change.rename(columns={0:'Returns'})\n",
    "per_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.sqrt(((prices_df['AAPL']-prices_df['AAPL'].mean())**2).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "vol = prices_df.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Returns</th>\n",
       "      <th>Volatility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MMM</th>\n",
       "      <td>0.126259</td>\n",
       "      <td>18.194670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABT</th>\n",
       "      <td>0.554449</td>\n",
       "      <td>15.160307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABBV</th>\n",
       "      <td>0.415461</td>\n",
       "      <td>12.394728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABMD</th>\n",
       "      <td>0.971716</td>\n",
       "      <td>65.090582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACN</th>\n",
       "      <td>0.929405</td>\n",
       "      <td>49.649354</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YUM</th>\n",
       "      <td>0.153649</td>\n",
       "      <td>15.086160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZBRA</th>\n",
       "      <td>1.514829</td>\n",
       "      <td>127.645795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZBH</th>\n",
       "      <td>0.122699</td>\n",
       "      <td>17.811862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZION</th>\n",
       "      <td>0.387497</td>\n",
       "      <td>10.929388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZTS</th>\n",
       "      <td>0.716917</td>\n",
       "      <td>26.173037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>503 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "       Returns  Volatility\n",
       "MMM   0.126259   18.194670\n",
       "ABT   0.554449   15.160307\n",
       "ABBV  0.415461   12.394728\n",
       "ABMD  0.971716   65.090582\n",
       "ACN   0.929405   49.649354\n",
       "...        ...         ...\n",
       "YUM   0.153649   15.086160\n",
       "ZBRA  1.514829  127.645795\n",
       "ZBH   0.122699   17.811862\n",
       "ZION  0.387497   10.929388\n",
       "ZTS   0.716917   26.173037\n",
       "\n",
       "[503 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_change['Volatility'] = vol\n",
    "per_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Returns</th>\n",
       "      <th>Volatility</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Returns, Volatility]\n",
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     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "\n",
    "per_change = per_change.fillna(0)\n",
    "per_change[per_change['Volatility'].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clusters with Unscaled Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vmuty\\anaconda3\\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=2.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "distortions = []\n",
