2 - Matrices de correlación.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6420440a",
   "metadata": {},
   "source": [
    "# <font color='blue'> Matrices de correlación locales </font>\n",
    "\n",
    "El objetivo de este notebook es ejemplificar el cálculo, acomodo y graficación de matrices de correlación de forma local."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e78ade1",
   "metadata": {},
   "source": [
    "## <font color='crimson'> Importar los datos </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "96cb951e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e7f779a",
   "metadata": {},
   "source": [
    "**Datos disponibles en csv**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2af090ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "returns^MXX_from2008-09-15_to2022-05-17_downloaded20230517.csv\r\n",
      "returns^MXX_from2008-09-15_to2022-06-16_downloaded20220616.csv\r\n",
      "returns_SP500.csv\r\n",
      "stockPrices_downloaded20230517.csv\r\n"
     ]
    }
   ],
   "source": [
    "dataLocation='./data'\n",
    "!ls {dataLocation} | grep .csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9fecd16b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LABB.MX</th>\n",
       "      <th>GENTERA.MX</th>\n",
       "      <th>WMT.MX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-12-30</th>\n",
       "      <td>25.710072</td>\n",
       "      <td>18.476227</td>\n",
       "      <td>650.904663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-03</th>\n",
       "      <td>26.457203</td>\n",
       "      <td>18.545977</td>\n",
       "      <td>657.174255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-04</th>\n",
       "      <td>26.518732</td>\n",
       "      <td>18.427408</td>\n",
       "      <td>657.174255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>27.239491</td>\n",
       "      <td>17.939169</td>\n",
       "      <td>654.815674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-06</th>\n",
       "      <td>27.511972</td>\n",
       "      <td>17.688078</td>\n",
       "      <td>647.121521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-10</th>\n",
       "      <td>18.047804</td>\n",
       "      <td>14.037169</td>\n",
       "      <td>3033.468262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-11</th>\n",
       "      <td>17.664007</td>\n",
       "      <td>14.377904</td>\n",
       "      <td>2993.501709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-12</th>\n",
       "      <td>17.439342</td>\n",
       "      <td>13.774435</td>\n",
       "      <td>2983.010498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-13</th>\n",
       "      <td>17.982275</td>\n",
       "      <td>13.985171</td>\n",
       "      <td>2962.527344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-16</th>\n",
       "      <td>18.384792</td>\n",
       "      <td>14.492851</td>\n",
       "      <td>2995.489990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2854 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              LABB.MX  GENTERA.MX       WMT.MX\n",
       "2010-12-30  25.710072   18.476227   650.904663\n",
       "2011-01-03  26.457203   18.545977   657.174255\n",
       "2011-01-04  26.518732   18.427408   657.174255\n",
       "2011-01-05  27.239491   17.939169   654.815674\n",
       "2011-01-06  27.511972   17.688078   647.121521\n",
       "...               ...         ...          ...\n",
       "2022-05-10  18.047804   14.037169  3033.468262\n",
       "2022-05-11  17.664007   14.377904  2993.501709\n",
       "2022-05-12  17.439342   13.774435  2983.010498\n",
       "2022-05-13  17.982275   13.985171  2962.527344\n",
       "2022-05-16  18.384792   14.492851  2995.489990\n",
       "\n",
       "[2854 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataName='stockPrices_downloaded20230517.csv'\n",
    "\n",
    "data=pd.read_csv(\n",
    "    f'{dataLocation}/{dataName}',\n",
    "    index_col=0,\n",
    "    parse_dates=True\n",
    ")\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae1ed05d",
   "metadata": {},
   "source": [
    "En un `DataFrame` de Pandas ya es más fácil calcular, por ejemplo, los rendimientos (logarítmicos):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1f320d52",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LABB.MX</th>\n",
       "      <th>GENTERA.MX</th>\n",
       "      <th>WMT.MX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-03</th>\n",
       "      <td>0.028646</td>\n",
       "      <td>0.003768</td>\n",
       "      <td>0.009586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-04</th>\n",
       "      <td>0.002323</td>\n",
       "      <td>-0.006414</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>0.026816</td>\n",
       "      <td>-0.026853</td>\n",
       "      <td>-0.003595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-06</th>\n",
       "      <td>0.009953</td>\n",
       "      <td>-0.014096</td>\n",
       "      <td>-0.011820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-07</th>\n",
       "      <td>0.007955</td>\n",
       "      <td>-0.036952</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-10</th>\n",
       "      <td>-0.023072</td>\n",
       "      <td>-0.003339</td>\n",
