Portfolio optimization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See README.md for installs, environment setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python               3.7.10\n",
      "numpy                1.19.2\n",
      "pandas               1.1.3\n",
      "pandas-datareader    0.9.0\n",
      "seaborn              0.11.0\n",
      "matplotlib           3.3.4\n",
      "cvxpy                1.1.7\n",
      "quandl               3.7.0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "from multiprocessing import Pool\n",
    "from datetime import datetime\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_datareader as pdr\n",
    "# need in colab\n",
    "# !pip install quandl\n",
    "import quandl\n",
    "import xlrd\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# https://www.cvxpy.org/install/index.html\n",
    "import cvxpy as cp\n",
    "\n",
    "# create an env var QUANDL_TOKEN=xxxxxx\n",
    "quandl_token = os.getenv('QUANDL_TOKEN')\n",
    "\n",
    "# set seed for reproducibility\n",
    "np.random.seed(2347)\n",
    "\n",
    "print(\"%-20s %s\" % ('python', \".\".join(map(str, sys.version_info[:3]))))\n",
    "print(\"%-20s %s\" % (\"numpy\", np.__version__))\n",
    "print(\"%-20s %s\" % (\"pandas\", pd.__version__))\n",
    "print(\"%-20s %s\" % (\"pandas-datareader\", pdr.__version__))\n",
    "# print(\"%-20s %s\" % (\"xlrd\", xlrd.__version__))\n",
    "print(\"%-20s %s\" % (\"seaborn\", sns.__version__))\n",
    "print(\"%-20s %s\" % (\"matplotlib\", matplotlib.__version__))\n",
    "print(\"%-20s %s\" % (\"cvxpy\", cp.__version__))\n",
    "print(\"%-20s %s\" % (\"quandl\", quandl.version.VERSION))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>S&amp;P 500 (includes dividends)</th>\n",
       "      <th>3-month T.Bill</th>\n",
       "      <th>US T. Bond</th>\n",
       "      <th>Baa Corporate Bond</th>\n",
       "      <th>Real Estate</th>\n",
       "      <th>Gold*</th>\n",
       "      <th>S&amp;P 500 (includes dividends)3</th>\n",
       "      <th>3-month T.Bill4</th>\n",
       "      <th>US T. Bond5</th>\n",
       "      <th>Baa Corporate Bond2</th>\n",
       "      <th>...</th>\n",
       "      <th>Stocks - Bonds</th>\n",
       "      <th>Stocks - Baa Corp Bond</th>\n",
       "      <th>Historical risk premium</th>\n",
       "      <th>Inflation Rate</th>\n",
       "      <th>S&amp;P 500 (includes dividends)2</th>\n",
       "      <th>3-month T. Bill (Real)</th>\n",
       "      <th>!0-year T.Bonds</th>\n",
       "      <th>Baa Corp Bonds</th>\n",
       "      <th>Real Estate3</th>\n",
       "      <th>Gold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</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>1928</th>\n",
       "      <td>0.438112</td>\n",
       "      <td>0.030800</td>\n",
       "      <td>0.008355</td>\n",
       "      <td>0.032196</td>\n",
       "      <td>0.014911</td>\n",
       "      <td>0.000969</td>\n",
       "      <td>143.811155</td>\n",
       "      <td>103.080000</td>\n",
       "      <td>100.835</td>\n",
       "      <td>103.22</td>\n",
       "      <td>...</td>\n",
       "      <td>0.429757</td>\n",
       "      <td>0.405916</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0115607</td>\n",
       "      <td>0.454932</td>\n",
       "      <td>0.0428561</td>\n",
       "      <td>0.0201483</td>\n",
       "      <td>0.044268</td>\n",
       "      <td>0.0267808</td>\n",
       "      <td>0.0126762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>-0.082979</td>\n",
       "      <td>0.031600</td>\n",
       "      <td>0.042038</td>\n",
       "      <td>0.030179</td>\n",
       "      <td>-0.020568</td>\n",
       "      <td>-0.001452</td>\n",
       "      <td>131.877782</td>\n",
       "      <td>106.337328</td>\n",
       "      <td>105.074</td>\n",
       "      <td>106.335</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.125018</td>\n",
       "      <td>-0.113158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005848</td>\n",
       "      <td>-0.088311</td>\n",
       "      <td>0.0256023</td>\n",
       "      <td>0.0359796</td>\n",
       "      <td>0.0241891</td>\n",
       "      <td>-0.0262625</td>\n",
       "      <td>-0.00725764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>-0.251236</td>\n",
       "      <td>0.045500</td>\n",
       "      <td>0.045409</td>\n",
       "      <td>0.005398</td>\n",
       "      <td>-0.043000</td>\n",
       "      <td>0.000969</td>\n",
       "      <td>98.745288</td>\n",
       "      <td>111.175676</td>\n",
       "      <td>109.846</td>\n",
       "      <td>106.909</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.296646</td>\n",
       "      <td>-0.256634</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0639535</td>\n",