    "inertia = []\n",
    "k = range(1, 11)\n",
    "# Calculate the inertia for the range of k values\n",
    "for i in k:\n",
    "    km = KMeans(n_clusters=i, random_state=0)\n",
    "    km.fit(per_change)\n",
    "    distortions.append(sum(np.min(cdist(per_change, km.cluster_centers_,\n",
    "                                        'euclidean'), axis=1)) / per_change.shape[0])\n",
    "    inertia.append(km.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the Elbow Curve\n",
    "elbow_data = {\"k\": k, \"inertia\": inertia}\n",
    "df_elbow = pd.DataFrame(elbow_data)\n",
    "plt.figure(figsize=(10, 5))\n",
    "# Plot the elbow curve to find the best candidate(s) for k\n",
    "plt.plot(df_elbow['k'], df_elbow['inertia'])\n",
    "# plt.plot(df_elbow['k'], distortions, 'bx-')\n",
    "plt.xticks(range(1,11))\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Inertia')\n",
    "plt.title('Elbow Method')\n",
    "\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looks like there or 4 or 5 clusters. Let's try scaling the data first"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clusters with Scaled Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(503, 2)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_data_scaled = StandardScaler().fit_transform(per_change)\n",
    "stock_data_scaled_df = pd.DataFrame(stock_data_scaled)\n",
    "stock_data_scaled_df.shape\n",
    "# stock_data_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vmuty\\anaconda3\\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=2.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "distortions = []\n",
    "inertia = []\n",
    "k = range(1, 11)\n",
    "# Calculate the inertia for the range of k values\n",
    "for i in k:\n",
    "    km = KMeans(n_clusters=i, random_state=0)\n",
    "    km.fit(stock_data_scaled)\n",
    "    distortions.append(sum(np.min(cdist(stock_data_scaled, km.cluster_centers_,\n",
    "                                        'euclidean'), axis=1)) / stock_data_scaled.shape[0])\n",
    "    inertia.append(km.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Kammsb5cCLh7Vm7+8uZq12/eEXY6IiIgkmIJamrvqzEEYxi9e0KiaiIhI1CiopbkenTrw2TF9ePTttazYsjvsckRERCSBFNQi4BvlA8nJzOCu5xeHXYqIiIgkkIJaBHQtyuOyU/vxxJx1LN64K+xyREREJEEU1CLiirEDKMzJ4o7nNKomIiISFQpqEdG5IIevnNGfZ+ZvYO7anWGXIyIiIgmgoBYhXz69P53ys7lj6qKwSxEREZEEUFCLkOK8bK4YO5Dpizbz9qptYZcjIiIiH5GCWsRcdmpfSgtz+dmzuldNREQk3SmoRUx+ThZXjh/Ia8u38urSLWGXIyIiIh+BgloEXTq6D9075vGz5xbh7mGXIyIiIh+SgloE5WVncvWEwcxavYPpizaFXY6IiIh8SApqEXXRSb3oU5LP7c8tprFRo2oiIiLpSEEtorIzM7j2rMHMX1fJs/M3hF2OiIiIfAgKahE2aURPBnUt5I6pi2nQqJqIiEjaUVCLsMwM4/qJQ1iyqYon57wfdjkiIiJyhBTUIu7cY7oxvHsxdz6/hLqGxrDLERERkSOgoBZxGRnGDWcPYdXWPTz+9tqwyxEREZEjoKDWDpw5rCsj+3Ti7mlLqKlvCLscERERaSUFtXbAzLjx7KGs21nNQ2+sDrscERERaSUFtXbi1IFdGDOghF9MX8beWo2qiYiIpAMFtXaiaVRtS1UND7y2MuxyREREpBUU1NqRUf1KKB96FL+ZsYxd1