PortfolioConstruction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import numpy as np\n",
    "from importlib import reload \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import tickers\n",
    "import tickers\n",
    "reload(tickers)\n",
    "from tickers import japan_tickers, china_tickers, us_tickers, ticker_id, swiss_tickers\n",
    "\n",
    "import stock_analysis_functions\n",
    "reload(stock_analysis_functions)\n",
    "from stock_analysis_functions import extract_max\n",
    "\n",
    "import stock_analysis_functions\n",
    "reload(stock_analysis_functions)\n",
    "from stock_analysis_functions import returns, data_from_yahoo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_names = ['China', 'USA', 'Japan', 'Switzerland']\n",
    "\n",
    "start = dt.datetime(2020, 1, 1)\n",
    "end = dt.datetime(2020, 12, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets_chn = data_from_yahoo(china_tickers, ticker_id, start, end, 'china', read=True)\n",
    "datasets_us = data_from_yahoo(us_tickers, ticker_id, start, end, 'usa', read=True)\n",
    "datasets_jpn = data_from_yahoo(japan_tickers, ticker_id, start, end, 'japan', read=True)\n",
    "datasets_swiss = data_from_yahoo(swiss_tickers, ticker_id, start, end, 'swiss', read=True)\n",
    "\n",
    "adj_close = {'China':datasets_chn['adj_close'], 'USA':datasets_us['adj_close'], 'Japan':datasets_jpn['adj_close'], 'Switzerland':datasets_swiss['adj_close']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = adj_close['China']['Date'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_return_china = returns(adj_close['China'].columns, adj_close['China'], index, 'China', True, True)\n",
    "df_return_usa = returns(adj_close['USA'].columns, adj_close['USA'], index, 'USA', True, True)\n",
    "df_return_japan = returns(adj_close['Japan'].columns, adj_close['Japan'], index, 'Japan', True, True)\n",
    "df_return_swiss = returns(adj_close['Switzerland'].columns, adj_close['Switzerland'], index, 'Switzerland', True, True)\n",
    "\n",
    "df_returns = {'China':df_return_china, 'USA':df_return_usa, 'Japan':df_return_japan, 'Switzerland':df_return_swiss}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CHINA  <a class=\"anchor\" id=\"china\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "country = 'China'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>TCEHY</th>\n",
       "      <th>BABA</th>\n",
       "      <th>MOUTAI</th>\n",
       "      <th>1398.HK</th>\n",
       "      <th>PNGAY</th>\n",
       "      <th>PDD</th>\n",
       "      <th>CICHY</th>\n",
       "      <th>3968.HK</th>\n",
       "      <th>ACGBY</th>\n",
       "      <th>JD</th>\n",
       "      <th>...</th>\n",
       "      <th>WXXWY</th>\n",
       "      <th>600309.SS</th>\n",
       "      <th>2202.HK</th>\n",
       "      <th>601633.SS</th>\n",
       "      <th>300015.SZ</th>\n",
       "      <th>600887.SS</th>\n",
       "      <th>BKFCF</th>\n",
       "      <th>AS7.F</th>\n",
       "      <th>6862.HK</th>\n",
       "      <th>300059.SZ</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-1.704096</td>\n",
       "      <td>-1.260411</td>\n",
       "      <td>-4.552203</td>\n",
       "      <td>-0.826445</td>\n",
       "      <td>-0.370216</td>\n",
       "      <td>-0.824643</td>\n",
       "      <td>-2.328221</td>\n",
       "      <td>0.121217</td>\n",
       "      <td>-1.160716</td>\n",
       "      <td>0.689112</td>\n",
       "      <td>...</td>\n",
       "      <td>0.204510</td>\n",
       "      <td>1.878690</td>\n",
       "      <td>-2.911204</td>\n",
       "      <td>-0.326084</td>\n",
       "      <td>-0.278480</td>\n",
       "      <td>-0.031497</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.243900</td>\n",
       "      <td>2.318386</td>\n",
       "      <td>0.628935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>-0.530282</td>\n",
       "      <td>-0.165899</td>\n",
       "      <td>-0.052854</td>\n",
       "      <td>-1.166668</td>\n",
       "      <td>0.289024</td>\n",
       "      <td>-1.956467</td>\n",
       "      <td>-0.465116</td>\n",
       "      <td>-0.968520</td>\n",
       "      <td>-0.813006</td>\n",
       "      <td>0.026318</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.592189</td>\n",
       "      <td>-3.055843</td>\n",
       "      <td>-1.349316</td>\n",
       "      <td>-2.290075</td>\n",
       "      <td>-2.157905</td>\n",
       "      <td>-2.047885</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.963195</td>\n",
       "      <td>-2.265855</td>\n",
       "      <td>0.937492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>2.070939</td>\n",
       "      <td>0.456982</td>\n",
       "      <td>1.534335</td>\n",
       "      <td>-0.337271</td>\n",
       "      <td>-0.452867</td>\n",
       "      <td>2.793711</td>\n",
       "      <td>-1.168230</td>\n",
       "      <td>-0.122254</td>\n",
       "      <td>-0.819670</td>\n",
       "      <td>0.842104</td>\n",
       "      <td>...</td>\n",
       "      <td>3.993570</td>\n",
       "      <td>-0.760872</td>\n",
       "      <td>0.607896</td>\n",
       "      <td>2.120529</td>\n",
       "      <td>1.686559</td>\n",
       "      <td>3.409461</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.857844</td>\n",
       "      <td>3.554867</td>\n",
       "      <td>-0.061918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>-0.261146</td>\n",
       "      <td>0.170011</td>\n",
       "      <td>-0.583807</td>\n",
       "      <td>-1.184426</td>\n",
       "      <td>-0.330858</td>\n",
       "      <td>-1.722881</td>\n",
       "      <td>0.236401</td>\n",
       "      <td>-2.325578</td>\n",
       "      <td>-0.367304</td>\n",
       "      <td>-0.052193</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.153577</td>\n",
       "      <td>-0.967508</td>\n",
       "      <td>-0.604223</td>\n",
       "      <td>-1.857923</td>\n",
       "      <td>-0.969630</td>\n",
       "      <td>0.124408</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.683773</td>\n",
       "      <td>1.343291</td>\n",
       "      <td>-3.717463</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               TCEHY      BABA    MOUTAI   1398.HK     PNGAY       PDD  \\\n",
       "Date                                                                     \n",
       "2020-01-02       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2020-01-03 -1.704096 -1.260411 -4.552203 -0.826445 -0.370216 -0.824643   \n",
       "2020-01-06 -0.530282 -0.165899 -0.052854 -1.166668  0.289024 -1.956467   \n",
       "2020-01-07  2.070939  0.456982  1.534335 -0.337271 -0.452867  2.793711   \n",
       "2020-01-08 -0.261146  0.170011 -0.583807 -1.184426 -0.330858 -1.722881   \n",
       "\n",
       "               CICHY   3968.HK     ACGBY        JD  ...     WXXWY  600309.SS  \\\n",
       "Date                                                ...                        \n",
       "2020-01-02       NaN       NaN       NaN       NaN  ...       NaN        NaN   \n",
       "2020-01-03 -2.328221  0.121217 -1.160716  0.689112  ...  0.204510   1.878690   \n",
       "2020-01-06 -0.465116 -0.968520 -0.813006  0.026318  ... -3.592189  -3.055843   \n",
       "2020-01-07 -1.168230 -0.122254 -0.819670  0.842104  ...  3.993570  -0.760872   \n",
       "2020-01-08  0.236401 -2.325578 -0.367304 -0.052193  ... -0.153577  -0.967508   \n",
       "\n",
       "             2202.HK  601633.SS  300015.SZ  600887.SS  BKFCF     AS7.F  \\\n",
       "Date                                                                     \n",
       "2020-01-02       NaN        NaN        NaN        NaN    NaN       NaN   \n",
       "2020-01-03 -2.911204  -0.326084  -0.278480  -0.031497    0.0 -1.243900   \n",
       "2020-01-06 -1.349316  -2.290075  -2.157905  -2.047885    0.0 -0.963195   \n",
       "2020-01-07  0.607896   2.120529   1.686559   3.409461    0.0  6.857844   \n",
       "2020-01-08 -0.604223  -1.857923  -0.969630   0.124408    0.0 -2.683773   \n",
       "\n",
       "             6862.HK  300059.SZ  \n",
       "Date                             \n",
       "2020-01-02       NaN        NaN  \n",
       "2020-01-03  2.318386   0.628935  \n",
       "2020-01-06 -2.265855   0.937492  \n",
       "2020-01-07  3.554867  -0.061918  \n",
       "2020-01-08  1.343291  -3.717463  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_returns[country].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EQUALLY WEIGHTED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_returns[country] = df_returns[country].iloc[1:] # ignore first row\n",
    "ew_portfolio = df_returns[country].mean(axis=1).values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VALUE WEIGHTED\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# names two groups\n",