       "      <td>-0.200079</td>\n",
       "      <td>0.116932</td>\n",
       "      <td>0.116835</td>\n",
       "      <td>0.0740896</td>\n",
       "      <td>0.0223851</td>\n",
       "      <td>0.0693587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>-0.438375</td>\n",
       "      <td>0.023100</td>\n",
       "      <td>-0.025589</td>\n",
       "      <td>-0.156808</td>\n",
       "      <td>-0.081505</td>\n",
       "      <td>-0.173850</td>\n",
       "      <td>55.457774</td>\n",
       "      <td>113.743835</td>\n",
       "      <td>107.035</td>\n",
       "      <td>90.1445</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.412787</td>\n",
       "      <td>-0.281568</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0931677</td>\n",
       "      <td>-0.380674</td>\n",
       "      <td>0.128213</td>\n",
       "      <td>0.0745222</td>\n",
       "      <td>-0.0701784</td>\n",
       "      <td>0.0128613</td>\n",
       "      <td>-0.0889714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>-0.086424</td>\n",
       "      <td>0.010700</td>\n",
       "      <td>0.087903</td>\n",
       "      <td>0.235896</td>\n",
       "      <td>-0.104664</td>\n",
       "      <td>0.212778</td>\n",
       "      <td>50.664911</td>\n",
       "      <td>114.960894</td>\n",
       "      <td>116.444</td>\n",
       "      <td>111.409</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.174327</td>\n",
       "      <td>-0.322320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.10274</td>\n",
       "      <td>0.0181843</td>\n",
       "      <td>0.126429</td>\n",
       "      <td>0.212472</td>\n",
       "      <td>0.377411</td>\n",
       "      <td>-0.00214508</td>\n",
       "      <td>0.351646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>0.284689</td>\n",
       "      <td>0.000450</td>\n",
       "      <td>-0.044160</td>\n",
       "      <td>0.009334</td>\n",
       "      <td>0.189143</td>\n",
       "      <td>-0.037544</td>\n",
       "      <td>761710.833617</td>\n",
       "      <td>2098.027709</td>\n",
       "      <td>8526.95</td>\n",
       "      <td>54237.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.328849</td>\n",
       "      <td>0.275354</td>\n",
       "      <td>0.051322</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.199522</td>\n",
       "      <td>-0.0658728</td>\n",
       "      <td>-0.107526</td>\n",
       "      <td>-0.0575775</td>\n",
       "      <td>0.110311</td>\n",
       "      <td>-0.101349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>-0.180090</td>\n",
       "      <td>0.020248</td>\n",
       "      <td>-0.178282</td>\n",
       "      <td>-0.144883</td>\n",
       "      <td>0.073033</td>\n",
       "      <td>0.005494</td>\n",
       "      <td>624534.546333</td>\n",
       "      <td>2140.508136</td>\n",
       "      <td>7006.75</td>\n",
       "      <td>46379.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001808</td>\n",
       "      <td>-0.035207</td>\n",
       "      <td>0.050613</td>\n",
       "      <td>0.0642</td>\n",
       "      <td>-0.229552</td>\n",
       "      <td>-0.0413007</td>\n",
       "      <td>-0.227854</td>\n",
       "      <td>-0.19647</td>\n",
       "      <td>0.00830042</td>\n",
       "      <td>-0.0551643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</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>Arithmetic Average Historical Return</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>Risk Premium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arithmetic Average Annual Real Return</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>1928-2022</th>\n",
       "      <td>0.115065</td>\n",
       "      <td>0.033202</td>\n",
       "      <td>0.048690</td>\n",
       "      <td>0.069579</td>\n",
       "      <td>0.044157</td>\n",
       "      <td>0.064836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stocks - T.Bills</td>\n",
       "      <td>Stocks - T.Bonds</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S&amp;P 500 (includes dividends)2</td>\n",
       "      <td>3-month T. Bill (Real)</td>\n",
       "      <td>!0-year T.Bonds</td>\n",
       "      <td>Baa Corp Bonds</td>\n",
       "      <td>Real Estate3</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      S&P 500 (includes dividends)  \\\n",
       "Year                                                                 \n",
       "1928                                                      0.438112   \n",
       "1929                                                     -0.082979   \n",
       "1930                                                     -0.251236   \n",
       "1931                                                     -0.438375   \n",