       "      <td>-0.023439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-11</th>\n",
       "      <td>-0.021495</td>\n",
       "      <td>0.023984</td>\n",
       "      <td>-0.013263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-12</th>\n",
       "      <td>-0.012800</td>\n",
       "      <td>-0.042878</td>\n",
       "      <td>-0.003511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-13</th>\n",
       "      <td>0.030658</td>\n",
       "      <td>0.015183</td>\n",
       "      <td>-0.006890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-16</th>\n",
       "      <td>0.022137</td>\n",
       "      <td>0.035658</td>\n",
       "      <td>0.011065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2853 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             LABB.MX  GENTERA.MX    WMT.MX\n",
       "2011-01-03  0.028646    0.003768  0.009586\n",
       "2011-01-04  0.002323   -0.006414  0.000000\n",
       "2011-01-05  0.026816   -0.026853 -0.003595\n",
       "2011-01-06  0.009953   -0.014096 -0.011820\n",
       "2011-01-07  0.007955   -0.036952  0.000000\n",
       "...              ...         ...       ...\n",
       "2022-05-10 -0.023072   -0.003339 -0.023439\n",
       "2022-05-11 -0.021495    0.023984 -0.013263\n",
       "2022-05-12 -0.012800   -0.042878 -0.003511\n",
       "2022-05-13  0.030658    0.015183 -0.006890\n",
       "2022-05-16  0.022137    0.035658  0.011065\n",
       "\n",
       "[2853 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataLogRets=np.log(data).diff(axis=0).iloc[1:]\n",
    "dataLogRets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "19b95c9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: >, <Axes: >, <Axes: >], dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataLogRets.plot(\n",
    "    subplots=True,\n",
    "    grid=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "642042d1",
   "metadata": {},
   "source": [
    "## <font color='crimson'> Matriz de correlación </font>\n",
    "\n",
    "### Global"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51cd8b23",
   "metadata": {},
   "source": [
    "Primero calculamos la matriz de correlación. Las instancias `DataFrame` de Pandas tienen un método útil: `DataFrame.corr()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "be407aba",
   "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>LABB.MX</th>\n",
       "      <th>GENTERA.MX</th>\n",
       "      <th>WMT.MX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LABB.MX</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.187453</td>\n",
       "      <td>-0.030994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENTERA.MX</th>\n",
       "      <td>0.187453</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.011101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMT.MX</th>\n",
       "      <td>-0.030994</td>\n",
       "      <td>-0.011101</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             LABB.MX  GENTERA.MX    WMT.MX\n",
       "LABB.MX     1.000000    0.187453 -0.030994\n",
       "GENTERA.MX  0.187453    1.000000 -0.011101\n",
       "WMT.MX     -0.030994   -0.011101  1.000000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrGlob=dataLogRets.corr()\n",
    "corrGlob"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95969005",
   "metadata": {},
   "source": [
    "Clase auxiliar para graficar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a67f2ee4",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MidpointNormalize(matplotlib.colors.Normalize):\n",
    "    def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):\n",
    "        self.midpoint = midpoint\n",
    "        matplotlib.colors.Normalize.__init__(self, vmin, vmax, clip)\n",
    "\n",
    "    def __call__(self, value, clip=None):\n",
    "        x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]\n",
    "        return np.ma.masked_array(np.interp(value, x, y), np.isnan(value))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe866db1",
   "metadata": {},
   "source": [
    "Graficamos con unos colores bien bergas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6b46d138",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "coloresCorr='seismic_r' #plt.cm.BuPu_r,'cool', 'bwr_r'\n",
    "normaCorr=MidpointNormalize(\n",
    "    midpoint=0.,vmin=-1,vmax=1\n",
    ")\n",
    "\n",
    "fig, axes = plt.subplots(\n",
    "    nrows=1, ncols=1, \n",
    "    figsize=(8,8)\n",
    ")\n",
    "\n",
    "cax=axes.matshow(\n",
    "    corrGlob,\n",
    "    cmap=coloresCorr\n",
    ")\n",
    "fig.colorbar(cax,shrink=0.82)\n",
    "\n",
    "axes.set_title(\n",
    "    'Componentes: {}'.format(\n",
    "        ','.join(data.columns.values)\n",
    "    ),\n",
    "    fontsize=15\n",
    ")\n",
    "\n",
    "plt.suptitle(\n",
    "    'Matriz de correlación ({:%m/%Y} - {:%m/%Y})'.format(\n",
    "        dataLogRets.index[0],\n",
    "        dataLogRets.index[-1]\n",
    "    ),\n",
    "    fontsize=18\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce7f23ad",
   "metadata": {},
   "source": [