    "stocks_60_names =  ['MOUTAI', 'MINDRAY', 'BABA', 'CTGDF', 'FOSHAN']\n",
    "stocks_40_names = [ stock for stock in df_returns[country].columns if stock not in stocks_60_names]  \n",
    "\n",
    "# stocks value two groups\n",
    "stocks_60 = df_returns[country][stocks_60_names]\n",
    "stocks_40 = df_returns[country][stocks_40_names]\n",
    "\n",
    "# mean each group\n",
    "stocks_60_mean = stocks_60.mean(axis=1).values\n",
    "stocks_40_mean = stocks_40.mean(axis=1).values\n",
    "\n",
    "# weight 60 40:\n",
    "vw_portfolio = stocks_60_mean * 0.6 + stocks_40_mean * 0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>EW</th>\n",
       "      <th>VW</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.567323</td>\n",
       "      <td>-1.190934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>-0.882252</td>\n",
       "      <td>-0.811192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>0.772575</td>\n",
       "      <td>1.187298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>-0.519914</td>\n",
       "      <td>-0.246405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-09</th>\n",
       "      <td>1.819714</td>\n",
       "      <td>2.245515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  EW        VW\n",
       "Date                          \n",
       "2020-01-03 -0.567323 -1.190934\n",
       "2020-01-06 -0.882252 -0.811192\n",
       "2020-01-07  0.772575  1.187298\n",
       "2020-01-08 -0.519914 -0.246405\n",
       "2020-01-09  1.819714  2.245515"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build dataframe\n",
    "df_portfolio = pd.DataFrame(index=df_returns[country].index)\n",
    "df_portfolio['EW'] = ew_portfolio\n",
    "df_portfolio['VW'] = vw_portfolio\n",
    "df_portfolio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4fA/s/xUM7VvqlmjqkGQmR1u2T71p7Jr2ueFWrpVsUrtWNCcfLeSViJoZYiMHl7YdmroklcnSmukHmxMCHdM+d3s8AMTjkcVumuY0RAt5JaJmt3nk0NK2Q1OXpLOSpnQfNKwGm33a55ZFnohpi1xz8tFCXgltkWuqkMrmaEz2lnWrANgNZZFnddSKZhHQQl4JS8iHn1nadmjqjmxOks1JGpK90LC27Dp2M2olm9IJQZqTjxbySliJHCOH4BQojalZOFKZHD7ieDIT0FhByE2LPKeFXLMIaCEvh5TKIjf8kJwo+Ms1GpSQrxZmj62CRe4w5/CUOkVfswhoIS9HchKyKVh1oXqv/eSaIlLZHGvEgHpTwUfucCmLXAu5ZjHQQl4OywJfc6n6P6L95JoCqWyRRV5ByA2nnYR0aiHXLApayMthDXS2n63+R/Rs6JoCqUyOVjFO1maAp7HsOobdRgJDVz/ULApayMthCXloJQg7pKJL2x5NXZHK5HCRJmd3gxBl1zEcSsilLvGgWQS0kJfDEnJfmxrwTOtYYE0BJeQpcnZXxXVcDhsJaSAy2iLXnHy0kJfD9JF/8o+DSMOnpvPSaExS2Rwuka4q5IbDRhInQhfN0iwCWsjLER0i6QjwlT91E8OlXSuaEpRFngG7UXEdy0duW65CnoxAepm2/TREC3k5okNEHWoQazTl1EKuKSGVVT5y6ahukScwENll6iP/5vVw+2uXuhWaGtFCXo7oMGF7AwD9CTvZpBZyTYFUJodBGmZyrUgn9uwytGonuqH3CTh4Nxz6/VK3RlMDWsjLERlg1NaITUA45yI8Ob7ULaqNJ74Dt//5UrfilCeVUT5yqljkDptQrpXlKOSWeLsb4O4P11+JikxSTfyiyaOFHODYDhjYU3gf7meIRjavCBDDjUwuk8HOR/4H9v4Cwv1L3ZJTmrTpWsHhrriOEIKUcOFYjq6Vg79TNdYvfzf07YLExFK3qJTHvwVfuxrGTyx1S+qGBRFyIcQ3hBCDQojdC7G/RecXfwPfuFaJeSoGyUkGcg20+F0khBt7ZhmEH0aHVXcYoPuRpW3LKY7lWhHOyhY5QNpmYM8tMyHPZuDwH2DjNeBrVcuSk0vbpqkM7gWZg+6Hl7oldcNCWeS3Ai9aoH0tPqmIKo713VfDZC8APdkQIY+TtM2DM7cMYoEP/g4wu8AnqtzgueyiNOdUJmla5KKKRQ6QFm4cy0nI4+Pw/dcrC3zzteAOquWJOhNyq/ZR96Mn/VBSSh46PIKsN/fSFBZEyKWU9wKjC7GvJSEdVwNXEyeg5zEATqSChLxO0g4vRjZef37CqRz8rbKgOp9V+Qbf+0v49HoY2r+4bTvFsCxym7O6kGdsLpzLScgf/G81T+0LPwlbXgzukFpebxb56GH1v5rBskB87+ETvPqrD/Lg4fqWt0XzkQsh3iaEeFQI8ejQ0NBiHbY2Mglo26JeH98BwJFkgJDHSdbhxU5WVUOsZ47cC+uvhjWXQO8T/PDhw+w4NKX8bt9OSIzDj94CGfN8sunFbunCkstBdGRRD2kNdtpmcK1kbC4cMlX/RoBFuA8C7XDpX6rSAy7LIq8jH3k6rqJq7C7of1INfJ4kYqkM/3H3AQBOjNW3e3XRhFxK+VUp5XYp5fbW1tbFOuzMSKlujrZt6v2xBwDoNV0r0qlmeqnrWPJ0HCIDDLm7eCSzATJxtt1xI/t+/ZXS9SZ61GTB/U/Czu/Akfvgk6th6MDStHsBmHj0NtKf3QbJ8KId0xrsnMkiz9pc2Mgtn4dlYrIg3lCwyOvJtTJ6BJBwxrXKuOrbteCH6B6L8Z7v7+T6/7qfobB6UPRP1Hf0kY5ayaYACU3r1VN+eD/SbjCOn5DHSc6pJgioayE3/frf25flLX8K0t/xPFYwwkXjd05Zrxs6z1M/1sGn1aBoJq6iXZYpB/bvwZmNMzzYu2jHVK6VDLYq4YcAWSvOfLnUW0lMFMQb6tO1MqomQ8+c9Ur1vufxBd39nt4JnvfZP3LnU/2safLyz9dvpcln0D+phby+scqMGt58bem0pw0QhDxOhLF8hPyBETcT0sfLx/6K32S305k5Pn294Ep1nqOHYeyIWr7re/V9ftUwLfFoeHzRDqkyO1NVww+BQi2W5ZLqPlXI866V8SVpTllGlJBf+f000vAX/OULxO2PnEBK+O17ns2tb7yIN12xjvag+/SwyIUQ3wMeAM4QQnQLId68EPtdFKxaGA63ssqBpFu5fkIep6p+CPUtdKaQh41W1jZ76RmP84xcSZOcKPiPpVSuldAqdZ6jR9Sfu0FZXE/9cOnaPw+EWdAsHl48P24mncIu5IxCnrWbny8ri7zIteIw1DnWk2tl5CBRZxN9CYOwZyWMH1uwXedykjt393P1GW2savTml7eHThMhl1LeLKXskFI6pZSrpJRfX4j9LgqWRe705IU85ioIud1lWuTp+hVyOdkDwPaztnHDeSsBOGZbDUBmcK9aKT6mBCXYqc5z/BgMH1C+xsYu2H9nuV3XPTbze4lHF0/Isynr4V+5aBag6pXD8rHIk5OlFjkoq7yeXCtjRxlydALQzQoYO7pgu3702BhD4STXndNRsrw95NaulbqnxCJfB8CkoxlQQm5zK4u8nrM7U2PdjEsfK9taeNn5K2kNuNiwdTsAsZ6n1Uqm2BNcqYQ8l4HIgHq94Ro4el8hkmUZYTeFPBVTYnPXnn4u/Pjd+UGqk0F+sogZLHLpWEYWuZTTXSug3tdT1Ep8jBECAOyJN8LYsQWLCrpzdx8uh41rtrSVLO8IuhmNpkik6zcHQwu5ZZEXCfm4JeReJw5TyNPx+hXy9OgJ+mQTK0Ju1rX4eOSDz+PsrVuJSDeZftMiN90veSG3aFoPG56rkqKWYUaow8y6TccmmYil+eBPdjMUTvLYsbGTdkxpWdgzDHbmhXw5WOSZhBr4nybkwfpyraQijGXUdX8q2qAekkVTMe7vD3PP/rlNzXh4KMoZ7QF8LkfJ8hUh9T0OTtZvToAWcssid7qhdQsIO332TmwC/IYDw6t8hslYHd3MU5CTPfTLJlYECsLS5HdxUHZiGzaTfya61f/QyvwDS624DtZdqaa0W4aV7pxZZZFn45N8/vfPMBpNYhOwu+fkWZHSumeqVD8EChb7crDILau7OPzQel9PrpVkhNG0k3NXN3BcKsv5l3/cweu+/hAT8TT/+ut9vP/HT81p1+FEmoDbMW15hynk9exe0UKed6141EDgOx/hUc8VBD1ObDaB4VHduHq2yJ2RPvpkE+2hQle/2efioFyFa/wZtWCyV4m1fwX429X5grLI3SFYdeGyFHJXTlnkuWSYJ46Pcc0aGzc3HWB37yyFfLKvUGxssk/VHKmE5YKawSLHaV7jhbDIpYT/eR7sun3++yqHZXWbFvkvdvXyp4PDdedakakooxkX12xpY82GrQD8dsfD3PfMMPc/M8wTx8cYi83NRRhOZAi4nNOWtwfV76pvon4fyFrI00UWOUDzBsYTORWxAri8lpAvXsLJrMgkcadG6ZfNrAgWhLzJZ3Ak144nMajcR5M9qqKdzQ42m7LEPY2FWeBXbIXx4xUOUr+4rDo4yQhnjN/L54fezMejHybWvXt29TG+/3r4xgtVeYPPnQ2Pf7PyupnaXCsLapGnY8r19cjX5r+vclhi7W4gnc3xgR8/xad/va++XCvZDCITJybddITcfPT11wHwNxc48Tjt/PCxE4zF0iTSOZKZ2fuzw4lMWYvcMpAGtEVex1g/MstCBSbi6byQ+zwektJBNlGnQh7uA2DCaMXttOcXN3qdDGH6O6NDSsiDnYXtVl+s/iwsy2u5pJObeKT6/kQqwg2JX2AzC4c1x48yWOuAZzIMPY+qCIhbr4dcWtXdqYC0StPOIOTCssgXIo3cylztfkSFkS40eSEPsfPEOOFkht29k6QdgfpxrZgD21FcSlydbgh0sM4+wrPWNvCH/YXSH5PxKj2qCijXynSLPOB24jPs3LN/iMND9dkz10I+1SIHxouF3GUnhptcvU7AbA5ipr3tJYsddhsJo0m9iQ5BuB8Z6GDc6na+5HPwmqJuujukBCxdv93HaUiJ1xTyXCJMA5MMhc4GYJ3o56nuGl0CJx5WZVGbNxYe7LHKRZJEjVEreSGv9Zr2Palqx5SjuATB3l/Utr/ZYCb9DKVd3HtACWI2J+lJGKo3UA9lBszIsSievLtDJbcd4uJ1zSWrTiZm195sTkIqQpu9vMF207NW8dCRUW744p/qshKiFvIyFvlIJEmLX1lbfpeDKG5yyTotmjOqsjPTwTXTPsp4WtSL6DCEBziS9HPpJ39fEPNirEGuerG+aiCXTuAUqgudS4ZpEmEywdXk/O2ss/XV7ic/tkONH9zyK3jtD6HlDBV3XwGbZZHPMNgpDI/ZzhqEfKIbvnIl7P1Z+c+Lv5eTIeTm/m/8xm5++Fg3W9oD2AQcDpsSUQ/uFTMpLybdtFlCvvYyOPEQV7Sqa+ywCUD1qmdDJJHho85bed2u16pyvlP4lxvP4u9ecAbhRIZYqv7CELWQl7HIR6Mpmnwq2cPnchCXLhWeV48MHyCNA3vT2umfec3iZOPHITlBXzZEPJ1lb5+yOg4Ohrnhi3/id3sHigok1c/A1kzEIuP51z4Zp5EwjkArtuaNbDGG2N1To/gcfwA6zoHACtj0fPA2VRXyfCXMGVwrNsNtrl6DkFs9ACuBawrSFNIJ/0YY3FN2nVmTTauJVCD/vQ9nPPRNJLj2rA62dgbZO6aEkWQd3Bcpdd+m7V6Cli/7glsAOKf/JwRcDi7bqIyXyVkK+WQiTQcj+JJD8LuPlF2n2dSEuQ6mnky0kE+xyOOpLLFUlmZ/QcijuBB1mtmZG36Go7kVtIX80z6zB0yLvF+FYw3IBkAJOMCOQyPsOjHOm7/5KL/Yb57fYgh5LrcgxylOy28XIzhFFnewFZrX00V/bSGImaQa4FxzWWGZp7qQ22r0kTscLnJSkE3WIOSWu6ZC7ZCUmbn6QKRVtS21AD3E3/yjmjINIDFBBgdNwQA3PWslr9y+igu7mthjVQiuhwe8aZG7fAGEMB8wDWtg84tw7PwWv3nXxbzvRaoc9Wwt8nAiQ1CY1/TRb5T9Hhq8yt06HqsDN9MUtJCnEyBsYFdf0khU/aCsp6/PsBOTbmzp0h+OlJIdB4eX1F8WSWZIDeznsOzIJy0U4w80EMMNA2oGvr6ssroPDKjeRe94AqddcON5nfzvY6ZFuBhd6J3fgf84a95iZKXlp3GwUqja697GdmjeSCA7RnRydOYMz+EDkE3CqgsAVW9jIOMhV81HXqOQu5x2EhjkajlPy6CoIOTJ6DgAT6dVCQZrkHte7L8ThvYpt05igqjNx5pmH5991Xl0NnjY2OZnJGO6HOvBtWL6yF3+htLlZ78SYsN0JI/SauZSTCZmN9gZTqQJEiUW3KgWDEzv9TRqi7yOySSUNW4+4Uci6ktq9qkbwmG3Ebd5cKZLB0F+vquX1/zPQzx0ZOlmDvm3X+3GNnZECXlgupA3+wyGZBA5oNL0T6RVKOUzpkXeNxGnPeTm3195LutXmwKxGJXuTjyofLLzFKNUVInLuL0Zw/SVe0Kt0LQBgC7Rz56Z/OTWtGEtmxkMJ7j5aw/ys/1xstHy32sykyWZKMoGroLhsJHAWZuPfAaLPB1T53HCocZCcvONXBk/Xig4deJhSEwSxpe3OgHaAm7CmEJeD2MnpkXu8QVKl4dUXSEiAwQ9yuUyW9eKZZEn2s5VC4afmbZOo3ltxrRFXoek4yX+ccsib/IXCiL12zpoSHSXRBR87+HjrBX9HFrKcKTxYxgiy6Cxhq2dwWkfhzxORmQQYVp7RxKmkJsWed94go6QB4fdxuY1SsizcVP4Dv0e7nzfyWm35QeOzC2V2iJliptV5AxA+FqgWQn5OtHPnt4ZBMgS8qb1fPyOvew8MU7KGcSZS5RN5DkxGlclbAHs1YtmKSE3kLUIubVOfKxsxEzGPNfQGjUBymDPPMu3Hv1T4fWJhyExwYT00uApnFNbwMUkZhXAMgOAi47pI3f5GkqXB1ao/+F+XA47bqdtDkKeJEAM0bBKJc2Z5XKLafCqa1M2WGCJ0UJuWuS3/ukIz/m3P+TrKbT4Ct3mHudaDJmECZUwc3Q4iuPoH/mj6z2kjy1dfZLmhGrPh954I50Nnmmf+90ORqQ5iGlzcCTmRggYiaYYiSTpnYjTabpkGpqVPz06YTpFd/8YHvqSymI8/mDFQbhZk8vB4D71Ojo/IU/HlUinPUUzTnmboXEdIDjPNzJzCOLIIQiuZM9whp/t7OVNV6xjRbsZb1/GT358NIqB2W2fySK320lIo1CbpRrFseZWnfjij+MTxKXBli2mkHdPX2dWHLtflTBeo6I+SEwwnvWUWuRBFyPSNBCiSz89o1W4zjvVIvebQh4ZgCP38nrjD7P2kSeik9iFxOlrhOZNhQd8EQ1mSPJYVFvk9Uc6Tsbu4tN37efoSIydJ8aBUot8wNWlXpiTFv/o8W6us6uJXx1DT8/z+ImqMcvVaEuamZjNG8t+HnA7GDJ/iNLXxmgsw9YO9X7/QJiByQQd5gOgpSFEWtqJhVVbek6YFl9kAH76l3B3+ZH82fKd3/ypUBJ4nhZ5xsq2DRSVHfW1qB5WaDVnuYfY11+DRd68gS/+4SAhj5N3XLWBllYlDMPD/dNWPzYSw4X5Q57BR244bCRnssh33a4elMXZn6PTRTqXCBPGy7a1nUzgIzI0zyzco/dD1xWw9lLofxI52cu49BAqEvIWv4uEcJO0+wvlC5YKKfP1jvyBKYW9HC41QB3uhwe/xNtzP5h1HHkqoh7aLl+D6tGVEXKH3UbA7dA+8pNFJlshiaKmjRMMxkU+NvTho6O4HDZ8RiFLctTTpV4MKUtyb+8EL3A8AYARnmdh+z/+K3