       "1932                                                     -0.086424   \n",
       "...                                                            ...   \n",
       "2021                                                      0.284689   \n",
       "2022                                                     -0.180090   \n",
       "NaN                                                            NaN   \n",
       "Arithmetic Average Historical Return                           NaN   \n",
       "1928-2022                                                 0.115065   \n",
       "\n",
       "                                      3-month T.Bill  US T. Bond  \\\n",
       "Year                                                               \n",
       "1928                                        0.030800    0.008355   \n",
       "1929                                        0.031600    0.042038   \n",
       "1930                                        0.045500    0.045409   \n",
       "1931                                        0.023100   -0.025589   \n",
       "1932                                        0.010700    0.087903   \n",
       "...                                              ...         ...   \n",
       "2021                                        0.000450   -0.044160   \n",
       "2022                                        0.020248   -0.178282   \n",
       "NaN                                              NaN         NaN   \n",
       "Arithmetic Average Historical Return             NaN         NaN   \n",
       "1928-2022                                   0.033202    0.048690   \n",
       "\n",
       "                                       Baa Corporate Bond  Real Estate  \\\n",
       "Year                                                                     \n",
       "1928                                             0.032196     0.014911   \n",
       "1929                                             0.030179    -0.020568   \n",
       "1930                                             0.005398    -0.043000   \n",
       "1931                                            -0.156808    -0.081505   \n",
       "1932                                             0.235896    -0.104664   \n",
       "...                                                   ...          ...   \n",
       "2021                                             0.009334     0.189143   \n",
       "2022                                            -0.144883     0.073033   \n",
       "NaN                                                   NaN          NaN   \n",
       "Arithmetic Average Historical Return                  NaN          NaN   \n",
       "1928-2022                                        0.069579     0.044157   \n",
       "\n",
       "                                         Gold*  S&P 500 (includes dividends)3  \\\n",
       "Year                                                                            \n",
       "1928                                  0.000969                     143.811155   \n",
       "1929                                 -0.001452                     131.877782   \n",
       "1930                                  0.000969                      98.745288   \n",
       "1931                                 -0.173850                      55.457774   \n",
       "1932                                  0.212778                      50.664911   \n",
       "...                                        ...                            ...   \n",
       "2021                                 -0.037544                  761710.833617   \n",
       "2022                                  0.005494                  624534.546333   \n",
       "NaN                                        NaN                            NaN   \n",
       "Arithmetic Average Historical Return       NaN                            NaN   \n",
       "1928-2022                             0.064836                            NaN   \n",
       "\n",
       "                                      3-month T.Bill4       US T. Bond5  \\\n",
       "Year                                                                      \n",
       "1928                                       103.080000           100.835   \n",
       "1929                                       106.337328           105.074   \n",
       "1930                                       111.175676           109.846   \n",
       "1931                                       113.743835           107.035   \n",
       "1932                                       114.960894           116.444   \n",
       "...                                               ...               ...   \n",
       "2021                                      2098.027709           8526.95   \n",