XVhlyMiIiKHoaDWztwwcSg79tTx+5dXhl2KiIiIHIaCWjtzXK+OnHNMGb97aTk79tSGXY6IiIgcgoJaO3T9xKFU1dbz2xeXh12KiIiIHIKCWjs0tFsRnzyhB/e/spLNu2rCLkdEREQOQkGtnbr2rCHUNjTy64plYZciIiIiB6Gg1k71Ly3gohN78eAbq1i/c2/Y5YiIiEgLFNTasasmDMLdueeFpWGXIiIiIi1QUGvHenXO59LRfXjkrTWs3ron7HJERETkAApq7dyV4weRmWHcNW1J2KWIiIjIARTU2rmy4jwuO7Uff5u9lqWbdoVdjoiIiMRRUBOuGDuADtmZ/Px5jaqJiIikklCCmpl1MrPHzGyhmS0ws1PMrMTMpprZkmDZOW7/75rZUjNbZGbnhFFzlHUpzOXLp/fnX++u5711lWGXIyIiIoGwRtTuAp5x92HACcAC4CZgmrsPBqYF3zGz4cBk4BjgXOBXZpYZStUR9tUzBlCcl8UdUxeFXYqIiIgE2jyomVkxMBa4D8Dda919BzAJeCDY7QHgwmB9EjDF3WvcfQWwFBjdljW3Bx07ZHPFuIE8v2ATs1dvD7scERERIZwRtQHAZuAPZjbbzH5nZgVAmbuvBwiWXYP9ewJr4o5fG7RJgn3x1H50KcjhjqmLwy5FREREAHP3tj2h2SjgdeA0d3/DzO4CKoGr3L1T3H7b3b2zmf0SeM3dHwza7wOecvfHW/jty4HLAcrKyk6aMmVKUvtSVVVFYWFhUs/R1p5dWcdDC2u5aXQevXL2Rq5/B4rin2G8qPcPot9H9S/9Rb2P6l9ijB8//m13H3Vge1bSz/xBa4G17v5G8P0xYvejbTSz7u6+3sy6A5vi9u8dd3wvYF1LP+zu9wL3AowaNcrLy8uTUP4+FRUVJPscbW1MXQMv3DadaZs68M2hGZHr34Gi+GcYL+r9g+j3Uf1Lf1Hvo/qXXG1+6dPdNwBrzGxo0DQBeA94ErgsaLsMeCJYfxKYbGa5ZtYfGAy82YYltyt52Zl868zBvLVyO/O2NIRdjoiISLsW1lOfVwF/NrN3gRHA/wK3ABPNbAkwMfiOu88HHiEW5p4BrnR3JYgkumRUb3p17sBfl9TR1pfGRUREZJ9Qgpq7v+Puo9z9eHe/0N23u/tWd5/g7oOD5ba4/W9294HuPtTdnw6j5vYkJyuDa88aworKRh58fVXY5YiIiLRbrb5HzczOJ/Yus7ymNnf/STKKkvB9emRP/jh9Hv/9rwWc3L+EYd2Kwy5JRESk3WnViJqZ/Qa4hNglSwMuBvomsS4JWUaG8dXjcunYIZur/jKbvbW62iwiItLWWnvp81R3/wKw3d1/DJzC/k9iSgQV5xo//7cRLN1cxU/++V7Y5YiIiLQ7rQ1qe4PlHjPrAdQB/ZNTkqSS0weXcsXYgTz05mqemrs+7HJERETaldYGtX+aWSfgNmAWsBJI7ttkJWXccPYQTujdiZsef5e12/eEXY6IiEi70aqg5u7/7e47gtkA+gLD3P2HyS1NUkV2Zgb3TB5Jo8M1U96hvqEx7JJERETahUMGNTM7M1h+uukDnA9MCNalnejTJZ+bP3Usb6/azt3TloRdjoiISLtwuNdzjANeAD7RwjYH/prwiiRlTRrRk5eWbOGe6Us5ZWAppwzsEnZJIiIikXbIoObuPwpWf+LuK+K3BdM5STvz408ew6xV27n24dk8fc1YSgpywi5JREQkslr7MMHjLbQ9lshCJD0U5GZx96Uj2b67ju88NkdTTImIiCTR4e5RG2ZmnwE6xt+nZmZfJG6GAmlfju3ZkZs+PoznF2zij69piikREZFkOdw9akOBC4BO7H+f2i7ga0mqSdLAl07rx8tLt3DzUws4uV8Jw3toiikREZFEO+SImrs/AXwVuN3dvxT3udrdX22bEiUVmRm3XXQ8nTpkc9VDs9hTWx92SSIiIpFz2HvU3L0BmNgGtUia6VKYy88vGcHyLbv5yT80xZSIiEiitfZhglfN7BdmdoaZndj0SWplkhZOG1TKN8YNZMpba/jHnHVhlyMiIhIph7tHrcmpwfIncW0OnJnYciQdXTdxCK8t38r3/jqXEb070bskP+ySREREIqG1U0iNb+GjkCZAbIqpuyePBODqKbOp0xRTIiIiCdGqoGZmZWZ2n5k9HXwfbmZfSW5pkk56l+Tzv58+jtmrd3Dn84vDLkdERCQSWnuP2v3As0CP4Pti4Nok1CNp7BMn9OCSUb35VcUyXl26JexyRERE0l5rg1qpuz8CNAK4ez3QkLSqJG396JPDGVBawLUPv8PWqpqwyxEREUlrrQ1qu82sC7EHCDCzMcDOpFUlaSs/J4t7Lj2RHXvq+PZj72qKKRERkY+gtUHteuBJYKCZvQL8EbgqaVVJWhveo5jvnTeMFxZu4g+vrAy7HBERkbTVqtdzuPssMxtHbEopAxa5e11SK5O0dtmpsSmmbnl6IaP7l3Bsz45hlyQiIpJ2WjuiBjAaOAE4EbjUzL6QnJIkCsyMWy86gc4F2Vz90Gx212iKKRERkSPV2tdz/An4GXA6cHLwGZXEuiQCSgpy+PklI1ixdTf/9eT8sMsRERFJO62dmWAUMNx1Z7gcoVMHlvKt8YO454WlnD64lEkjeoZdkoiISNpo7aXPeUC3ZBYi0XXNhMGc2KcT3//bPFZv3RN2OSIiImmj1e9RA94zs2fN7MmmTzILk+jIyszgrskjMYOrNMWUiIhIq7X20ud/JbMIib7eJfnc8unjufIvs7hj6mL+49xhYZckIiKS8lr7eo4ZyS5Eou/847vz8tLe/GbGMk4bWMrpg0vDLklERCSlHfLSp5ntMrPKFj67zKyyrYqU6PjPC45h4FGFXPfIO2zRFFMiIiKHdMig5u5F7l7cwqfI3YvbqkiJjg45mdxz6Uh27q3jxkfn0NioB4lFREQO5kheeCuSEEd3L+YH5x9NxaLN/P6VFWGXIyIikrIU1CQUnx/Tl4nDy/jpMwuZ9/7OsMsRERFJSQpqEgoz49bPHE9pYS5XPTSbKk0xJSIi8gEKahKazsEUU6u27uZHT2iKKRERkQMpqEmoxgzowrfOHMzjs9by99nvh12OiIhISlFQk9BdfeYgTu7Xme//bS6rtu4OuxwREZGUoaAmocvKzODOySPJzDCufmg2tfWaYkpERAQU1CRF9OzUgVsvOp45a3dy+3OLwi5HREQkJSioSco499jufPZjffjti8t5cfHmsMsREREJnYKapJQfXjCcIWWFXP/IHDbv0hRTIiLSvimoSUrJy87knktPZFd1HTdoiikREWnnQgtqZpZpZrPN7J/B9xIzm2pmS4Jl57h9v2tmS81skZmdE1bN0jaGdivihxcM58XFm/ndy8vDLkdERCQ0YY6oXQMsiPt+EzDN3QcD04LvmNlwYDJwDHAu8Cszy2zjWqWNffZjfTjnmDJufWYRc9bsCLscERGRUIQS1MysF3A+8Lu45knAA8H6A8CFce1T3L3G3VcAS4HRbVSqhMTM+OlnjqdrUS5XT9EUUyIi0j6FNaJ2J/AdIP6FWWXuvh4gWHYN2nsCa+L2Wxu0ScR1ys/hzskjWbNtDz/8+7ywyxEREWlz5t62N2ub2QXAee7+TTMrB2509wvMbIe7d4rbb7u7dzazXwKvufuDQft9wFPu/ngLv305cDlAWVnZSVOmTElqX6qqqigsLEzqOcKUKv37+9Ja/r60jq8dl8NpPbMT+tup0sdkiXr/IPp9VP/SX9T7qP4lxvjx499291EHtmcl/cwfdBrwSTM7D8gDis3sQWCjmXV39/Vm1h3YFOy/Fugdd3wvYF1LP+zu9wL3AowaNcrLy8uT1IWYiooKkn2OMKVK/84Y66z7v9f586Kd/Ps5p9K/tCBhv50qfUyWqPcPot9H9S/9Rb2P6l9ytfmlT3f/rrv3cvd+xB4SeMHdPwc8CVwW7HYZ8ESw/iQw2cxyzaw/MBh4s43LlhBlZhh3XjKC7MwMrnpolqaYEhGRdiOV3qN2CzDRzJYAE4PvuPt84BHgPeAZ4Ep3bwitSglFj2CKqXnvV3LbswvDLkdERKRNhHHps5m7VwAVwfpWYMJB9rsZuLnNCpOUdM4x3fj8mL7830srOHVQKeOHdj38QSIiImkslUbURA7r++cfzbBuRdz4yBw27aoOuxwREZGkUlCTtBKbYmoku2vruf5hTTElIiLRpqAmaWdwWRH/ecExvLx0C/e+pCmmREQkuhTUJC1dOro35x3XjZ89u4h3NMWUiIhElIKapCUz4/996njKivO4+qHZ7KquC7skERGRhFNQk7TVMT+buyaP4P0de/n+3+bR1rNsiIiIJJuCmqS1Uf1KuHbCYJ6cs47H3l4bdjkiIiIJpaAmae+b4wcxZkAJP3pyPss2V4VdjoiISMIoqEnai00xNZKcrAyufmg2NfWauEJERKJBQU0ioVvHPG676ATmr6vkp08vCrscERGRhFBQk8iYOLyMy07py+9fWcELCzeGXY6IiMhHpqAmkfLd84Ipph59l42VmmJKRETSm4KaREpedia/+PeR7Kmt57qH36FBU0yJiEgaU1CTyBnUtYj/+sQxvLpsK7+ZsSzsckRERD40BTWJpEtO7s35x3fnjqmLmbV6e9jliIiIfCgKahJJZsb/fuo4ugVTTFVqiikREUlDCmoSWR07ZHP3pSNZv7Oa7/11rqaYEhGRtKOgJpF2Ut/OXD9xCP98dz2PztQUUyIikl4U1CTyvj5uIKcM6MKPnpzP0k27wi5HRESk1RTUJPIyM4w7J48gLzuDqx56h+o6TTElIiLpQUFN2oWy4jx+dvEJLFhfyS1PLwy7HBERkVZRUJN2Y8LRZXzptH7c/+pKnn9PU0yJiEjqU1CTduWmjw9jePdivv3YHDbs1BRTIiKS2hTUpF3Jzcrknn8fSXVdI9c+PJtGvbJDRERSmIKatDsDjyrkx5OO4fXl2/jlOzWs3LI77JJERERapKAm7dLFJ/XixrOHMHdLA2fdMYPv/20uGyt1KVRERFJLVtgFiITBzPjWmYPpVbeGWdVd+csbq3l81lq+eGp/vjFuIB3zs8MuUURERCNq0r51ys3gJ5OO5YUbyvn4sd357YvLOOPWF/hVxVL21up9ayIiEi4FNRGgT5d8fn7JCJ6+5gxG9y/h1mcWMfa26fzp9VXUNTSGXZ6IiLRTCmoicYZ1K+Z3l53MY18/hf5dCvjh3+cx4fYZPPHO+zQ26glRERFpWwpqIi0Y1a+Eh68Ywx++dDIFuVlcM+Udzrv7JV5YuBHXKz1ERKSNKKiJHISZMX5oV/511encfelI9tY18OX7Z/Jvv32Nt1ZuC7s8ERFpBxTURA4jI8P45Ak9eP76cfzPhceyauseLv7Na3z5/rd4b11l2OWJiEiEKaiJtFJ2ZgafG9OXGd8ez3+cO4yZK7dx/j0vcc2U2azaqpfmiohI4imoiRyhDjmZfKN8IC9950y+MW4gz87fwITbZ/CDv89lk16aKyIiCaSgJvIhdczP5jvnDuPFb4/n0tF9mPLmGsbeNp2fPrOQnXvqwi5PREQiQEFN5CPqWpzHf194LNNuGMe5x3TjNzNiL839dcUyvTRXREQ+EgU1kQTp26WAOyeP5Kmrz+DkfiX89JmFjLttOg/qpbkiIvIhKaiJJNjR3Yu574sn8+jXT6Fvl3x+8Pd5nHWHXporIiJHTkFNJElO7lfCI1ecwh++eDIdsjO5Zso7nH/Py0xfuEkvzRURkVZRUBNJIjNj/LCuPHX1Gdw1eQS7a+r50v1vcclvX2emXporIiKHoaAm0gYyMoxJI3ry/PXj+O8