z9BXPatC19ggkRVOFyZfC7HIyY2Z1ZXxuZnOSS9SpxYsfBEdJZmbfI24IeJvGSMkP6RFglGiXHTqjBMKvo1jzZs+vB/OvM5PzEIWdm2zoblAWdFgZYc6w2r6cz28tIdIYf3chBaN7I3r4wV2xsIeRx0mla5EePTz/nYyMxfI4s0uZQ5Q6qYLlWqiYE/faf4eGvlVrk5fzkiUnC0kPA7WDS2ZavQT8nkhGVxdp5vrLIcxnEZDdDsqHEteK022jyGkw4WiC8eFPpTeORr8N/nksqOk5Mugj5pvc+CbQro2P0CB7is7bI0zEl5Moi3wix4bI9skavoV0rJ4PByQRnf/g3PHBojjOpp+OMpuxcuUm5Fg4PRWnxuwrhTYDwNjIimvIWuXdkDy1SWa7BePf8IlcmutWg0xz24clGiNhDFT8PuJ35rrE169G2ziAdITc/3amEoCOkfhTtITeT0ksmNg5AMKW60rHju1RUx3yEo4iOpLI2I9JNbHR+g51WV9vXsgqAhNGYH7SmaQMtqW7CiYzK2ptK92PKNxwfQzZtoGc8zspGdS3WrFLjBb3908Xr2EiUVrdEzOBWATDsNhLSKNRmmXYCEuKjSugtsXeFyg60iVSYCB58LgdZfweB1OCsxSqPOcj58+MGb7jXD3/+I7pf9A0+m3lFiWsFoDXgYlg0LZ1FLiU89GUYP4YcOUwUV37QsQT/CjV4PnYUt0wwMUuxzcUKJQryPdwyfvJGr7PiYOeOQ8Nc+PG7l6S41rIX8qd6JtiYeYajg3OMc80kSEgHqxq9+WxOKxnI4qzOIPuyHaTN2t5nRnaQQzAU2MpK2c/oTFZfNVJRlWAyh9R4Wy5NRpS5qU2CbgfDpo88ZqgHVZPP4PKNLXSPqeN1NChBavIaRPAiE+OQiuKXSiRzJ8wxAEtw5oGUkjWZYwzbWjgh20hNzEMcxo5BKkxSOmluUa4QI1g0IUDzBtyZSRoITx/4SobhWzfArS8GYNLXRSqTy/dOPEH10BsZGph22GOjMZrdcsaBTihErYhKtVby3320YJFveh7s+TEce6BkVXsqnLfIjabVdIhRnhmYY/2fMSXkt+6FPx0aJb3uuXS3PYdJ/NOEvC3opl82Lr6QJ8Nw77/BvjtUiChgjB4gKj35QccSAu0w8DRk4tiQs65WKotqzdCySb0u5yevYpE/eFiFu37voVK3V+73nyB38J5ZtWe2LF8hjwyq2sSHn+AXrn+kuefuue0nHSeWU0VxNrSqad2a/aU3ygu2tXMgtwqG9hFLptmQOcSYdz2JFeezVgxyfGQWyULRERgvKshkjsTPJeHCIVNkRWVB8bsdjKAscmvWoyafwRVm9htAp2mR22yChCOALRkmM16wvl2DOws7nJxf9zqRztFFL+O+dQzJ0Nx95BM98PnzOGvgF0SFG+FSg1+uYNGgp2lVrRd90y3Xnd9T1z2nBi177coCX2nN02hWhJwcGyzpbWVzkhOjMUJGbsaBTgCX6VoRlSzyuDk2ko4rH7mww4s/q5JcfvCGEneLPRMlKry4HDZsoZW0igkmI3NMUjMt8mPZFjI5Se94PJ/kUuxaARW50p0JKWu3Uh2YMjx+fIx//fW+OTVvx8Fhdn3t7fD7j8Htr80vd0e7ieGubJEXjTNkZlmtVBQLecNa9V2YPfBiqlnkVgTbbY+cIG25e6Ukc+9nOP6rT8+qPbNl+Qr5rdfDL/8W5wkVRmWLDc9pNzKTIJpz4jXsbGhT2ZFTLfJtnUGG3F04szEGuw+zWgySCq7B3baRoIjRNzALF8Gv/wG+elVhgNOa1HkOQm7PZcjaKlvkAbdTCSYwZlPi1OQzuGyjEnW301ZigWWcfpzpScYHCn5/32SRv3ae7pVJM+nC7mthhAaciRq+s+5HlR+5uJs7dgRkDm92gjhecJlZrd7CA6o4lny8WMhzOXj4q9D5LHj+v0BjF8ey6np0mr0TDB9Z4cBIjTNQNCtM30ScdFYScuZmLmGLNdjpxJatIOT5eyCmRNvpAU8DPOcDyt87clC5fw7/ESMdJmnzIoTA3qAePOmJuT1YMyNHiEkXG9Z2AXB0JJa3Msu5Vo4kg+qhF6vdffnznb186Z5Dtbl/dv9IibbJgXtv49zhO3ii8UVIfzuc+RIABJIIbhorWeRFyFSYXDmXWgXsKasee4Oai7X9LHXtp9DgNSoOdh4ajBB0OxgMJ/nt06o3l05EMMiwYuyJ6jXu58nyFPLEBAzvR+7/FZ2jDwFzn1NTpuMkMPAYDja0KkGwXCwWQgha1p0DwMjRXawRg9DYRWjlZgDCPQdqP+DoYfWDsOo55IV8fNZtd8hUVSH3Ou0cppN71/41u0IqFbvZ56It4OaMFQE6Q56SsYCcEcKVjTI5qIR8XPoQFP0Y5mmRT8TT+EUCmztAzGjGmxqeeWzg8W/Bn/4TvngRDJnXuaibn7B5wDDD0bxFFfAa1yKFnS5bf6mYHP4DjDwDF78dLnsnvGsn3ZPqB7bSCuEUgpyrgRARnipK8z8+qjI0A44ahdz0kVcU8rxFHlNWubXPfNf+ENz1AbjjPRjZqIoeAdxNSsgz4zN/HydGY3zmN/tLxgniQ4c5Ltt4wVlK/I6NRPMPu2mulYCL3lyDejOLAc/Wgfv4rPO/OVZLb/WpH8G9/56/vzaN3suwDPLKvtdw//V/gFfcCk7VW47hJuipYJEX4ZEJIqnahdORF3Iz3LLrSlUyeEroaKPXIJzITAuwyOUkR4ajvPyCVXSE3PzgUdXrnhgdMtsTg/5dNbdntixPITdjmkUqwkUpJeR5QZwt6TgJaSiL3HStTLXIAc44+0IAhnf/AZ9I4m7bgNGqrL7cyCySMyZ7weaEx25VkQPWA2gurhXS5Kq4Vmw2gc9l8Pvmm+lPefA47XjMaJwPvvhM/v6FZ5Su7wnikxESIypa48mcOben37R2Kljk47FUTUX3J+JpfCSwewJkva04ZXrmjEGrWFkuky81UCzkSVuRRe4rEnK7k3RgFetFf6lP8+Gvgq8Vtr1MvReC3vEEXsOeL10MYPc30yiiJUJujSt4bemaLfIEVYQ8VuxaSRYqcJoJTQwfUPf66GHcuRhphzpPT0iNBeQqZJ8W84sne/mv3x9kb1/RdR47xgnZykXrmvA47RwdjjEeS2PYbXicpZE4bQE3g9KcfGQWfvJ14w9yk/1+TvTV4D5LTAASnvohAP7kAKNGB4bh4q59I2B3qCkKgZTNi90mpu9jikXuJzGrpCBnJkzS5smX6mDt5WqQv+fRkvXy9Vam7LtnPE4yk2PzigAvO38lfzwwxOBkIi/kANEDf6y5PbNlWQp5vPtJALKyKLJkrtUJM0nTIrdzZkcQw25jXYtv2mrnnbGBERlk3ei9AAQ6NqpayIA3WmNMb9acuX6lquvBRE9BqOaQOeeQaXJVLHKAgMtBOJFhpKiiI8CzN7dy7dkdJevavQ14SZIeOcq49DHoVNEgtGxSfuMKFvn7fvQU7/i/x2Zs72Q0gVckcboD2KxsvHJ+8lxOxVZLqR501oPECgeL9IPDzW7nOYwYHUqYz30NbCoN45TNG+kS/YUf9OhhOHAXXPDGEiHuHY/T2VDaO7F5m+gw4iWFt3rG4ggBbpGpyUduCbk9W2Gw0zqfdFT5d602uQLKwjx8j7o/ZA4bOTJOJeQOnwo3rVYPxsJ6+OQfSFLijpygW7bS1eJjbbOXYyNRJuIpQl5nyTUAlRTUL83w1lmUVLCn1PFG+msIz7V6o099H4BQZpiYawWXb2zhD/uG6BmPM2hT4x9Zh7f8PiyL3CzH7BOzC0F0ZyL5Hg+g4usRpRmwFBfOKnWvWP7xDa1+Xn7BKnISfvJED9GJgvswefDemtszW5alkE8c3UVYevhjTtVFyEgb9swcLfJMgiTKIl8RdHP/+67mBVtXTFvN53LQZ6xls1DWqqN5PTg9jNqaCCRq7HJGBtQEBm2qQhuJiXn5yB0yQ26G6ImA20kkmS4pzVsJwxQI1/gzDIkmMj51HcKuNtK+jrIz00gpeejIyMzFqYBoRFmFTm8QV4MS58xkv4o2+M6rVA8FYMd/qmnXBnar6IUGs5aG9YMP90Ognb+2/yM/XfMBFc/9si9Bx7klx7O3bFA+ciuq6E//qdbd/qaS9Xon4gW3ioWniRWOCE92T+QHPHvH47QFXNiyqZqjVpIYOHLJ8i6kqRa5s6gNzRvhWKmI5AxTaMzBWFHDw/+E6Q560spwjY3gzMYZcXYQdDvpavZxdCTKeCydz1wspi3gKsw0NVm7kBtp9V1HhirPtJQnPq6uZ/9TMPwMLXKEhGcFz93SRs94nFd9+QF+36valnVON7KAgkW+4iwAfCTY11fbgGc2J/HkIqScRRmjnkblJz92f8m6ln9+dEp256Eh9Tve0OpjQ6ufd7U9gefBz5GYVEK+M7cBf//DCzNbVBmWpZAz+DQH5Cp+4buJ72aupt/ejj0zh0p6uRy2bJIETnyGKrbTFnBPs0osUk2bC28aVPGiiKMJb7pK7epiLIu29Uz1PzqkZuWBOQm5kzQ5W3VB8buVRT4cSU6LxplKqFG5JtoTR5hwtJL2KiH/ySHYHwuUda0cGY4yFksTr6HYfsJMNnL5QvibVdLN+GAPHLwbnrkL/vfF8PTPlb8UlLWeCqtBTIenYMGG+5H+dk5MZmlvnF6+18LRsgm/SJAJD6i48ce+CRe+FYKlPZGesfj0EgeBFTTmRhmOJPOzp/eMm4KfSdRmkVtx5FA+ltzykWdT6oFV7K5p3qAe+qBccYA0TP+tO0QWG7bk+Ixt6Mlb5Oa6ZuhhOqgejmtbvJwYjTMSTU3zjwN0NniwOQwijsaaLfJ0Noc3pyzU1Hh5d1wJiYn8tIOZozvwEyfja+fqM5QLqWc8Tp803WZGhe/b8MGFb4EL3gDASm+WXz5Zm4E1EkkSJEbOur4Wqy9R903RQ9gS8qkDnoeHIoQ8zryx9Jr0T7k29nMSZqb0T7gaIxs9aROcLz8hl5Lg5AGOObp46Y038+C2D5Fz+tRFmi3mjyshjbzvuBqBVarOxZijJW89JYxG/Nnx2o5nCmG2ZUvJe7WjGvdRhCHTyBktcgeRZKa8WE2ha6USuAYRZdKzOm/lHIgH6c6Vd608fly1u5ZZUxJRZSG5fUEaVpgPwuFjpEeOkLF71FyY339dwd2UnFSuFZdfRXNYFmhkgIRbhc6tbKjQ1QZoVj5+18Rh+PX7VPf76g+UtimdZSSaYmXDFGEOduJOT+AilU82ywt5NlWTj9zlsJG04vzLxeAXu0biY6UPByspJbQaWtX9ItymxSgEURHASI1XPX4uJ+kej2MTsK8vTCKdhfGjADia1gHQ1ewjlc2xr2+SkGf6veS02zizPcCQaK5ZyEciKUKo36OYaZtsRj2sVz4LgMwh5UeWwU7aQ26u2dLGay5ew5BdRSQJV+UHNy/+DGx+EQDbO53c98wwYzXkeBwbjREUUezehtIP2s5Ubq+irGYrsslyWeX3MRKjq8WnjMD4GCviB2ligti4Glfo7XwhYeGHPT+ZsT1zYXkJ+cNfg+/9Gd5chGhoM1dvaePzN59Pxu7FyM7BIreE3HStzMSqzecDEPGsyi9LuZoI5Wq0ps2b+h13mW2dp5A7yCBn8pG7nQxMJhiLpae7D6YgOs4h27yZHxo3cOScd5NpOZMx6WdnbgNHUo0qbXmKID1xXFnJ8XR2xnAvq1qh3R1gxYoOxqSf3NBBhk8cYG96BQ9c/wd4xf/C9Z9TGyQmlKVq+FVYWJFrZdKhftidUwW4GFMMWyb3QPfDcNFbClEJJlYlyDXNU7rsAdVj2OKL8McDQ+RyEmP8CJ86fJOKJqlByIUQSGsAs5pFjvJ3P9oTL2QoW0LetlUJCiCK2h5zBHClq993Q5EkqUyOS9Y3k8lJ9vWHSY8cBcDfrgZUt7Srh8NkIpOvwT+VrZ0h+tI+ZI3hh8ORJCGhhDyYHq7uq7YGuwOdEFyF/dh9ADjMwc2v33Ihn3jZ2fha1AxYdncVIYe8xX52q4NMTvKr3TM/fI6NxAgSw+WfUurCvO7F8eRNPoOQxzltEuaxWIoW6/odewCBxC4kttFDZLCxYc1qfp3dTm7fr06Ke2VZCfnxgWHCvfuZkD5Sqy/PL884fbhzc8g6NEWpViH3dCqLvG1NIdoj42mhiQll7cxAZrybhHTyu+4c0ukr9TnO0rUipcRgZovc73LkY6FXNVYXchrWYP/rR3j5+7/Jm59/Ab7mVZyf/Cq75Xr2Js2u7ZSCTpZFDpDIVL8GabM2CoafjpCHw7IDY/wQrsgJTsg2bt81DGfdBGe93NzhpLLOXX7ls4yPqzGF5CQjonHmcwqtJo2T7ZO/U+9Xbp+2ygOHlQ/zknVTfsSm++UFK7Pce2CIgXCCjfIovsyYGpicYb7OPJaVPZNFHhtlNCn48M/3qFDBvJCfSdZ0xdk9DfnVE44gnmz1iJ/usRhNTPKuhh0Icjx+bIxo/yFGZIDOFWrw8Pw1jfzmb5/NJ286m794zoay+9nWGWQ0686Xb5iJoUiSBpTQtYmx6iGIlrvMHSLXvAFnTA1+u5pXl6zW3mU+zHytVMXuAIeHNiPN2mYvv987c9TMsZEoQRHDE5xyD5g9IYYKlT+FEKxr8XFkuPScxmPpQsGxorGNtvgRIsLPjc9ayS8zF2NLhU+Ke2VZCfnnE9dx9vDHOTf5NVZsPD+/POf045bxWSUAAFNcK46Z1/e3wYZrcJ35wsIybws+kWR8YmYhHuk9Qr9sIicFaecUn/Mso1bSWanKqc4gKPm5DWFGi9zCGiNoCaiHhMth43DOHEwqSlsemEywv38yb8nN5F7JWtl2Lj8ew84JsZKG6BECiT6OyzZ+vduM+Tb8gFAWayahIhEs14oZAtdnxjZXdRfZ7Aw7O1ifMdvcef60VXYcGmFDq68wma9FUFmEF7cmGYul+fXuftrEuPrswrfA2S+veq55LCEvZ4XFR/Of2zIxEhjsHwjz48e7VULTua+Bs15ObMO1/DZ7AelQYV7WlDOEL1t9MK97LM6N9j9xyZ6P8I7Gx/jFk70kh49wQraWRGZtXhHg5ovW0FUmWguUkIell1y8tsklRsfDeIRyabSLsWmiV4LZyzoSdfLdgwWjJNC6qmS1zWeezcuSH2FkTQ0F5lx+RCrC5RtbeOjI6IxF9Y4NKyEvflACqhidrzVfLM9ifauPw0Ol5zQZTxNyO1SJ5kN/QJq5DRtED3F7gC3tQSbaL+PT3veqCaMXmGUl5J9++Tn8+C8v4yMv3cbziyNLDD9+kSA6iwQAoNQid85skSMEvO7HBYsRsPmVhRAZmznGNjZ8gkGhnvoR4cv7nCelt1DrodamZzI4RRZpr+5a8buKhHwmi3wKq8yU9Ref3cFRWRDyj9/xNO+5fSdfu/cwQghee7Hyd8804JlLWha5usmHXKvxZ0ZxyhQDthUkMzluf+Q42GxKvC2fvGFZ5GMq8gc4lgrS6HXineEBPOo2LbvmTephUEQ6m+PhI6NctqFl+oZmadyt/ihCwDd3HGWFGFOurOv+HTY+r+pxLYRhuVbKWeRj+QcGQFI6MRw2vvvwcWVZvuxL0HEOk74u3pp+L15fwa2QcTUSkGGyOVmxaNuJ0RgrhLL635n9NvuP95MaPsKY0ZEvZ1wLW9qDRPAqa7IGwuOFkLsVYoxjI1XcnuZ9v2/cxsGcuuaj0k9DsLQY3EXrmth28TU8e0vnzA0wfJCKcNmGZiLJTEkuwCNHR/nwz/cQLipzOzncg52cEu2ptG6Zlqq/odVP/2SCaFLpTTqbI5zMsD12P/z3xWpybDNPISDiJB3qWt90YRf/PXoBu+dY368ay0rIbTbBs9Y08obLunA5ioTX8OEjTiQ5SyHP+8idNQ12lsMZUF9+fKSPx3/4bySi5a2WXE7ijPUjgivpDLkZy3ny/sFe2YycrUWeMn2uM1jkAdMid9gEbWWmg6vG5hUBfvyXl/HmK9cRwUvC3Upu5CC3P3KCHz/Rw//cf4Trz+lgs+lnnXHA00p+MpTlN+ktWJjOlnVctbmVz/72gIrJdYcK4Y6uIh+5Oc5wOO6r6cEU9qqHTD52v4gnu8eJpbJctqF52me4g2D48SYGeOUFqzg6EqNNjCP9bYUKizVgs0IKp04ukc2omelDBcszgcHF65rY3VPqqouY80/6XYWHds7dQIOI8O+/2c81n/ljWauzeyzOGuckOL34UsO81vE7VuQGWdm1BYe99p++x7Dj9IXUrEnZmWOz45OmUoXWsEKMc2yoygPAvO+PRBwclkrIB2TTtHoqLoedj914dt64qIoRgFSUS62Szea4w+6eCd74v49w646j/NlXH2TCrJkSGlN5KXSeN31flpAXPSyt3ozV07DyFDrTR9UKr/854kWfyK+fNtRD6YZzV/Kibe3YZnH/1MqyEvJK2NwBfCJJJD7LKoTmhMop4cblmNulcDWYsdZP/oxn7f4YO3/7f2XXm4glaZWjuJpWcc6qBgZTBQHul02z9pFnUrVNAOx3qx9ER4O7fEbcDDxrTWO+sNaYew3xvv1MJjKsb/XhsAne/uwN+fGF2Aw9IpsVjWJGHiQaCj7ZTGgtn37FOXicdv7fD59EuotcT66AsqZTkXwEwd6IL9+uasQDXQCkO0rdKqlMji/+4RA2ARevLyPkAMFOmOzlH6/fSmfITad9AtuUDMKZsBum8Ey1yC3fcJGQJ3Fy1eZW0lnJLnOCE4BIUgmFv8hNhqeRoIhz91PdHB6Oct8z0+vWdI/FWemYgPZzoHkTb/DuwBBZNmzeNqtzAHD7zezO5MxWeSJsCnnbmTjJMFJmgo7CyuMAHJh0cFgqa3vI1jyrB800XH5Ihmn2u9jSHmDHoWES6Szv+L/HCLodfPKms9nTO8nPn+xlIp5mY/oAOWGflocAQOsZ0+aXXd9aKuTWYG5Tul8lr62/ClwBIjZl4ORcDQCEvE6+/LoLyk7LOF9OCSG3m2FZ0QrWcEVM14p0eCrGjs+Er0lZEc1DqsDORH/5KbgmRvpxiQwy2Mm5qxsYKBLyXtmESE7OqrpcJqXaLhwzhx9C7f7xcjR4VZe/37kSmznpwXfecjE73vdctnYG8TjVMWZyreRj/c2kDtG0nqwU5KTA1rCaFUE373n+Zh47NkaEgutpNGPwnSfN77bvSaTTx74Je00WeWTFBSSkk8mOgl8ym5P81Xcf5/f7BvnIS7dVTpQKdEC4j6Dbya1vuohzQvHS2YhqwOEqY5Hv/B58+0b1usi1ksDgCrMu/qPHCrkJ4bxFXhByu5m8NTKiUsB/9Pj0STAGwwnaGFNzWnZdwaqUujftTV2zOgcAlyXkNRgcGat0gJn4lhitMimJub+9Y4Je2UxcGow72iqvXwumawXgqs2tPHh4lPd8fyfdY3E+c+NGXu34I+91/pCJwW6Oj8Q4VxwiHDqjNCHLIj/gWfCTdzX7EIK8n9xK1w8m+wvJa0DULB0tpvreTwKnhJA7PErIE+FZJtVYQl7uC6yRQJOyyNenVEGn7Fj5dP3YsMpws4dWsqnNz6QsDCz1yWZVnGoWM5Vn0qZFXkMcOVA93noGhBC0B90coxNPepR/CP6GjgPfzQ8QFizyykKeyeZwZqOk7F7lAweaQgG6ZSv9NNIUUt/hTc9aRcDt4GjErmpdAF9/eJiH+syH3ImHyDZtJJrK1fRwkm3b2JK8lVHPuvyyT/xqL799eoAPv2Qrr7u0q/LGpkUOys3kTw1PK840Ew6XZZEXCflTP4CBPep1+1n5xUnppCPkYfMKP48cLUS0WC7DYiF3+FUvokFE8Bl2fvP0wLQwv9FoiobsiHr4dF1R+MAsLTEbvIEGAKKTM5cFyFnRLWa0jSdRZRKM+DjS5uTgeI6WgIe3p/+WXzf82azbV4Lhz2dM/9VzN7Kpzc+vnurnuVvauHTkJ9h+/k7+2v5jVvXcwdHhMOfaDpHreFb5fZWZZMLttNMZ8vDA4WHS2VzeReON9aq4f5O0Rz2QrIfuyeSUEHKnx5wFJzY3i1zMQ8g9vhAJ6cQplIh54/1lQxHTY8oqMZpWq+m6zNnJcwgGqN3asbBcK8I5g4/c9KvOdqBzKu0hN/tT6sb8i9StcMd74IEvAkVCXiUEczKRwUeCjL3wQGkLuHgwt5VHc2fQalac9Lkc/NmFqzk0WRizuPtwjAnMgb6JE0QCKtGnFiFXsb2CvokEsVSGv//BLr5+/xFuuayLWy5fV33jQIeKksllVdRJfHRacaaZcLrN8y0OPxzYDefeDH/3DKwtCKwVBru9q4nHjo7lKxYeNbvw7aHCGIcrYAo5Ed54+TpSmRw7DhbcK7mcJBGdxJ2LqTbnhVyUiE2tBBrU8cZGq5cellKSNadNs6KE1oveyiGIiQmyrhDZHFx7Vjv35s4lHVwz6/aV4PLnx2OCbiffuOVCbjp/JR96yVblrnOHCIsA/vARJnr2ExIxvOsvKr+vQLvqQU6ZLeiNl3fx4OFR3v7txxiPpxDkMKJ9JRa5MO8VV0ALeU24fGowITVrIVddfWHMw1q12RgXhRH2DobZ0zu9HZlxJeS+5tUqbV6qY8ZwMyFNkZqFkGfNrrptBh95S8BACPKVHedKe9DNbwaV1TzRdLaqEX3XB2Bof36gOF7FRz4aTeEX8ZJaGW0BN/+QeRvvSv81LYHCeVx9RhsTskikDT8TRT2YIbcaJJ0pUxXgnNUNOGyCBw+P8Olf7+eHj3fzrudu5J+u3zrzSQc7QWZVKQUzWmbWQm5a5NmU6VaKDKp9tZ+lwlmLjIiMzcBpt3HOyhDhZIbe8ULBq65mb0l1RndQddsbRIQbz1fumUNFSSrj8TQtmILqb1ftbt6kXDkzuOPKETKFfHy8esjFwGQSd8b0ozdvIO1dwTbbsekhiKmoynJMjJN0qPvqhdvasdtExcSkmjEChexg1H3y2Vefx9pmn7r+/hUMudbQnDyOve9xAFxrLiy/LyGgaT2Mlgr5W65cz7ueu5Hf7xtkX1+YViYQuVS+dAdA+0rzPu2oIdJmniyIkAshXiSE2C+EOCiEeN9C7HM2uHzqRhgYHuYb95f3UZfFtJLs8xBygElbAwBZ7KwUw+w6Pr32ii3cR0baCLR2EnA58xZ5VLryr2eT3Zk1XStiBtdKR8jDL//6Cq4/Z343U3vIzSHZyW1t7yZ4yw/gxf8BNgc8/q18nZpqrpW79w7gJYnhLQz0tAUL4t1aVAO+wWsQpvCd+AINJQWNTtiU1VNLL8PvcnDe6gbufWaIXz7Zx7VntfOeF5xR28Bvo2mxD+2DsCnk/tkJudujHkDppCnk/U8B8JX9qu0/f7pwr+TsyuJe06zO3ap//lT3BGevaijZr69BRUu1G3E2tProCLlLYptHo0lWMK7eWA+fK98Ll/7lrNpv0dSsHhzRiequlYODEUIiStbhA7sTW+e5bBNHp4cgPv0z+MEtcOgPRIUyZM5oD/CRl27jNRfP0yK3fOTlwjJNIQ/7uliZ6aFh9Eniwq0GNSvRvL7s/J0XrVMPtye7J1glzHK1oULb7WZSmc0scnYymbeQCyHswBeBa4GtwM1CiBrMnYXD42sA4J6njvLRXz5dUyU+oCDkrvkJecShjj/UeB5+kWD/semDO85oH4M0EvC48LnseR95VLqJSrPLPIua6paQ25wzhxRu6wzNKWKlmGdvauXKTa1c+8Z/QgQ7wN8KZ1wLu27Dd/gOrrA9lRfykUiSPb2F3oWUktsfOUGHO4PLW+i9FIt3W5FF3uhz5nssYPa4PIXu6YFcJy6HrWbL7fKNLezumWQ4kuTFZ8/igbb6IjXl15H7ClELgdn5yF2WkCdMITNrqn9pn5dwIs0HfrKbpDC/QzOUdE1TQchHIkl6JxKcvbI00sFrWuSb/GmEEKxv9ZVY5CORFG3CfEhYA7Tn3QyX/tWs2m/R0qweHLHJ6gXiDg6GVXq+OcBn7zyXDbZeeganPACs8sWJcSakl4DbQZPP4M8vWcs5Ux5as8blVwXHymXTRgbA10qyYT1tYozN0cfo8WxRVTEr0bRBTY83cgj2/DS/2PqedvdMsNEwz6/ItZK/V5bJYOdFwEEp5WEpZQq4DbhhAfZbM1bUileqL24yUWFgZSrpGBnsuFw1pltXIGGoJ25sjZqFp+foM9OSNNyJAYZEC0II/EU+8ihuYrjy7amVXF7I59kNrZErNrXw7TdfXNK95/zXQ2wY40e38E+Ob+ejVv79Nwd48efv50v3HEJKycNHRjkyHKXDkylMAoEZDWO3qd5rkSg3eAoWeQonAZ8Xp8+0amwO9iRaWNlQe6SRFQnidtq4essMKd7FuIPKz3v0viLXyuyiVjxuF2lpL7LIdzNsa2GcAHfvHSCSzJAU5vdvulk6Qh6cdsHx0Vg+meWslaUJMsKsgLgxoEJuN7T6OTwUzd93o9FiIZ/dw6ccTrOgVDI6XnW9Q0NRmu0xbFYBqvZzcJAjZs4hkKeobstYzjur73NGrAqJ5eYoiA6BfwW2FjWIuUH0KFdhNZo3qolNvv96+OGb1JgJqs6PwyYIJzOsd5pCXjz+sOEauOSv1LSCJ5mFEPKVQHHR4W5z2eJhioNPKCG3wrVmJJMggWvG7MCZCHtWMSb9eDY9GwAj1jutK+lPDjJhToDsctiJ2wpTVyXyQl57vZjcLCzyk8bGa+BZb4DmjfhEMm+RHxmOYBPwr7/ex6939/OtB48RcDsI2ZP5ZCBQ0TCtARdNXqMkbthj2ImZ1yeKh0avQcjvISq80LSe4xPpWQ3enre6gYDbwTVnrpj9d73u2dDzmLLGhL10XtAa8LscJDDIJs3vtv8p9knlO/3RYypOPm59/+Z4h90mWNXo5fhILD+xxVQhx2YDXxuX+VRPYUOrn3Ayk++NDkdTKoHJ7lbJVPPFYZDEIBuvPo5zcDBCmzOOcJsPXjM2OzC+Nx/dAZTUmRnLecvPwzlXzMm4p8W8p+MqMszfiqdjS35xtmN6slgJ1oxNA7vVmIk5luWw2/L34Rr7iMo+Lq7O6GmAF30CFuE3umiDnUKItwkhHhVCPDo0NDTzBrPBFAcfagAwPAuL3JodaD48ve4WXpb7NK2rVb3yTjHCg4eLBoWkpCEzRNgoWEYZ0+cblW7iVs3qWQi5NGt32GeIWjmp2Ozw0s9D15V4RIp4Wj1Au8fiXHd2B+tbfXzyzn3c+VQff37JWmzp6LR60i0B17Q5UoH8TC8R6abB51SzlxOCtjNVSd4akoEsnHYbP3jHpXz0pbNPhGHdlcoae+xWaFqXD52sFa/hIIGTbCqu5hwd2ssDaSUMfzqkIkCs77/4oby6ycvx0Rg7T6iBzqB7eikG+4VvxH7wLujdmU9SsSY4GI2kWCHMGPIFsnQTdh8yUT2g4NBQhCZbrOBOaFhDxghxljjCY8eLi4SNqOxdYCTrodFXvdTErLCMhakWueXO8a+gYdWW/Axj3nUVIlYsmqYUEyvqTVjulQ4xMqdooIViIYS8Byg+g1XmshKklF+VUm6XUm5vbZ1F97YWTHFo9yiLsGaLPB0nLmuss1KFNz5nK1/8i+txBFYg7QYbjbFSIU9M4JYJYu5CokPOvIlLXCtz8pEvoZBbOD24SRFLZclkc/RPJFjT5OWdV2/k+GgMh83GGy/rMmuLB0o2fe1Fa/jzS6YPbgnz+oSlssgbvQbvle8iefVHGI4kZx1OuaU9SHO5B8ZMrL4EnF5Vh+OV35z15n6XgyQGuXQcHvgCObub2zLKBWd532KmkBdHT61t8nJ0OMoDh4a5bGOFXsAlf6HE8J5P5ScOPzysxGs0mqTTPoGYpSuoGmlHAFtqsmJtl/FYisFwkobcWGEibCEQqy/kcvseHj0yRcg7zoMXfoIfZa6kYSEtcuvYY1OmmSsS8rbGIN20MigbWLlmY/X9+VrAFSrkbJQR8iCxRfGFV2IhhPwRYJMQYp0QwgD+DPj5Auy3dmx2pNPLy7YpK65Wi1ymYsRkbSVsq9HgNdjWGQKbDRFcyQXeQR48PFq44c2kkpSv8KOyZnuJSjcJ5mORL6FrxcLhxmUK+UA4SSYnWdXo5aXndrKlPcBrLl5Dm99QRfqnWOSvunB12aQcm5kbEMFNo9dJo9fgocRajuesOuTzi4uvGcMLb78X3nFfSfJOrfhcdpLSiTPSA7tuY2jDyxghxPqiSoORnPr+i7/LNU1ewskM0VSW559ZwcftDsF5fw4Hf0u734nHaefQoDIGRqIp2sX4rMMlq5EzAnhzMSbLGEr/c99hzvvob2lkEk96HFoKs2nZz3wxa8UAAwd3FjaIj4K3mdzFf8nORBtNCynkqy5UD15zDtA8UVPIfa047TZ+Z7+Cn4qraZhp0FwIeMnn4EWfUu+L3EKWkLtJqQf+EjFvIZdSZoB3AncBe4HvSyn3zHe/s0UYPvx510ptFnkuFSNOjSVsa2XrSzkr+gDN4X1MxlU7pCnkssg6cnu8RKSbSXxIbKSEMavBTplRg1yOOrHIHWRJJZP5qcVWNnpw2G3c+e4rVSKG1c0t8pFXw+FVPtaodNPgNWgyu95WjP58Sg7MmpbplRNrxWf6yFuHH4Zskt0rbwbgmjNV76zFbxCTpm/cKJzTalMgPE47l5Yr6mXRuhlyGWyRXja2+dk/oK7PSCRFC7NPYKqKO0hAqEiaqTx+fIwWv4t/vcq8H4vD+c64Dolg1eDvSWXMDN3YCHibCScyZHOy7DRzc8buhHNeDft/DdGinrE1YG1m5/6s6c38vPnNtQ2ynnUTbHp+oe0ma5stIU8sbyEHkFL+Skq5WUq5QUr58YXY56wx/BiRXraJI7MS8gQqHHDBuOI9pJwhPuj4v/y8fkkzq1MU1dXwuxy8Nf1evp65Fq9hV5ELc7HIjfoQcoBMMkb3mHoYWRM+CCHUD8VyG1WbqqsIl5USbg52NppWkzX4N+MkGXWCJeQAtJzBvpy6B1567koMu43nb12Rd63ZXYVzsiy9Kze14K7m+rPS7ceOctbKILt7lOsjFplQUVwLKeSuIAFiZdPth8Mp1rf6eEGrORhaZJETaGe44RyeJx5mNJpSlR/j4+Btzv9GFnSwE+C816j5cIutcsu14lO9un+4dgsfuO7M2vdpuWyKhNx64LpkcvkLeV3g8iMO3c3PjH8iFaltMuSc6SP3zNNHXoKngZ4zXs9l9qeZHFODusnhY+SkwGgsCLnP5eCB3DYGaKLF7yKBe5YWuRJyh1EfrhVQ2YvWXIbTLGZrWjOj1EdeCZ8vSEbaTB+5M/9Df/z4GHabYMXUiSDqFK/TXpiAect19I7HafQ6OXtViN0feSGXb2whbgq9syifYX2rj/UtPl61fYYBtBIhDzERT9M9FscWnVu4ZDVsniABES/rWhmOJFVewPABJWhTBv5GV17N2bajTIwNm4lvskTIKxYumysrtqnxjT99Hoqzar3NymIHLtvQUr4WfSWcXhXrXyTkG1r9XLyuCbdMKDfcEnHqCPlV/wCbr8UhcipWtBZSceILEH44FWH+uGITygLIjp1ggEZC/sIXHSgqSdoacCmrbRZCTtZyrdSBoJkWuUzH6RmL0+J3Tbci9/wEELDm4pp22eA12JHbxi65gQZzsBPU1HLnrgphzLHs8GJjswnSNrPXtOV6+iYSdJgRN4bDRtDtJGG6VhxFQu522vn93z2H522t4B+3CK5SYZFjRzmrUw0QP9UzgTtRGNhbKOyeEH7iZS3yoUiSFr+hanc3b5wW3WMz5+CMjA/khbAv7c0L+YK6Viye9yEI98KD/63eRwbAN4/KikKoB0GRj9zttHP72y/FkT0FXCt1wZkvge1vUq/jtVnkVvjhfAc7p+IOqaicpCnkYvIEvbK5xOoormTX6ncRl7NzrVhC7qwj14pMx+kej013e2TT8Pi3YNMLSmpRVKPRa/D69Pu5LftcGrzOkvC0KypFcdQpEVuQCWcrdD6L3vF4yUBtyOPMu1YM9xyEwO5QNc3HjnJGewCHTXD/wWFa5JSszgXA8DYoizxWOklGIp0lnMioMNLhA2XT3d1B9ZtITAznhfzvf9XNk93KFbPgrhVQU6qd8WK4/3MQHTbT8+dZItfbXGKRAypBKKtdKwuHWdNA1FizRGTi5nydCyvkvkbll0xNqp6BM9JDr2wuCbHymUIuBDT5DaLSmFX4oTUHpLMuXCuFmts9Y/HpoYH771TW0PY31rxLy0ILuBw47baSH3rFcLw65RvuN/Cfq/4DbDZ6xuN0NhS+s6DHSb9sZEz6cc81w7ixC8aO4Xba2bQiwA8f7S7K6lw4H7nhbwAgHilNChqJmvNzerIwcQJapgu5N6S+s2S4IOSjMpCfvadxoV0rFs/7sOrp/uAW6H6k/OQRs8HbVHATWlg9ae1aWSBMIbcnx2ta3ZaxXCsLLeTqqZ+LDEMuhzvWz6CtrcRStSxyj9NOwOVQIWizsMhFNk1SOnA6Frbtc8J078i08pF3NU+5oZ/5jfpuNj6/5l1aD70G0xJ3O+14DTsep53z1zQsSLMXi6SnlWOynYlYmnAiw+qi6cqCbgffyr6QFyQ/PffoqcYuGDsKwFmdQVLZHM9uz6ixC3eo6qazIZ+mP2UMatjMJl2TNWvxt25mKn7zN5GNjJQI+a4T49htomSS8AWldTM86/WqzELzRnjOPGv6eZumW+SWD34e5bDny0m6ekuEKeTOVG3lYG3ZBHGMBfeR280JmUVshNhYH16ZpqlzfYnf2PKRew07XsNBTBrIdIyac/CySdI4cM2zGNaCYFrkblJkcpIrNk5J+Op5HFZuV26AGrHmbCy2xFsDLrqafaXztS4DfIaDaCrDCTOiZ3VT4Qcf9DhJ42CIhrnfh41dEBuGZJiXnNvJWCzF5Z4s5NoXLKsTyIeOpmKlqe/DZjhi1+DdqiLm2sunbeoKKIs8FxsFM1V/jADJTI5mn7FwdVbKcfUH1ADrlX9Xc/hrRcq5VtJmT9o5z33Pg1NLyE3rw5WpQcizaWwyS1wuvEWO00sSA1tilAee2MU1wLatpenhlmvFazjwuezEcJFLjVBrS0Q2RZqTMEA0F5wFIQ+4HWzvKirbmYzA0F41hjEL8hZ5kZB/7tXnLXx0wyLgc9kZjqQ4MWqFZhYscqfdhtewE0tl534f5iNXjvHszWfx7M2tcOtHZl1yd0bMZK5UfLqQC3I0H/45bHxePryvBDMOX8THIJYmjoukGa1z0twqFv42eOWtC7Mvb7MKncxmCoaJ1ZNeQov81HKt2B0k7D7cmRommDD9WidjsBMhmLSFMJJj7N33NACbNm4pWcXvKljkPpdD1duYhY9c5NKk6uU5XCTkz96ksuby9O1SJUXLzGJfDavKYvFs6uevaVSTAywzGr0Gw5Fk3iJfM8X1ZJ3rnMdqmsy66cMHCsvC/QsbQw55H3AmUVrDZDiS4hLbXhzRPjjnVeW3tdkJCx/25BgyNsKYLIShNp6MiJWThbcZkKVzB1iulfla+/Pg1BJyIOUM4ZcRkpnqEwFbT9GFKJpVjpgjhDs9hjTn8BQNpXG1lmvFYyjfbwLX9JnWq2DLpsiIOhFyM47cLVJcvaUNTjwMD35JFRPpeUyts3J2pTwNh40WvysfqrecObMjSN9Egl0nJgh5nNMKYFnv52xQtG1T8c3WtQZTyBcuYgXIC1V2ipAPhZNc53xCRW1svrbi5lFbEGdqgkx4iFHpz4+lLGidlZONVRe/KAQxP9ipfeQLR9oI0RCNEk5kcPmr/DDMi58ULgz7wj/P4s5GPNFx/Jl+Ui4vxpRSor4ii9zvctCNgW0Wg522XD25VtQP8sKVbl6wbQXc+XHY9V2VeNHzKDSsLd/dnoEfvONSmv3L6EdegbNXKZff7/cNsqFtutUW9BTuhTnhMFTd9BMPq/fJCKTCC1KHvATTtSKTpT3H4UiSZzsm1NR4VSI34o4Q7vQE2bEYfbKZKze1cnTk2MLWWTnZeC0hL/KT54VcR60sGBmjgQYRmTlN3xTNnH0BC9oX797VSDA3QacYIentmDbo5C/ykavBTjdCZlTMdQ2IXJq0qJMfgBm18qpzW5V1mTRdW3e8V03ptWr7nHa7rsVXtnzrcmNbpyoAFk9nSyJWLAqulXnYVasvhL6dKiz1xENqmTVV3UJhWuQiPdW1kqTZHpux7nnKCOHLTuKcPM5x2caV5oQfDQtZwvZkY6XpTxYVeNVCvvBIdwMNRGaugGgKuTxJmZFZdzONIswZ4gSyedO0z63Z7S2LPF+Po0Y/uT2XIiPq5AeQjyM3exTJMLRthas/CC/8BDz/X5aubXVAwO3M1wu3anMUk3etzKdUxKqLVJJY35Pwp8+pgc4zKrs55oQpVLZMjFyuUMp2OJIiJGYu45p1NbJG9mLPxjkhWzlvdQO3XNbFC7ctsC//ZNJ2psqWfeLbhWV5H/nSCfkp51oR3gZCIkp/jRa5dJyki+9rJijiBEWc1LrpaelWoS6vYcfrshdmiUnHa6q0Z8vVkY/c7gCbs+DjT06q2dqv+n9L26464pyVIQ4PRcsL+XwHO0HNLwpw32fgyL3q4elY4Kxf0yL3ygQPHx0ll5Ncsr6ZwckEASMyo0Uu3SozFKBXrKDF7+LDc5nsYylxuFQd+Ls/DL07ofO8oqgVbZEvGDZvEyGihOOp6iuaF1+cpAEKW5FP2Oi6ZNrnDruNkMdJg9fA73LkS5nWWm/FnkvXj0UOaqAnbaZuJ8PTJpA43bGmaltdpmrjuhYfrQEXrvnUjwm0Q9N6OHCnGpO44Ja576sSdidZm4FXJHnP7Tt5y7ce5a49/UwmMgRkZGYDxHJLAMnAamz1kAMxF7a/Sc1g9dCX1ft8HLm2yBcMp78Zp8gSi04CVWZMNwXzZAm5M6CSYjLYcXScV3ad7771YjpDHhx2ka+AV7OQyzQZUUdi6Siq3qiFfBov2NrO7/cNct7qhmmf/fkla3nFBavmP1bzup+qsLjWM9UA6Ekg6/DiTSXonVAP7f/3wydpcNtxZsIzWuQOXyG/ILBiQ5U16xx3CDa/CA7+TkVm6TjyhccTVJZwf39f9RUti9x1cp6irpBKSR7wbKzoO9vWGaLRZ5jzOs5uAma7TJGz1ZNF7oZMkUXuDi5te+qMNc1evvvWS8qG2tltIh/FNC8a16paIidJxAGk04tPqEzOoNtBOJnh1ec2ImRuRovcaWZ3DskQr7h0ehr/sqLrCjXj0PAzalzL6V3YLNpZcsoJuSugwoMe3nOwZEBmGqb1aDtJAxShZhX6FW49f8Z17TZBzu4uaddMOHJpsnXlWvGqh1A2rc7BpYX8lMTw4yWBTcCnXn4OLoeNV59l9r5msMjdppE1aG/n2ZsWeN7exabrCvX/6H3qfl9CtwqcgkJu1VtJRUd58MhI5fVMy9fhOjnZWM0rNxFp2MK6Z7+2pvWl9UCp2SLPkLXVSfghmK6VuLLGQbtWTlGEy4eXJGevDHHd2R3s+cgLWe83I8RmsMgbTOPGu2LD8vWPWzSth0AnHL1f3fdayBcYU8jbnXF+/HhP5fXMCAuH+ySl1Rpe/H/zEK6Nz65p9XzPoMbwQ2fduVY8yrWSF3JtkZ+K2F1+fCLBJeY8og67TdUegRktcq9Zp79r49aT2MJFQghllR+9X81Hu4Shh3AKC/m5zdn8RL1lScfJSNvca0AvMMKqnFajRe4gTa6eLHKnx7TIzWuuLfJTErvLz5nNdv7yqo2FhVbdkZmiVoKdsPoSxIbnnqzmLS6rL1J+8pHDSzrQCaeikPtXgKeJLZn9xFKVY8llKkYc1/yy6RYQuzU7TPcj8MAXZ1zfITP1ZZE7plrkWshPSQwffpEgVFzoqkaLHIcL3nwXdE0vc7ssaTYfZsMHlrSELZyKQm6zw8bnsS36EPFEsuJqmWT0pEwqMVfsVuW0x78Jd31QhTVVwUkaaa+P3gSgolbSsYKQ66iVUxPDN939V6tFfqrRtF79z6W1RX5S2PxC/Nlx1qf2V1wlm4gQOxm1yOeIq2S+Rlm95kouh0EGWU8WuZUQlLBcK1rIT0kMXyEl3SI+riaANotqnTaEVqmMZtA+8pPCxmvIYecK+RjZCiGIOdMi98ynvsUC4nE5C/VWQE3mWonYsPpnNFZeZ7FxeNQAsvaRn9oYPjW4V9xjTIwra3wJ46iXBJu9MKmHjlo5CXgaGQydzSW2vUQr+MllKkoM18IkYiwAPpcjP5s6AJkqJQbCKtkp5prnjOALiVOHH54WGD5AQv9T0G3WP4+Pz+wfP1Wx3CtayE8OKU8rIaJEk1WEXLpOyqQSc8HnsjMmA0ib+WCpZpFPKiFPuOsoqSI/2DmputlLfGNrThKW++SXfwO33awsc8siPx3JC7n2kZ8cDB8+ESeaLD9TkEirqJV5lQ5dQLyGg7ek3kvm6n9SCzJVhLwuLXLzRo4MKmv8dOtmny5YD+j+pyAyoFLUT2eLvNmsGbOE07zBKS3kAXwkKlrkIh0jhmvuM5cvMH6XgyOyg4THrM2crexakeE+clKQrieLvETI9UDnKYslWNb9efTe09wiNyfv0K6Vk4PN7VdCXmGCCVsmVleuFSt6JinNB0sVizw93sMIQXzekzMpxpww5+0kOqhDD09lpkamHL5HTXt2ulrkTfVhkdeHOXoScLgDOESOeKJ8pqQ9EyeOu27CD62p32I5y0de2SLPTfQxIBvraxo0yyKJDELDmqVti+bkURxmt+Js2PsL9XrdlUvTnqWmaR3c9DXY+LwlbcYpa5E7PCpqIhmdmP6hlDiycaJ1lBDkNYU8kTPbU9VH3k+/bMxP2lsXuExLLTKgI1ZOZfKWp4Dtb1QvL/4L2PayJWvSknPOqwqTMi8RdaQEC4vTq7r3yViZeiuZBAJJXLrrxkfuMx8o0ezMUSv2aD+D8ly66skiX3eVqnMTH9NCfipjuVYa1sD5f65m/dly/dK2SXPqWuSGKeSZeBkhNzPTEsKFMZ/ptRYQK549nrUs8gqulUwKZ2JEuVY8dSTkLj9c8lfma+0jP2WxLPKWzap2yrYb1ZytmiWlPlTsJODymEKeiEz/0JxjL+NY2tjPYnxmzyBquVYqWeSRAQAGqDMfOcBFbwVvi/aRn8pYYyEty3yGn1OMU/ZRanOrLmAuEZ7+oVX0x1E/SSs+lxLwSGYGizzcD1B/PnJQIWjv3rnkoViak4grAJe/G856xVK3RFNEnSnBAmL68mSyjEVuulbkEpeeLMZyrYSzM1jk4V4ABmVjPtKlrtD+8VMbIeD5H13qVmimcMq6Vixfniw3447pWjlZEy/PBZfDht0mGE+YGZGVolYigwBEnU1qdhaNRnPaMy8lEEK8UgixRwiRE0JsX6hGLQimZShSlS1y2xIH8RcjhMBr2PnGQ+b0dJXiyKOq8mHWvbThThqNpn6Yr0m3G7gJuHcB2rKwmCIt0lGklMjispumRW47SRMvz5WAy0FCqgHMXDpRfqXYMFFbAJ+nfgZqNRrN0jIvIZdS7pVSVp69YSlxuMliw5aKcMW//oH/ue9I4TPTIne466sQ/kduOIvnn70agGyqgpBHhxm3hepvoFOj0SwZi+ZkFUK8TQjxqBDi0aGhocU4IEmbh2QsTM94nC/84SCRZAZyWTUlGeCsMyF//tYVnLe2hYy0kU1X8JFHhxkjWH+hhxqNZsmYUciFEHcLIXaX+bthNgeSUn5VSrldSrm9tXVxqval7D68UtVamYin+d8v/xvxj3YSP7ELAMNTfxEWhsNGCmdV18qwDNZXMpBGo1lSZuyfSymXthrMPMjYPXhFgguNY1zf1M2fjX4Vl0gTPfonclLg8tSXjxzAZbeRwoGjUtRKdJih7GqCbu1a0Wg0ilNaDTIOH02E+Y7tnzHG08QCayFyDE/kODFc+OtQDC2L3FbOtZLLImMj9Gd82iLXaDR55ht++DIhRDdwKXCHEOKuhWnWwpB1eDnTdhyDNFz37yTf9gDDMoiNHPG6FnIHspxFHh9DIBmWIe0j12g0eeYbtfITKeUqKaVLSrlCSvnChWrYQpAz/DQKM4585QUE/V66pfLPx6QrX9+knjDsNpLSWV7IzRjyURnQUSsajSbPKZ0aWDKY2bwRu03Qb1dTqdW3a6WCRR5V0T7DaItco9EUOKWFvK2lRb3wt+enHxtxdgAo10od1iqxfORlU/RjxRa5FnKNRqM4pYVc5Gsnb8ovm3SvApRrpR6F3GlGrZRN0c+7VnQcuUajKXBKC3m+El/zxvyimE8JeRx3XQq5y2EjJStY5JaQo33kGo2mwKkt5JZFXiTkmaCa9CCGK186tp6wfOSinEUeGybhCJHFTovftfiN02g0dcnpIeRFrhXRsJK0tBOvo4mXizHsykdeVsijw0QcDXgNe10+hDQazdJwagt5YxfYXdB+dmGRz8t/ZV7G72yXI4RYurZVIG+R58oIeXyMSQK0BrQ1rtFoCpzaZt36q+HvD+YjVgAafQYfz95Eh9+9hA2rTD6zs5xFnooSli7tVtFoNCWc2ha5ECUiDtDoVdEe9eqaMBw2ktKBrZxFnooymTVo1UKu0WiKOLWFvAyNPgOgLiNWoOAjzwv57h/Bf5ytolhSUcYzBi0BY2kbqdFo6orTTsibvMtDyO2WkD/w3zBxHGIjyHSUsYxBa526hTQazdJw2gm5ZZH7XPUXsQJgswkywok9l4bhZ6DnUfVBMgLJKDHcerBTo9GUcNoJedDtwG4T+F31mxmZE05sZOGJbxcWJiYQ2QQx6aLFr10rGo2mwGkn5EIIzl0VYkt7/c0OZJG1m0K995dgmO2M9AMQxaUtco1GU0J9OopPMj/+y8uXuglVydkMyAJjR2HVhXDiQQgrIY/h1uGHGo2mhNPOIl8O5GymRS6z0Hameh0ZAFSxL22RazSaYrSQ1yF51wrAim3qv2mRS8OH21mfA7UajWZp0EJeh0hbkZC3bVX/TYvc6a5f375Go1katJDXI8UWectmELa8RW54/EvUKI1GU69oIa9DpCXkDg/4WlTkSmQQALtbC7lGoylFC3k94jAHM0OrVL0YVwCippB7tGtFo9GUooW8HrEs8obV7Dg4THfMDjIHgFNb5BqNZgpayOuRvEW+mq/ce5ihVCEL1fBqi1yj0ZSihbwOEQ5VFCvq6eS+Z4YISw8AWSlwu31L2TSNRlOHaCGvQ1JGAwAPhZvJSYhiCjtu/O76rRGj0WiWhtMyRb/eiXk6uNn+GXKDZ+J2ThJFWeRxXPjd+ivTaDSlaIu8DnE5bOzJrqZnIsmZHcG8ayUq3QTqtI66RqNZOrSQ1yGGw0Yqm2MkkmJ9iz/vWonh1ha5RqOZhlaFOsSw20ikVbjhuhYvUdMij+Gq25mNNBrN0qEt8jrEaS98LW1BN2m7F4CYdBPQFrlGo5mCFvI6xHAUvpYWv0HWUElAUVx1PbORRqNZGrSQ1yHFQt7kc4Ep5HHtI9doNGXQQl6HFAt5s89AuFQ2ZwwXPkPXItdoNKVoIa9DXEU+8ma/gc0TBCBt9yKEWKpmaTSaOkULeR1iWeQepx2v4cBhVjzMmoOeGo1GU4wW8jrEEvJmv6qC6PSGAMg5tZBrNJrpzEvIhRD/JoTYJ4R4UgjxEyFEwwK167TGsFtCrqog2gIdfCb9Cp7wXbGUzdJoNHXKfC3y3wJnSSnPAQ4A759/kzR5i9ynLPKQ1+C/sjcR961aymZpNJo6ZV5CLqX8jZQyY759ENBKswBMFfKgR8WO6zorGo2mHAvpI38TcGelD4UQbxNCPCqEeHRoaGgBD3vqUfCRK9dKyBRynZ6v0WjKMaMyCCHuBtrLfPRBKeXPzHU+CGSA71Taj5Tyq8BXAbZv3y7n1NrThLyP3HKtWEKuk4E0Gk0ZZlQGKeXzqn0uhLgFuB64RkqpBXoBsCzvtqCyyIMeR8lyjUajKWZeyiCEeBHw/4CrpJSxhWmSpqvFx9dev52rNrcC2rWi0WiqM19l+ALgAn5rZhw+KKV8x7xbpeH5W1fkX7cH3bz7mk286KxyHi6NRnO6My8hl1JuXKiGaCojhOBvn795qZuh0WjqFJ3ZqdFoNMscLeQajUazzNFCrtFoNMscLeQajUazzNFCrtFoNMscLeQajUazzNFCrtFoNMscLeQajUazzBFLUR5FCDEEHKvwcQswvIjNqXf09ShFX49S9PUo5VS/HmullK1TFy6JkFdDCPGolHL7UrejXtDXoxR9PUrR16OU0/V6aNeKRqPRLHO0kGs0Gs0ypx6F/KtL3YA6Q1+PUvT1KEVfj1JOy+tRdz5yjUaj0cyOerTINRqNRjMLtJBrNBrNMmdGIRdCrBZC/EEI8bQQYo8Q4t3m8iYhxG+FEM+Y/xvN5a8VQjwphHhKCLFDCHFu0b5eJITYL4Q4KIR4X5VjvsHc7zNCiDcULf+4EOKEECIyQ5svMI9/UAjxeWFOXySE+BezbTuFEL8RQnTOfInq+nr8Wgixy2zHl4UQ9grbf0MIMSiE2D1l+e3mtdgphDgqhNi5zK/HPeb21jm1ldnWK4S4Qwixz2zvp4o+W2OeyxNmG69b5tfjZnO/T5r3SkuF7cseRwhxX9G17BVC/HSZXI9fCyHGhRC/nLJ8nRDiIXP724UQRoXty+qMEGKtEOJ3ZvvuEUKsmu31OGlIKav+AR3As8zXAeAAsBX4NPA+c/n7gH81X18GNJqvrwUeMl/bgUPAesAAdgFbyxyvCThs/m80X1v7u8RsT2SGNj9sriuAO4FrzeXBonXeBXx5pvOv8+sRNP8L4EfAn1Vo87OBZwG7q5zXZ4B/XubX4x5g+wzt9QJXm68N4L6i++OrwF+Yr7cCR5fr9UDN/jUItJjrfRr4cJntaz3Oj4DX1/v1MNe9BngJ8Mspy7+P+RsBvmx912W2L6szwA+AN5ivnwt8e7bX42T9zX4D+BnwfGA/0FH0Ze0vs24j0GO+vhS4q+iz9wPvL7PNzcBXit5/Bbh5yjoVhdxsy75K+5ty/C/N+wLWx/VwAr8AXl2lnV1UEHLUg+AEsGk5Xw9qEPIy+/tP4K1F+/qHovbsWK7Xw7wnhoC15vf7ZeBtZbaf8ThAEBijyBCq1+tR9PlzKBJy8xoMA45y+6uwj6lCvgdYXbS/yflej4X6m5WPXAjRBZwPPASskFL2mR/1AyvKbPJmlEUMsBIlFhbd5rKp1LpeJVaa25Td3uo2Aa8F/nkW+51GPVwPIcRdKMsrDPxw1iehuBIYkFI+M8ftrbZ0sfT3x/+aroB/EkK51Kq0twFluf3OXPRh4M+FEN3Ar4C/rrb9TCzl9ZBSpoG/AJ4CelFW8Ndr3X7KOjcCv5NSTpbZvmYW6XpUohkYl1Jm5rg9qF7ATebrlwEBIUTzLPdxUqhZyIUQflT36m+mfqFSPaLklPWvRn0R/7AA7VwwpJQflFKuBr4DvHOu+6mX6yGlfCHKonGhuntz4Wbge/NpR51cj9dKKc9GPZiuBF5Xpb0O1Dl/Xkp52Fx8M3CrlHIVcB3wbSHEnAIClvp6CCGcKCE/H+gEnkRZsXPhVLk/5svfAVcJIZ4ArgJ6gOzSNklR001q3hQ/Ar4jpfyxuXhACNFhft6Bsgqt9c8B/ge4QUo5Yi7uAVYX7XYV0COEuLhoQOWlldar0jZ70fYfNdctHoSotP13gJfPdO4VjllX10NKmUB1WW8wB5es7d9Rw7k4UFbG7bWef5l91MX1kFJa/8PAd4GLytwfFl8FnpFSfq5o2ZtRflSklA8AblQRpuV4Pc4zz+OQKZTfBy4rc39Uvb/MAdKLgDtmex2W6HpUYgRoMO/34u0r3R/TkFL2SilvklKeD3zQXDY+8xVYBGrwaQngW8Dnpiz/N0oHKz5tvl4DHAQum7K+AzUQs47CYMW2MsdrAo6g/GON5uumar6rMvuYOth5nbl8U9E6fw38cLa+qHq5HoCfgo/RgRLid1ZpdxdlfOTAi4A/zvY61OH1cFAY2HOi3EzvqNDmj6GExTZl+Z3ALebrM1EuCbFMr0cn0Ae0muv9C/CZMttXPQ7wDuCby+X+KFr/OUwf7PwBpYOdfzlD26f6yFusewb4OPDRuV6Xhf6r5Yu4AtXteRLYaf5dh/I5/Q54BrgbU2xRT9KxonUfLdrXdahR60PAB6sc803ml3kQeGPR8k+jfFs58/+HK2y/HdhtHucL1o8R9ePdbZ7LL1C+xNnemHVxPVA+xUfMduwG/gtzIKfM9t9D/ajT5nV7c9Fnt1JB8JbZ9fABj5nt2IMaxLSX2XaV2d69RW14i/nZVuBPKJHYCbxguV4Pc/k7zPO07vfmCttXPA5qAPlFy+z+uA810BtH3e8vNJevRxl5B1Gi7qqwfVmdAV5htveA2c6y2y/Fn07R12g0mmWOzuzUaDSaZY4Wco1Go1nmaCHXaDSaZY4Wco1Go1nmaCHXaDSaZY4Wcs0pjxAiayZ87BGqWuR7Z8rYFEJ0CSFes1ht1GjmgxZyzelAXEp5npRyG6pg07XAh2bYpgvQQq5ZFug4cs0pjxAiIqX0F71fj0qmakFVB/w2KqEIVHbsDiHEg6jsziPAN4HPA59CZQy6gC9KKb+yaCeh0VRBC7nmlGeqkJvLxoEzUFUjc1LKhBBiE/A9KeV2IcRzgL+TUl5vrv82oE1K+TEhhAuVAfpKKeWRRTwVjaYsjplX0WhOaZzAF4QQ56Eq2W2usN4LgHOEEK8w34eATSiLXaNZUrSQa047TNdKFlVx70PAAHAuaswoUWkz4K+llHctSiM1mlmgBzs1pxVCiFZU5bsvSOVXDAF9Usocqn65Ne9pGDU1mcVdwF+YJVkRQmwWQvjQaOoAbZFrTgc8Qk0s7QQyqMHNz5qf/TfwIyHE64FfA1Fz+ZNAVgixC1Uh8j9RkSyPmzMPDaFmztFolhw92KnRaDTLHO1a0Wg0mmWOFnKNRqNZ5mgh12g0mmWOFnKNRqNZ5mgh12g0mmWOFnKNRqNZ5mgh12g0mmXO/wf168EH0lrgKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_portfolio.rolling(7).mean().plot()\n",