       "2022                                      2140.508136           7006.75   \n",
       "NaN                                               NaN               NaN   \n",
       "Arithmetic Average Historical Return              NaN      Risk Premium   \n",
       "1928-2022                                         NaN  Stocks - T.Bills   \n",
       "\n",
       "                                      Baa Corporate Bond2  ... Stocks - Bonds  \\\n",
       "Year                                                       ...                  \n",
       "1928                                               103.22  ...       0.429757   \n",
       "1929                                              106.335  ...      -0.125018   \n",
       "1930                                              106.909  ...      -0.296646   \n",
       "1931                                              90.1445  ...      -0.412787   \n",
       "1932                                              111.409  ...      -0.174327   \n",
       "...                                                   ...  ...            ...   \n",
       "2021                                              54237.6  ...       0.328849   \n",
       "2022                                              46379.5  ...      -0.001808   \n",
       "NaN                                                   NaN  ...            NaN   \n",
       "Arithmetic Average Historical Return                  NaN  ...            NaN   \n",
       "1928-2022                                Stocks - T.Bonds  ...            NaN   \n",
       "\n",
       "                                     Stocks - Baa Corp Bond  \\\n",
       "Year                                                          \n",
       "1928                                               0.405916   \n",
       "1929                                              -0.113158   \n",
       "1930                                              -0.256634   \n",
       "1931                                              -0.281568   \n",
       "1932                                              -0.322320   \n",
       "...                                                     ...   \n",
       "2021                                               0.275354   \n",
       "2022                                              -0.035207   \n",
       "NaN                                                     NaN   \n",
       "Arithmetic Average Historical Return                    NaN   \n",
       "1928-2022                                               NaN   \n",
       "\n",
       "                                      Historical risk premium  \\\n",
       "Year                                                            \n",
       "1928                                                      NaN   \n",
       "1929                                                      NaN   \n",
       "1930                                                      NaN   \n",
       "1931                                                      NaN   \n",
       "1932                                                      NaN   \n",
       "...                                                       ...   \n",
       "2021                                                 0.051322   \n",
       "2022                                                 0.050613   \n",
       "NaN                                                       NaN   \n",
       "Arithmetic Average Historical Return                      NaN   \n",
       "1928-2022                                                 NaN   \n",
       "\n",
       "                                                             Inflation Rate  \\\n",
       "Year                                                                          \n",
       "1928                                                             -0.0115607   \n",
       "1929                                                               0.005848   \n",
       "1930                                                             -0.0639535   \n",
       "1931                                                             -0.0931677   \n",
       "1932                                                               -0.10274   \n",
       "...                                                                     ...   \n",
       "2021                                                                  0.071   \n",
       "2022                                                                 0.0642   \n",
       "NaN                                                                     NaN   \n",
       "Arithmetic Average Historical Return  Arithmetic Average Annual Real Return   \n",
       "1928-2022                                                               NaN   \n",
       "\n",