Lj2XF1t1c9JvX+Mr9b7FgvV6aKyIiLWvzoGZmvc1supktMLP5ZnZN0F5iZlPNbEmw7Bx3zHfNbKmZLTKzc9q6ZpFEycnK4PNj+vJi8NLct1Zu47y7X+LaKbNZvXVP2OWJiEiKCWNErR64wd2PBsYAV5rZcOAmYJq7DwamBd8Jtk0GjgHOBX5lZpkh1C2SMPEvzf36uIE8M38DZ95ewQ//Pk8vzRURkWZtHtTcfb27zwrWdwELgJ7AJOCBYLcHgAuD9UnAFHevcfcVwFJgdJsWLZIkHfOz+Y/gpbmTR/fmoTdXM+62Cm59ZiE79+qluSIi7V2o96iZWT9gJPAGUObu6yEW5oCuwW49gTVxh60N2kQio2txHv9z4XFMu2EcZx9Txq8qljH21un8ZoZemisi0p5ZWK8JMLNCYAZws7v/1cx2uHunuO3b3b2zmf0SeM3dHwza7wOecvfHW/jNy4HLAcrKyk6aMmVKUvtQVVVFYWFhUs8Rpqj3D1K3j6srG3h8SR1zNjfQKdeYNDCbM3plkZVhR/Q7qdq/RIp6H9W/9Bf1Pqp/iTF+/Pi33X3Uge1ZST9zC8wsG3gc+LO7/zVo3mhm3d19vZl1BzYF7WuB3nGH9wLWtfS77n4vcC/AqFGjvLy8PBnlN6uoqCDZ5whT1PsHqd3HLwBvrtjGrc8s5IH3tjNjYxbXnz2EC47rTkYrA1sq9y9Rot5H9S/9Rb2P6l9yhfHUpwH3AQvc/Y64TU8ClwXrlwFPxLVPNrNcM+sPDAbebKt6RcI0un8Jj379FH7/xVHkZWdy9UOzueCel5m+SC/NFRFpD8IYUTsN+Dww18zeCdq+B9wCPGJmXwFWAxcDuPt8M3sEeI/YE6NXurtu2pF2w8w4c1gZ5UO68o9313H7c4v50h/eYnS/Er5z7lBG9SsJu0QREUmSNg9q7v4ycLDrNhMOcszNwM1JK0okDTS9NPfjx3bn4bdWc/cLS7noN69x1tFdufGcoQzrVhx2iSIikmCamUAkzeRkZfD5U/ox49vlfPucobyxYhsfv+slrnv4Hb00V0QkYhTURNJUfk4WV44fxEvfGc8VYwfy9Lz1TLijgv98Yh6bdumluSIiURDKU58ikjid8nO46ePD+NJp/bh72hL+8sZqHp25li+f3o/hGXrgQEQknSmoiUREWXEeN3/qOL52xgDumLqYX05fhgG3v1vB0d2KGdatiGHdY8tenTsQewBbRERSmYKaSMT0Ky3g7ktH8vVxA/ntv16nOq+Qeet28q+565v3KczNYmi3oubwdnS3IoZ2K6IoLzvEykVE5EAKaiIRNbxHMZ8anEN5eexF11U19SzeuIuF63excEMlC9fv4h9z1vHnN1Y3H9Orc4dYeOtWzLDusWW/LvlkZep2VhGRMCioibQThblZnNinMyf26dzc5u6s31nNwg2VLFi/i4UbdrFwfSXTF22moTF2f1tuVgZDyor2u3Q6rFsRXQpzw+qKiEi7oaAm0o6ZGT06daBHpw6cOaysub26roGlm6pYuGEXizZUsnDDLqYv2syjb69t3ueoolyGdSvi6ObwVszArgXkZmWG0RURkUhSUBORD8jLzuTYnh05tmfH/do376ph0YZdzSNwizZWcv+rK6mtbwQgK8MYcFRB86XTo4Nlt+I8PbwgIvIhKKiJSKsdVZTLUUW5nD64tLmtvqGRlVt3B5dOY/e+vb1qO0/OWde8T8cO2c2jb00PMQztVkR+jv4KEhE5FP0tKSIfSVZmBoO6FjGoaxGfOKFHc/vOvXXBwwuVLAjufXt05hp218am6jWDviX5+z24MKxbEX1K8snI0OibiAgoqIlIknTskM3J/Uo4OW7S+MZGZ+32vSwIRt4WbYwtn31vAx68mzc/J5MhZUUcHRfehnUrpmO+Xh0iIu2PgpqItJmMDKNPl3z6dMnnnGO6NbfvrW2Ijb41P31aydPzNvDQm2ua9+nRMY9hcZdOj+5eTP/SgjC6ISLSZhTURCR0HXIyOaF3J07o3am5zd3ZtKuGBesrm18bsnDDLl5cvJn64NUhOZkZlOY5x6yeyYCjChhQWkD/0kL6lxZQWpijBxhEJO0pqIlISjIzyorzKCvOo3xo1+b22vpGlm2uCp4+3cWbC1aycstuZizaTG1DY/N+RblZ9I8Pb8F6v9ICCnP1V5+IpAf9bSUiaSUnK4OjuxdzdPdiACo6bKC8fBwNjc66HXtZvmU3KzZXxZZbdvPWyu08MWdd8z1wAF2LchlwVCzAxYJcAf2PKqBPST7ZmoVBRFKIgpqIREJmhtG7JJ/eJfmMG3LUftuq6xpYtXUPK7ZUsWxzLMCt2LKbZ+dvYNvu2v1+o09Jfiy4BZ8BQYjTu+BEJAwKaiISeXnZmQwN3t12oB17almxZTfL4wLc8i27eXXZFqrr9l1K7ZCd2TzyNmC/IFeoJ1JFJGkU1ESkXeuUn8PIPjmMjJsDFWKvEtm4q5rlm3cHl1N3s2JLFfPf38kz8zY0z4UK0KUgZ98oXNx9cX275JOXrSm1ROTDU1ATEWlBRobRvWMHunfswGmDSvfbVlvfyOpte4IRuKrmEbkZi/efD9UMenbqsO8SamkB/Y+K3RfXo1MHMvViXxE5DAU1EZEjlJOVwaCuhQzqWgiU7bdtV3UdK7fsYXkQ4Jo+j896n6qa+v1+o1+XpvvhCuNeL1JASYFeLSIiMQpqIiIJVJSXzXG9OnJcr/0ntHd3NlfVBJdQ990Lt2zzbl5YuIm6hn2XUovzsppH3uora1mbt4qy4jy6FedR1jGXLgW5Go0TaScU1ERE2oCZ0bUoj65FeXxsQJf9ttU3NPJ+86tF9gW5N5ZvZf3OOv6xbN5++2dmGF2LculanEe34ly6FecF63l065hHWXEuZcV5FOZmaWROJM0pqImIhCwrM4O+XQro26WA8UP33zbthekcO+oUNuysZkNlNZsqY8uNlTVsrIw97PDasq1UVtd/4HfzczJjo3DFQXjrmEdZUVOYi7V1LcojJ0vvjhNJVQpqIiIpLDNj3wwNJxxivz219c3hremzYee+7zNXbWdTZc1+szc06VKQE7u0Gjcaty/gxdp035xIOBTUREQiID8ni/6lWYecqN7d2b6njg07q9m4q5qNO/cfndtYWc27a3ewpar2A8fmZGbQNS7EdQ0uuXbrGLuc261jrL1Djl5HIpJICmoiIu2EmVFSkENJQQ7DKT7ofrX1jWyuqmHDzn2XWmOXXWNtC9ZXMn1RNXtqGz5wbFFe1n6jcd065saNzMXCXGlhDlmaqkukVRTURERkPzlZGfTs1IGenToccr9d1XXNo3Hxo3QbK2vYUFnNsmVb2LSrZr+XAwNkGJQW5tKtYx5WU83j62dTmJtJQU4W+blZsfXcLApzs8jPyaIgN7N5vTA39r0gJ4sMPfkq7YCCmoiIfChFedkU5WUH75NrWUOjs3V3DRuD++XiH4jYUFnDqkpn59od7K5tYHdNfYujdAfTIbsp0GXuH+JysyjIydq3LXffelN7/HGx/TM1yicpSUFNRESSJvYqkdh9bMfR8QPbKyoqKC8vb/7e2OjsqYuFtqqaevbUNFBVU8/umnp219azuyZuW209VTVNAS/WtqWqllXb9sT2r2lgd2097h84bYtyszKCsLcvvO0b2du3XhA3qlcQFw4LDzguW8FPEkBBTUREUkZGhlEYhJ6yw+9+WO7O3rqmsNcQBLgPhr7dNQ3NYS+2PbZtx55a3t+xN26/ehpbGfxysjIoyMnEGuvpOLOC3KwMcrMzyWtxmUFeVub+y+xMcrP2LXMP+N7SUuEwehTUREQkssyM/JzY/W0UffTfc3dq6hv3Bbpg1K5p9K+l0b6Va96nc2lHqusaqKlvpLqugcq9dWyqa6A2+N7UXl3f+IF7+o5EZoa1GOIOHvIyyM3KPOwy9xDb6xsdd9frW5JEQU1ERKSVzIy87EzysjMpLcxt1TEVFVsoLx/Z6nPUNzRSXd9ITRDcauoaqK5rpKb+g8uauO/xge9gy8q9dVQnISDa1KdigTCrKRjG1nMym9bjt8WWOVkHtu9b32/bAcfnZWeQk7l/e05WRmSnVVNQExERSSFZmRkUZsbul2tLHzYgLliylJ69+1JT30htffz2YL2+kZq6RnbsqY3bZ//9WnoR85HKzrTm0JZ7kKDX6m1NI5FZmSzeVM+YugbyssN5R6CCmoiIiHzogFjRuJry8qGH3/EQGhud2oYPBrimoNcc7oJRwPj9ahsam4NjS8c3Hbu3roEde2tb3FZd13DIew+/cF69gpqIiIi0TxkZRl5GZhCGskOpob45KO4f4l59/U06dginJlBQExERESErM4OszAwKDrj1cF3HcN+xp+d4RURERFKUgpqIiIhIilJQExEREUlRCmoiIiIiKUpBTURERCRFKaiJiIiIpKi0CWpmdq6ZLTKzpWZ2U9j1iIiIiCRbWgQ1M8sEfgl8HBgOXGpmw8OtSkRERCS50iKoAaOBpe6+3N1rgSnApJBrEhEREUmqdAlqPYE1cd/XBm0iIiIikZUuU0hZC20fmD7VzC4HLg++VpnZoqRWBaXAliSfI0xR7x9Ev49R7x9Ev4/qX/qLeh/Vv8To21JjugS1tUDvuO+9gHUH7uTu9wL3tlVRZjbT3Ue11fnaWtT7B9HvY9T7B9Hvo/qX/qLeR/UvudLl0udbwGAz629mOcBk4MmQaxIRERFJqrQYUXP3ejP7FvAskAn83t3nh1yWiIiISFKlRVADcPengKfCruMAbXaZNSRR7x9Ev49R7x9Ev4/qX/qLeh/VvyQy9w/cky8iIiIiKSBd7lETERERaXcU1D4EM/u9mW0ys3lh15IMZtbbzKab2QIzm29m14RdUyKZWZ6ZvWlmc4L+/TjsmpLBzDLNbLaZ/TPsWpLBzFaa2Vwze8fMZoZdT6KZWScze8zMFgb/Wzwl7JoSycyGBn92TZ9KM7s27LoSycyuC/6OmWdmD5lZXtg1JZKZXRP0bX5U/uxa+vfdzErMbKqZLQmWnduyJgW1D+d+4Nywi0iieuAGdz8aGANcGbEpu2qAM939BGAEcK6ZjQm3pKS4BlgQdhFJNt7dR0T01QB3Ac+4+zDgBCL2Z+nui4I/uxHAScAe4G/hVpU4ZtYTuBoY5e7HEnsQbnK4VSWOmR0LfI3YzEEnABeY2eBwq0qI+/ngv+83AdPcfTAwLfjeZhTUPgR3fxHYFnYdyeLu6919VrC+i9g/EJGZCcJjqoKv2cEnUjdrmlkv4Hzgd2HXIkfOzIqBscB9AO5e6+47Qi0quSYAy9x9VdiFJFgW0MHMsoB8Wnj/Zxo7Gnjd3fe4ez0wA/hUyDV9ZAf5930S8ECw/gBwYVvWpKAmh2Rm/YCRwBshl5JQwWXBd4BNwFR3j1T/gDuB7wCNIdeRTA48Z2ZvB7OSRMkAYDPwh+Dy9e/MrCDsopJoMvBQ2EUkkru/D/wMWA2sB3a6+3PhVpVQ84CxZtbFzPKB89j/xfRRUubu6yE2kAF0bcuTK6jJQZlZIfA4cK27V4ZdTyK5e0NwyaUXMDoYxo8EM7sA2OTub4ddS5Kd5u4nAh8ndnl+bNgFJVAWcCLwa3cfCeymjS+3tJXgJeafBB4Nu5ZECu5jmgT0B3oABWb2uXCrShx3XwD8FJgKPAPMIXbbjCSYgpq0yMyyiYW0P7v7X8OuJ1mCy0kVROuew9OAT5rZSmAKcKaZPRhuSYnn7uuC5SZi9zaNDreihFoLrI0b6X2MWHCLoo8Ds9x9Y9iFJNhZwAp33+zudcBfgVNDrimh3P0+dz/R3ccSu1y4JOyakmSjmXUHCJab2vLkCmryAWZmxO6NWeDud4RdT6KZ2VFm1ilY70DsL9SFoRaVQO7+XXfv5e79iF1SesHdI/P/5AHMrMDMiprWgbOJXYqJBHffAKwxs6FB0wTgvRBLSqZLidhlz8BqYIyZ5Qd/p04gYg+EmFnXYNkH+DTR/HOE2JSVlwXrlwFPtOXJ02ZmglRiZg8B5UCpma0FfuTu94VbVUKdBnwemBvcxwXwvWB2iCjoDjxgZpnE/s/KI+4eyVdYRFgZ8LfYv39kAX9x92fCLSnhrgL+HFwaXA58KeR6Ei64t2kicEXYtSSau79hZo8Bs4hdEpxN9N7g/7iZdQHqgCvdfXvYBX1ULf37DtwCPGJmXyEWwC9u05o0M4GIiIhIatKlTxEREZEUpaAmIiIikqIU1ERERERSlIKaiIiISIpSUBMRERFJUQpqItLmzMzN7Pa47zea2X8l6LfvN7OLEvFbhznPxWa2wMymJ7MuM+tnZv9+5BWKSBQoqIlIGGqAT5tZadiFxAverddaXwG+6e7jk1VPoB9wREHtCPshIilMQU1EwlBP7OWf1x244cCRJzOrCpblZjbDzB4xs8VmdouZfdbM3jSzuWY2MO5nzjKzl4L9LgiOzzSz28zsLTN718yuiPvd6Wb2F2BuC/VcGvz+PDP7adD2n8DpwG/M7LYWjvlOcMwcM7ulhe0rm0KqmY0ys4pgfZyZvRN8ZgezL9wCnBG0XdfafgSzN/wrqGGemV3Smj8YEUktmplARMLyS+BdM7v1CI45ATia2LyCy4HfuftoM7uG2Jv8rw326weMAwYC081sEPAFYKe7n2xmucArZvZcsP9o4Fh3XxF/MjPrQWzi6ZOA7cBzZnahu//EzM4EbnT3mQcc83HgQuBj7r7HzEqOoH83EnvD+ytmVghUE5uM/UZ3bwqcl7emH2b2GWCdu58fHNfxCOoQkRShETURCYW7VwJ/BK4+gsPecvf17l4DLAOaAspcYuGsySPu3ujuS4gFumHE5gP9QjAt2htAF2BwsP+bB4a0wMlARTCxdj3wZ2DsYWo8C/iDu+8J+rntCPr3CnCHmV0NdArOeaDW9mMusZHFn5rZGe6+8wjqEJEUoaAmImG6k9i9XgVxbfUEfzcFk1nnxG2riVtvjPveyP5XCA6cG88BA65y9xHBp7+7NwW93Qepz1rZjwOPOdzcfM19BPKai3S/Bfgq0AF43cyGHeT3D9sPd19MbCRwLvD/gsu1IpJmFNREJDTBaNMjxMJak5XEAgbAJCD7Q/z0xWaWEdy3NgBYBDwLfMPMsgHMbIiZFRzqR4iNWI0zs9LgBv1LgRmHOeY54MvBhOMc5NLnSvb18TNNjWY20N3nuvtPgZnERgJ3AUVxx7aqH8Fl2z3u/iDwM+DEw9QtIilI96iJSNhuB74V9/3/gCfM7E1gGgcf7TqURcQCVRnwdXevNrPfEbs8OisYqdtM7F6yg3L39Wb2XWA6sZGsp9z9icMc84yZjQBmmlkt8BTwvQN2+zFwn5l9j1gYbHKtmY0HGoD3gKeJjRbWm9kc4H7grlb24zjgNjNrBOqAbxyqbhFJTeZ+uBF6EREREQmDLn2KiIiIpCgFNREREZEUpaAmIiIikqIU1ERERERSlIKaiIiISIpSUBMRERFJUQpqIiIiIilKQU1EREQkRf1/gbmIvZk+Xk0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the Elbow Curve using hvPlot\n",
    "elbow_data = {\"k\": k, \"inertia\": inertia}\n",
    "df_elbow = pd.DataFrame(elbow_data)\n",
    "plt.figure(figsize=(10, 5))\n",
    "# Plot the elbow curve to find the best candidate(s) for k\n",
    "plt.plot(df_elbow['k'], df_elbow['inertia'])\n",
    "# plt.plot(df_elbow['k'], distortions, 'bx-')\n",
    "\n",
    "plt.xticks(range(1,11))\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Inertia')\n",
    "plt.title('Elbow Method')\n",
    "\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting clusters with k=4\n",
    "\n",
    "# Initialize the k-means model\n",
    "model = KMeans(n_clusters=4, random_state=0)\n",
    "\n",
    "# Fit the model\n",
    "model.fit(stock_data_scaled)\n",
    "\n",
    "# Predict clusters\n",
    "predictions = model.predict(stock_data_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the predicted cluster column to the dataframe (name it class)\n",
    "per_change[\"Cluster\"] = model.labels_\n",
    "per_change[\"Cluster\"].unique()\n",
    "centroids = model.cluster_centers_\n",
    "centroids_x = centroids[:,0]\n",
    "centroids_y = centroids[:,1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 4))\n",