    "titlee = country + ' returns portfolios'\n",
    "plt.title(titlee)\n",
    "plt.savefig(f\"plots_stocks/{country}_portfolio.PNG\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_portfolio.to_csv(f'datasets/{country}_portfolio.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EW    1.519515\n",
       "VW    1.623051\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_portfolio.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## USA  <a class=\"anchor\" id=\"usa\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "country = 'USA'"
   ]
  },
  {
   "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>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>FB</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>BABA</th>\n",
       "      <th>TSM</th>\n",
       "      <th>BRK-A</th>\n",
       "      <th>...</th>\n",
       "      <th>TMO</th>\n",
       "      <th>XOM</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>ABBV</th>\n",
       "      <th>ORCL</th>\n",
       "      <th>CVX</th>\n",
       "      <th>ACN</th>\n",
       "      <th>AVGO</th>\n",
       "      <th>QCOM</th>\n",
       "      <th>DHR</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.972216</td>\n",
       "      <td>-1.245165</td>\n",
       "      <td>-1.213905</td>\n",
       "      <td>-0.490720</td>\n",
       "      <td>-0.523134</td>\n",
       "      <td>-0.529126</td>\n",
       "      <td>2.963319</td>\n",
       "      <td>-1.260411</td>\n",
       "      <td>-3.297798</td>\n",
       "      <td>-0.907495</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.087714</td>\n",
       "      <td>-0.803941</td>\n",
       "      <td>-1.631553</td>\n",
       "      <td>-0.949181</td>\n",
       "      <td>-0.352180</td>\n",
       "      <td>-0.345870</td>\n",
       "      <td>-0.166554</td>\n",
       "      <td>-2.543500</td>\n",
       "      <td>-1.882957</td>\n",
       "      <td>-0.618931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>0.796824</td>\n",
       "      <td>0.258478</td>\n",
       "      <td>1.488559</td>\n",
       "      <td>2.465710</td>\n",
       "      <td>2.665406</td>\n",
       "      <td>1.883360</td>\n",
       "      <td>1.925466</td>\n",
       "      <td>-0.165899</td>\n",
       "      <td>-1.153980</td>\n",
       "      <td>0.311067</td>\n",
       "      <td>...</td>\n",
       "      <td>0.718666</td>\n",
       "      <td>0.767805</td>\n",
       "      <td>0.356914</td>\n",
       "      <td>0.789172</td>\n",
       "      <td>0.520838</td>\n",
       "      <td>-0.338816</td>\n",
       "      <td>-0.652990</td>\n",
       "      <td>-0.149599</td>\n",
       "      <td>-0.586073</td>\n",
       "      <td>0.298422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>-0.470300</td>\n",
       "      <td>-0.911767</td>\n",
       "      <td>0.209156</td>\n",
       "      <td>-0.062401</td>\n",
       "      <td>-0.193165</td>\n",
       "      <td>0.216365</td>\n",
       "      <td>3.880053</td>\n",
       "      <td>0.456982</td>\n",
       "      <td>1.620494</td>\n",
       "      <td>-0.384762</td>\n",
       "      <td>...</td>\n",
       "      <td>0.565908</td>\n",
       "      <td>-0.818406</td>\n",
       "      <td>-0.648539</td>\n",
       "      <td>-0.570468</td>\n",
       "      <td>0.222054</td>\n",
       "      <td>-1.276949</td>\n",
       "      <td>-2.159017</td>\n",
       "      <td>-0.344250</td>\n",
       "      <td>2.843615</td>\n",
       "      <td>0.983131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>1.608636</td>\n",
       "      <td>1.592822</td>\n",
       "      <td>-0.780866</td>\n",
       "      <td>0.788033</td>\n",
       "      <td>0.711776</td>\n",
       "      <td>1.013801</td>\n",
       "      <td>4.920485</td>\n",
       "      <td>0.170011</td>\n",
       "      <td>0.737305</td>\n",
       "      <td>0.084685</td>\n",
       "      <td>...</td>\n",
       "      <td>0.159027</td>\n",
       "      <td>-1.508025</td>\n",
       "      <td>0.063177</td>\n",
       "      <td>0.708743</td>\n",
       "      <td>0.389472</td>\n",
       "      <td>-1.142274</td>\n",
       "      <td>0.196167</td>\n",
       "      <td>-1.247453</td>\n",
       "      <td>-0.292243</td>\n",
       "      <td>0.102465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL      MSFT      AMZN      GOOG     GOOGL        FB  \\\n",
       "Date                                                                     \n",
       "2020-01-02       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2020-01-03 -0.972216 -1.245165 -1.213905 -0.490720 -0.523134 -0.529126   \n",
       "2020-01-06  0.796824  0.258478  1.488559  2.465710  2.665406  1.883360   \n",
       "2020-01-07 -0.470300 -0.911767  0.209156 -0.062401 -0.193165  0.216365   \n",
       "2020-01-08  1.608636  1.592822 -0.780866  0.788033  0.711776  1.013801   \n",
       "\n",
       "                TSLA      BABA       TSM     BRK-A  ...       TMO       XOM  \\\n",
       "Date                                                ...                       \n",
       "2020-01-02       NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "2020-01-03  2.963319 -1.260411 -3.297798 -0.907495  ... -1.087714 -0.803941   \n",
       "2020-01-06  1.925466 -0.165899 -1.153980  0.311067  ...  0.718666  0.767805   \n",
       "2020-01-07  3.880053  0.456982  1.620494 -0.384762  ...  0.565908 -0.818406   \n",
       "2020-01-08  4.920485  0.170011  0.737305  0.084685  ...  0.159027 -1.508025   \n",
       "\n",
       "                CSCO      ABBV      ORCL       CVX       ACN      AVGO  \\\n",
       "Date                                                                     \n",
       "2020-01-02       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2020-01-03 -1.631553 -0.949181 -0.352180 -0.345870 -0.166554 -2.543500   \n",
       "2020-01-06  0.356914  0.789172  0.520838 -0.338816 -0.652990 -0.149599   \n",
       "2020-01-07 -0.648539 -0.570468  0.222054 -1.276949 -2.159017 -0.344250   \n",
       "2020-01-08  0.063177  0.708743  0.389472 -1.142274  0.196167 -1.247453   \n",
       "\n",
       "                QCOM       DHR  \n",
       "Date                            \n",
       "2020-01-02       NaN       NaN  \n",
       "2020-01-03 -1.882957 -0.618931  \n",
       "2020-01-06 -0.586073  0.298422  \n",
       "2020-01-07  2.843615  0.983131  \n",
       "2020-01-08 -0.292243  0.102465  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_returns[country].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EQUALLY WEIGHTED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_returns[country] = df_returns[country].iloc[1:] # ignore first row\n",
    "ew_portfolio = df_returns[country].mean(axis=1).values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VALUE WEIGHTED\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# names two groups\n",
    "stocks_60_names =  ['BRK-A', 'AMZN', 'GOOG', 'GOOGL', 'NFLX']\n",
    "stocks_40_names = [ stock for stock in df_returns[country].columns if stock not in stocks_60_names]  \n",
    "\n",
    "# stocks value two groups\n",
    "stocks_60 = df_returns[country][stocks_60_names]\n",
    "stocks_40 = df_returns[country][stocks_40_names]\n",
    "\n",
    "# mean each group\n",
    "stocks_60_mean = stocks_60.mean(axis=1).values\n",
    "stocks_40_mean = stocks_40.mean(axis=1).values\n",
    "\n",
    "# weight 60 40:\n",
    "vw_portfolio = stocks_60_mean * 0.6 + stocks_40_mean * 0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>EW</th>\n",
       "      <th>VW</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.898291</td>\n",
       "      <td>-0.879362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>0.455514</td>\n",
       "      <td>1.309527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>-0.173111</td>\n",
       "      <td>-0.292702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>0.555981</td>\n",
       "      <td>0.622157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-09</th>\n",
       "      <td>0.517048</td>\n",
       "      <td>0.538031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  EW        VW\n",