       "                                      S&P 500 (includes dividends)2  \\\n",
       "Year                                                                  \n",
       "1928                                                       0.454932   \n",
       "1929                                                      -0.088311   \n",
       "1930                                                      -0.200079   \n",
       "1931                                                      -0.380674   \n",
       "1932                                                      0.0181843   \n",
       "...                                                             ...   \n",
       "2021                                                       0.199522   \n",
       "2022                                                      -0.229552   \n",
       "NaN                                                             NaN   \n",
       "Arithmetic Average Historical Return                            NaN   \n",
       "1928-2022                             S&P 500 (includes dividends)2   \n",
       "\n",
       "                                      3-month T. Bill (Real)  !0-year T.Bonds  \\\n",
       "Year                                                                            \n",
       "1928                                               0.0428561        0.0201483   \n",
       "1929                                               0.0256023        0.0359796   \n",
       "1930                                                0.116932         0.116835   \n",
       "1931                                                0.128213        0.0745222   \n",
       "1932                                                0.126429         0.212472   \n",
       "...                                                      ...              ...   \n",
       "2021                                              -0.0658728        -0.107526   \n",
       "2022                                              -0.0413007        -0.227854   \n",
       "NaN                                                      NaN              NaN   \n",
       "Arithmetic Average Historical Return                     NaN              NaN   \n",
       "1928-2022                             3-month T. Bill (Real)  !0-year T.Bonds   \n",
       "\n",
       "                                      Baa Corp Bonds  Real Estate3        Gold  \n",
       "Year                                                                            \n",
       "1928                                        0.044268     0.0267808   0.0126762  \n",
       "1929                                       0.0241891    -0.0262625 -0.00725764  \n",
       "1930                                       0.0740896     0.0223851   0.0693587  \n",
       "1931                                      -0.0701784     0.0128613  -0.0889714  \n",
       "1932                                        0.377411   -0.00214508    0.351646  \n",
       "...                                              ...           ...         ...  \n",
       "2021                                      -0.0575775      0.110311   -0.101349  \n",
       "2022                                        -0.19647    0.00830042  -0.0551643  \n",
       "NaN                                              NaN           NaN         NaN  \n",
       "Arithmetic Average Historical Return             NaN           NaN         NaN  \n",
       "1928-2022                             Baa Corp Bonds  Real Estate3        Gold  \n",
       "\n",
       "[98 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load spreadsheet from Damodaran website into pandas dataframe\n",
    "\n",
    "# if below gives cert error\n",
    "import ssl\n",
    "ssl._create_default_https_context = ssl._create_unverified_context\n",
    "\n",
    "data_xls = 'http://www.stern.nyu.edu/~adamodar/pc/datasets/histretSP.xls'\n",
    "data_sheet = \"Returns by year\"\n",
    "# these will change as rows get added on Damodaran website\n",
    "skiprows = range(17)\n",
    "skipfooter = 10\n",
    "download_df = pd.read_excel('http://www.stern.nyu.edu/~adamodar/pc/datasets/histretSP.xls', \n",
    "                         sheet_name=data_sheet, \n",
    "                         skiprows=skiprows,\n",
    "                         skipfooter=skipfooter)\n",
    "download_df = download_df.set_index('Year')\n",
    "download_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>S&amp;P 500 (includes dividends)</th>\n",
       "      <th>3-month T.Bill</th>\n",
       "      <th>US T. Bond</th>\n",
       "      <th>Baa Corporate Bond</th>\n",
       "      <th>Real Estate</th>\n",
       "      <th>Gold*</th>\n",
       "      <th>S&amp;P 500 (includes dividends)3</th>\n",
       "      <th>3-month T.Bill4</th>\n",
       "      <th>US T. Bond5</th>\n",