    "plt.scatter(x=per_change['Returns'], y=per_change['Volatility'], c=per_change['Cluster'])\n",
    "\n",
    "# plt.title('Clusters')\n",
    "ax = plt.gca()\n",
    "ax.axes.xaxis.set_visible(False)\n",
    "ax.axes.yaxis.set_visible(False)\n",
    "plt.savefig(\"../static/Images/\"+indx+\"_PCV_clusters.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Stock</th>\n",
       "      <th>Returns</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MMM</td>\n",
       "      <td>0.126259</td>\n",
       "      <td>18.194670</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NEM</td>\n",
       "      <td>0.492583</td>\n",
       "      <td>9.041496</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NWL</td>\n",
       "      <td>0.164267</td>\n",
       "      <td>4.830464</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NTAP</td>\n",
       "      <td>0.685663</td>\n",
       "      <td>16.292702</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NDAQ</td>\n",
       "      <td>0.990400</td>\n",
       "      <td>30.552903</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>AZO</td>\n",
       "      <td>0.626809</td>\n",
       "      <td>212.289068</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>1.205945</td>\n",
       "      <td>46.085666</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>CHTR</td>\n",
       "      <td>0.673478</td>\n",
       "      <td>97.925162</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>BBWI</td>\n",
       "      <td>3.991725</td>\n",
       "      <td>19.445796</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>NVDA</td>\n",
       "      <td>3.734329</td>\n",
       "      <td>51.165780</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>503 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Stock   Returns  Volatility  Cluster\n",
       "0     MMM  0.126259   18.194670        0\n",
       "1     NEM  0.492583    9.041496        0\n",
       "2     NWL  0.164267    4.830464        0\n",
       "3    NTAP  0.685663   16.292702        0\n",
       "4    NDAQ  0.990400   30.552903        0\n",
       "..    ...       ...         ...      ...\n",
       "498   AZO  0.626809  212.289068        3\n",
       "499  MSFT  1.205945   46.085666        3\n",
       "500  CHTR  0.673478   97.925162        3\n",
       "501  BBWI  3.991725   19.445796        3\n",
       "502  NVDA  3.734329   51.165780        3\n",
       "\n",
       "[503 rows x 4 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_change = per_change.sort_values(by='Cluster')\n",
    "per_change = per_change.reset_index()\n",
    "per_change = per_change.rename(columns={'index':'Stock'})\n",
    "per_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = \"Data/Analyzed/\"+indx+\"_Clustered_Stocks.csv\"\n",
    "per_change.to_csv(file_path)"
   ]
  }
 ],
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