       "Date                          \n",
       "2020-01-03 -0.898291 -0.879362\n",
       "2020-01-06  0.455514  1.309527\n",
       "2020-01-07 -0.173111 -0.292702\n",
       "2020-01-08  0.555981  0.622157\n",
       "2020-01-09  0.517048  0.538031"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build dataframe\n",
    "df_portfolio = pd.DataFrame(index=df_returns[country].index)\n",
    "df_portfolio['EW'] = ew_portfolio\n",
    "df_portfolio['VW'] = vw_portfolio\n",
    "df_portfolio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": [
    "df_portfolio.rolling(7).mean().plot()\n",
    "titlee = country + ' returns portfolios'\n",
    "plt.title(titlee)\n",
    "plt.savefig(f\"plots_stocks/{country}_portfolio.PNG\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_portfolio.to_csv(f'datasets/{country}_portfolio.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EW    2.163475\n",
       "VW    2.072784\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_portfolio.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## JAPAN  <a class=\"anchor\" id=\"japan\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "country = 'Japan'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>TM</th>\n",
       "      <th>MITSUB</th>\n",
       "      <th>HMC</th>\n",
       "      <th>1JP.F</th>\n",
       "      <th>NPEXY</th>\n",
       "      <th>ITOCY</th>\n",
       "      <th>NSANY</th>\n",
       "      <th>SFTBY</th>\n",
       "      <th>HTHIY</th>\n",
       "      <th>JXHLY</th>\n",
       "      <th>...</th>\n",
       "      <th>NHOLF</th>\n",
       "      <th>SSUMY</th>\n",
       "      <th>MZDAY</th>\n",
       "      <th>KAEPY</th>\n",
       "      <th>MAHLY</th>\n",
       "      <th>SMTOF</th>\n",
       "      <th>FUJHY</th>\n",
       "      <th>CHUEF</th>\n",
       "      <th>EJPRY</th>\n",
       "      <th>NEC1.F</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-1.047525</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.780734</td>\n",
       "      <td>1.197594</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.579653</td>\n",
       "      <td>-0.687285</td>\n",
       "      <td>-1.409724</td>\n",
       "      <td>-1.206084</td>\n",
       "      <td>-2.065219</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.546737</td>\n",
       "      <td>-0.941174</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.803207</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.994039</td>\n",
       "      <td>-1.621620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>0.014212</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.284390</td>\n",
       "      <td>-1.183421</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.604618</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.398527</td>\n",
       "      <td>1.481568</td>\n",
       "      <td>5.105441</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.844264</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.487781</td>\n",
       "      <td>-0.564808</td>\n",
       "      <td>-0.161947</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.401602</td>\n",
       "      <td>3.296692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>0.525674</td>\n",
       "      <td>1.196250</td>\n",
       "      <td>0.570409</td>\n",
       "      <td>1.796405</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.214635</td>\n",
       "      <td>0.865047</td>\n",
       "      <td>0.519850</td>\n",
       "      <td>-0.443831</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.402416</td>\n",
       "      <td>0.475064</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.892133</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.268815</td>\n",
       "      <td>3.191494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>-0.247327</td>\n",
       "      <td>-1.559015</td>\n",
       "      <td>-0.283590</td>\n",
       "      <td>-1.176478</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.385522</td>\n",
       "      <td>0.171531</td>\n",
       "      <td>0.517153</td>\n",
       "      <td>-0.305017</td>\n",
       "      <td>1.583941</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.404042</td>\n",
       "      <td>-0.472818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.245501</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.335124</td>\n",
       "      <td>2.577319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  TM    MITSUB       HMC     1JP.F  NPEXY     ITOCY     NSANY  \\\n",
       "Date                                                                            \n",
       "2020-01-02       NaN       NaN       NaN       NaN    NaN       NaN       NaN   \n",
       "2020-01-03 -1.047525       NaN -1.780734  1.197594    0.0 -0.579653 -0.687285   \n",
       "2020-01-06  0.014212       NaN -0.284390 -1.183421    0.0  0.604618  0.000000   \n",
       "2020-01-07  0.525674  1.196250  0.570409  1.796405    0.0  0.214635  0.865047   \n",
       "2020-01-08 -0.247327 -1.559015 -0.283590 -1.176478    0.0  0.385522  0.171531   \n",
       "\n",
       "               SFTBY     HTHIY     JXHLY  ...  NHOLF     SSUMY     MZDAY  \\\n",
       "Date                                      ...                              \n",
       "2020-01-02       NaN       NaN       NaN  ...    NaN       NaN       NaN   \n",
       "2020-01-03 -1.409724 -1.206084 -2.065219  ...    0.0 -1.546737 -0.941174   \n",
       "2020-01-06 -2.398527  1.481568  5.105441  ...    0.0  1.844264  0.000000   \n",
       "2020-01-07  0.519850 -0.443831  0.000000  ...    0.0 -0.402416  0.475064   \n",
       "2020-01-08  0.517153 -0.305017  1.583941  ...    0.0 -0.404042 -0.472818   \n",
       "\n",
       "            KAEPY     MAHLY     SMTOF     FUJHY  CHUEF     EJPRY    NEC1.F  \n",
       "Date                                                                        \n",
       "2020-01-02    NaN       NaN       NaN       NaN    NaN       NaN       NaN  \n",
       "2020-01-03    0.0  0.000000  0.000000 -0.803207    0.0 -0.994039 -1.621620  \n",
       "2020-01-06    0.0 -2.487781 -0.564808 -0.161947    0.0 -0.401602  3.296692  \n",
       "2020-01-07    0.0  0.000000  0.000000 -0.892133    0.0  0.268815  3.191494  \n",
       "2020-01-08    0.0  0.000000  0.000000  0.245501    0.0  0.335124  2.577319  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_returns[country].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EQUALLY WEIGHTED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_returns[country] = df_returns[country].iloc[1:] # ignore first row\n",
    "ew_portfolio = df_returns[country].mean(axis=1).values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VALUE WEIGHTED\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# names two groups\n",
    "stocks_60_names =  ['MITSUB', 'MITSY', 'SZKMY', 'TM', 'SNE']\n",
    "stocks_40_names = [ stock for stock in df_returns[country].columns if stock not in stocks_60_names]  \n",
    "\n",
    "# stocks value two groups\n",
    "stocks_60 = df_returns[country][stocks_60_names]\n",
    "stocks_40 = df_returns[country][stocks_40_names]\n",
    "\n",
    "# mean each group\n",
    "stocks_60_mean = stocks_60.mean(axis=1).values\n",
    "stocks_40_mean = stocks_40.mean(axis=1).values\n",
    "\n",
    "# weight 60 40:\n",
    "vw_portfolio = stocks_60_mean * 0.6 + stocks_40_mean * 0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>EW</th>\n",
       "      <th>VW</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.606306</td>\n",
       "      <td>-0.798095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>0.225091</td>\n",
       "      <td>0.427348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>0.195871</td>\n",
       "      <td>0.527549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>0.130477</td>\n",
       "      <td>-0.130530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-09</th>\n",
       "      <td>0.287765</td>\n",
       "      <td>0.429509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  EW        VW\n",
       "Date                          \n",
       "2020-01-03 -0.606306 -0.798095\n",
       "2020-01-06  0.225091  0.427348\n",
       "2020-01-07  0.195871  0.527549\n",
       "2020-01-08  0.130477 -0.130530\n",
       "2020-01-09  0.287765  0.429509"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build dataframe\n",
    "df_portfolio = pd.DataFrame(index=df_returns[country].index)\n",
    "df_portfolio['EW'] = ew_portfolio\n",
    "df_portfolio['VW'] = vw_portfolio\n",
    "df_portfolio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": [
    "df_portfolio.rolling(7).mean().plot()\n",
    "titlee = country + ' returns portfolios'\n",
    "plt.title(titlee)\n",