    "Se puede guardar la tabla de los rendimientos logarítmicos en formato `CSV`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "84cc1a90",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataLogRets.to_csv(f'{dataLocation}/rendimientos.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef5ff324",
   "metadata": {},
   "source": [
    "### Local"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ed269f70",
   "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>LABB.MX</th>\n",
       "      <th>GENTERA.MX</th>\n",
       "      <th>WMT.MX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>0.039637</td>\n",
       "      <td>-0.047591</td>\n",
       "      <td>-0.005521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-0.045430</td>\n",
       "      <td>0.011257</td>\n",
       "      <td>0.035452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>-0.008753</td>\n",
       "      <td>-0.008431</td>\n",
       "      <td>0.016725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>-0.029742</td>\n",
       "      <td>-0.041619</td>\n",
       "      <td>0.038218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>-0.015209</td>\n",
       "      <td>-0.002291</td>\n",
       "      <td>0.010683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-22</th>\n",
       "      <td>0.000987</td>\n",
       "      <td>0.013097</td>\n",
       "      <td>0.024988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-26</th>\n",
       "      <td>0.008841</td>\n",
       "      <td>-0.011215</td>\n",
       "      <td>0.011756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-27</th>\n",
       "      <td>0.014081</td>\n",
       "      <td>0.011215</td>\n",
       "      <td>-0.013439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28</th>\n",
       "      <td>-0.006773</td>\n",
       "      <td>0.003711</td>\n",
       "      <td>0.007322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-29</th>\n",
       "      <td>-0.003891</td>\n",
       "      <td>0.011660</td>\n",
       "      <td>-0.003072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>503 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             LABB.MX  GENTERA.MX    WMT.MX\n",
       "2016-01-04  0.039637   -0.047591 -0.005521\n",
       "2016-01-05 -0.045430    0.011257  0.035452\n",
       "2016-01-06 -0.008753   -0.008431  0.016725\n",
       "2016-01-07 -0.029742   -0.041619  0.038218\n",
       "2016-01-08 -0.015209   -0.002291  0.010683\n",
       "...              ...         ...       ...\n",
       "2017-12-22  0.000987    0.013097  0.024988\n",
       "2017-12-26  0.008841   -0.011215  0.011756\n",
       "2017-12-27  0.014081    0.011215 -0.013439\n",
       "2017-12-28 -0.006773    0.003711  0.007322\n",
       "2017-12-29 -0.003891    0.011660 -0.003072\n",
       "\n",
       "[503 rows x 3 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dateFirst='2016'\n",
    "dateLast='2017'\n",
    "\n",
    "dataLogRets.loc[dateFirst:dateLast]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8caf0ff4",
   "metadata": {},
   "source": [
    "Calcular la correlación correspondiente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ba38880b",
   "metadata": {},
   "outputs": [],
   "source": [
    "corrLoc=dataLogRets.loc[dateFirst:dateLast].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c0836a6",
   "metadata": {},
   "source": [
    "Grafiquita:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "aaa4ff5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "fig, axes = plt.subplots(\n",
    "    nrows=1, ncols=1, \n",
    "    figsize=(8,8)\n",
    ")\n",
    "\n",
    "cax=axes.matshow(\n",
    "    corrLoc,\n",
    "    cmap=coloresCorr\n",
    ")\n",
    "fig.colorbar(cax,shrink=0.82)\n",
    "\n",
    "axes.set_title(\n",
    "    'Componentes: {}'.format(\n",
    "        ','.join(data.columns.values)\n",
    "    ),\n",
    "    fontsize=15\n",
    ")\n",
    "\n",
    "plt.suptitle(\n",
    "    'Matriz de correlación local ({} - {})'.format(\n",
    "        dateFirst,\n",
    "        dateLast\n",
    "    ),\n",
    "    fontsize=18\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb76232d",
   "metadata": {},
   "source": [
    "**¿Qué tanto difieren las matrices local y global?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "93bfe4c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LABB.MX</th>\n",
       "      <th>GENTERA.MX</th>\n",
       "      <th>WMT.MX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LABB.MX</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028731</td>\n",
       "      <td>0.017928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENTERA.MX</th>\n",
       "      <td>0.028731</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.105062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMT.MX</th>\n",
       "      <td>0.017928</td>\n",
       "      <td>0.105062</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "             LABB.MX  GENTERA.MX    WMT.MX\n",
       "LABB.MX     0.000000    0.028731  0.017928\n",
       "GENTERA.MX  0.028731    0.000000  0.105062\n",
       "WMT.MX      0.017928    0.105062  0.000000"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(corrLoc-corrGlob).abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "059028b4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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