    "plt.savefig(f\"plots_stocks/{country}_portfolio.PNG\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_portfolio.to_csv(f'datasets/{country}_portfolio.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EW    2.167253\n",
       "VW    1.740446\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_portfolio.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SWITZERLAND  <a class=\"anchor\" id=\"swiis\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "country = 'Switzerland'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>JNJ</th>\n",
       "      <th>MC</th>\n",
       "      <th>HD</th>\n",
       "      <th>OR</th>\n",
       "      <th>NOVN</th>\n",
       "      <th>PEP</th>\n",
       "      <th>ABT</th>\n",
       "      <th>LLY</th>\n",
       "      <th>PM</th>\n",
       "      <th>SAP</th>\n",
       "      <th>...</th>\n",
       "      <th>SGSN.SW</th>\n",
       "      <th>LOGN</th>\n",
       "      <th>SOON.SW</th>\n",
       "      <th>SLHNE.SW</th>\n",
       "      <th>UHR.SW</th>\n",
       "      <th>BARN.SW</th>\n",
       "      <th>ADEN.SW</th>\n",
       "      <th>TECN.SW</th>\n",
       "      <th>SRCG.SW</th>\n",
       "      <th>ZURN.SW</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-1.157777</td>\n",
       "      <td>-2.560147</td>\n",
       "      <td>-0.332342</td>\n",
       "      <td>-0.823040</td>\n",
       "      <td>-73.054661</td>\n",
       "      <td>-0.139900</td>\n",
       "      <td>-1.219084</td>\n",
       "      <td>-0.332792</td>\n",
       "      <td>-0.187830</td>\n",
       "      <td>-2.068557</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>-0.124744</td>\n",
       "      <td>-0.094967</td>\n",
       "      <td>0.470476</td>\n",
       "      <td>-0.726150</td>\n",
       "      <td>-2.147972</td>\n",
       "      <td>0.383388</td>\n",
       "      <td>0.523917</td>\n",
       "      <td>0.371844</td>\n",
       "      <td>1.164311</td>\n",
       "      <td>0.164207</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.639099</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.135137</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.145600</td>\n",
       "      <td>1.417761</td>\n",
       "      <td>0.263074</td>\n",
       "      <td>-1.382819</td>\n",
       "      <td>2.804960</td>\n",
       "      <td>-0.571574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>0.610673</td>\n",
       "      <td>1.267427</td>\n",
       "      <td>-0.654660</td>\n",
       "      <td>3.552779</td>\n",
       "      <td>-8.536585</td>\n",
       "      <td>-1.571788</td>\n",
       "      <td>-0.555941</td>\n",
       "      <td>0.189023</td>\n",
       "      <td>0.441755</td>\n",
       "      <td>-0.432191</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.151343</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.404856</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.373828</td>\n",
       "      <td>0.745580</td>\n",
       "      <td>0.229573</td>\n",
       "      <td>1.107011</td>\n",
       "      <td>0.444165</td>\n",
       "      <td>0.399902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>-0.013784</td>\n",
       "      <td>0.719646</td>\n",
       "      <td>1.496422</td>\n",
       "      <td>-6.559043</td>\n",
       "      <td>1.333332</td>\n",
       "      <td>0.514883</td>\n",
       "      <td>0.407657</td>\n",
       "      <td>0.905613</td>\n",
       "      <td>1.898139</td>\n",
       "      <td>1.092652</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.189468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.224014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.670389</td>\n",
       "      <td>-1.387608</td>\n",
       "      <td>0.654455</td>\n",
       "      <td>0.875910</td>\n",
       "      <td>-0.694886</td>\n",
       "      <td>-0.149360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 JNJ        MC        HD        OR       NOVN       PEP  \\\n",
       "Date                                                                      \n",
       "2020-01-02       NaN       NaN       NaN       NaN        NaN       NaN   \n",
       "2020-01-03 -1.157777 -2.560147 -0.332342 -0.823040 -73.054661 -0.139900   \n",
       "2020-01-06 -0.124744 -0.094967  0.470476 -0.726150  -2.147972  0.383388   \n",
       "2020-01-07  0.610673  1.267427 -0.654660  3.552779  -8.536585 -1.571788   \n",
       "2020-01-08 -0.013784  0.719646  1.496422 -6.559043   1.333332  0.514883   \n",
       "\n",
       "                 ABT       LLY        PM       SAP  ...   SGSN.SW  LOGN  \\\n",
       "Date                                                ...                   \n",
       "2020-01-02       NaN       NaN       NaN       NaN  ...       NaN   NaN   \n",
       "2020-01-03 -1.219084 -0.332792 -0.187830 -2.068557  ...       NaN   0.0   \n",
       "2020-01-06  0.523917  0.371844  1.164311  0.164207  ... -0.639099   0.0   \n",
       "2020-01-07 -0.555941  0.189023  0.441755 -0.432191  ... -0.151343   0.0   \n",
       "2020-01-08  0.407657  0.905613  1.898139  1.092652  ... -0.189468   0.0   \n",
       "\n",
       "             SOON.SW  SLHNE.SW    UHR.SW   BARN.SW   ADEN.SW   TECN.SW  \\\n",
       "Date                                                                     \n",
       "2020-01-02       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2020-01-03       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2020-01-06  0.135137       NaN -1.145600  1.417761  0.263074 -1.382819   \n",
       "2020-01-07  0.404856       NaN  0.373828  0.745580  0.229573  1.107011   \n",
       "2020-01-08  0.224014       NaN  0.670389 -1.387608  0.654455  0.875910   \n",
       "\n",
       "             SRCG.SW   ZURN.SW  \n",
       "Date                            \n",
       "2020-01-02       NaN       NaN  \n",
       "2020-01-03       NaN       NaN  \n",
       "2020-01-06  2.804960 -0.571574  \n",
       "2020-01-07  0.444165  0.399902  \n",
       "2020-01-08 -0.694886 -0.149360  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_returns[country].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EQUALLY WEIGHTED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_returns[country] = df_returns[country].iloc[1:] # ignore first row\n",
    "ew_portfolio = df_returns[country].mean(axis=1).values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VALUE WEIGHTED\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# names two groups\n",
    "stocks_60_names =  ['LINDT', 'SGSN.SW', 'BARN.SW', 'PGHN.SW', 'SCMN.SW']\n",
    "stocks_40_names = [ stock for stock in df_returns[country].columns if stock not in stocks_60_names]  \n",
    "\n",
    "# stocks value two groups\n",
    "stocks_60 = df_returns[country][stocks_60_names]\n",
    "stocks_40 = df_returns[country][stocks_40_names]\n",
    "\n",
    "# mean each group\n",
    "stocks_60_mean = stocks_60.mean(axis=1).values\n",
    "stocks_40_mean = stocks_40.mean(axis=1).values\n",
    "\n",
    "# weight 60 40:\n",
    "vw_portfolio = stocks_60_mean * 0.6 + stocks_40_mean * 0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>EW</th>\n",
       "      <th>VW</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-4.197861</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>0.021097</td>\n",
       "      <td>0.065817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>0.666565</td>\n",
       "      <td>0.486600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>-0.060602</td>\n",
       "      <td>-0.005064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-09</th>\n",
       "      <td>-0.129355</td>\n",
       "      <td>-0.085470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  EW        VW\n",
       "Date                          \n",
       "2020-01-03 -4.197861       NaN\n",
       "2020-01-06  0.021097  0.065817\n",
       "2020-01-07  0.666565  0.486600\n",
       "2020-01-08 -0.060602 -0.005064\n",
       "2020-01-09 -0.129355 -0.085470"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build dataframe\n",
    "df_portfolio = pd.DataFrame(index=df_returns[country].index)\n",
    "df_portfolio['EW'] = ew_portfolio\n",
    "df_portfolio['VW'] = vw_portfolio\n",
    "df_portfolio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": [
    "df_portfolio.rolling(7).mean().plot()\n",
    "titlee = country + ' returns portfolios'\n",
    "plt.title(titlee)\n",
    "plt.savefig(f\"plots_stocks/{country}_portfolio.PNG\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_portfolio.to_csv(f'datasets/{country}_portfolio.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EW    2.482388\n",
       "VW    1.694269\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_portfolio.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fbd",
   "language": "python",
   "name": "fbd"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}