       "      <th>Baa Corporate Bond2</th>\n",
       "      <th>...</th>\n",
       "      <th>Stocks - Bonds</th>\n",
       "      <th>Stocks - Baa Corp Bond</th>\n",
       "      <th>Historical risk premium</th>\n",
       "      <th>Inflation Rate</th>\n",
       "      <th>S&amp;P 500 (includes dividends)2</th>\n",
       "      <th>3-month T. Bill (Real)</th>\n",
       "      <th>!0-year T.Bonds</th>\n",
       "      <th>Baa Corp Bonds</th>\n",
       "      <th>Real Estate3</th>\n",
       "      <th>Gold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</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>1928</th>\n",
       "      <td>0.438112</td>\n",
       "      <td>0.030800</td>\n",
       "      <td>0.008355</td>\n",
       "      <td>0.032196</td>\n",
       "      <td>0.014911</td>\n",
       "      <td>0.000969</td>\n",
       "      <td>143.811155</td>\n",
       "      <td>103.080000</td>\n",
       "      <td>100.835</td>\n",
       "      <td>103.22</td>\n",
       "      <td>...</td>\n",
       "      <td>0.429757</td>\n",
       "      <td>0.405916</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0115607</td>\n",
       "      <td>0.454932</td>\n",
       "      <td>0.0428561</td>\n",
       "      <td>0.0201483</td>\n",
       "      <td>0.044268</td>\n",
       "      <td>0.0267808</td>\n",
       "      <td>0.0126762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>-0.082979</td>\n",
       "      <td>0.031600</td>\n",
       "      <td>0.042038</td>\n",
       "      <td>0.030179</td>\n",
       "      <td>-0.020568</td>\n",
       "      <td>-0.001452</td>\n",
       "      <td>131.877782</td>\n",
       "      <td>106.337328</td>\n",
       "      <td>105.074</td>\n",
       "      <td>106.335</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.125018</td>\n",
       "      <td>-0.113158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005848</td>\n",
       "      <td>-0.088311</td>\n",
       "      <td>0.0256023</td>\n",
       "      <td>0.0359796</td>\n",
       "      <td>0.0241891</td>\n",
       "      <td>-0.0262625</td>\n",
       "      <td>-0.00725764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>-0.251236</td>\n",
       "      <td>0.045500</td>\n",
       "      <td>0.045409</td>\n",
       "      <td>0.005398</td>\n",
       "      <td>-0.043000</td>\n",
       "      <td>0.000969</td>\n",
       "      <td>98.745288</td>\n",
       "      <td>111.175676</td>\n",
       "      <td>109.846</td>\n",
       "      <td>106.909</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.296646</td>\n",
       "      <td>-0.256634</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0639535</td>\n",
       "      <td>-0.200079</td>\n",
       "      <td>0.116932</td>\n",
       "      <td>0.116835</td>\n",
       "      <td>0.0740896</td>\n",
       "      <td>0.0223851</td>\n",
       "      <td>0.0693587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>-0.438375</td>\n",
       "      <td>0.023100</td>\n",
       "      <td>-0.025589</td>\n",
       "      <td>-0.156808</td>\n",
       "      <td>-0.081505</td>\n",
       "      <td>-0.173850</td>\n",
       "      <td>55.457774</td>\n",
       "      <td>113.743835</td>\n",
       "      <td>107.035</td>\n",
       "      <td>90.1445</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.412787</td>\n",
       "      <td>-0.281568</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0931677</td>\n",
       "      <td>-0.380674</td>\n",
       "      <td>0.128213</td>\n",
       "      <td>0.0745222</td>\n",
       "      <td>-0.0701784</td>\n",
       "      <td>0.0128613</td>\n",
       "      <td>-0.0889714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>-0.086424</td>\n",
       "      <td>0.010700</td>\n",
       "      <td>0.087903</td>\n",
       "      <td>0.235896</td>\n",
       "      <td>-0.104664</td>\n",
       "      <td>0.212778</td>\n",
       "      <td>50.664911</td>\n",
       "      <td>114.960894</td>\n",
       "      <td>116.444</td>\n",
       "      <td>111.409</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.174327</td>\n",
       "      <td>-0.322320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.10274</td>\n",
       "      <td>0.0181843</td>\n",
       "      <td>0.126429</td>\n",
       "      <td>0.212472</td>\n",
       "      <td>0.377411</td>\n",
       "      <td>-0.00214508</td>\n",
       "      <td>0.351646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>-0.042269</td>\n",
       "      <td>0.019363</td>\n",
       "      <td>-0.000167</td>\n",
       "      <td>-0.027626</td>\n",
       "      <td>0.045327</td>\n",
       "      <td>-0.009295</td>\n",
       "      <td>382870.937409</td>\n",
       "      <td>2047.442590</td>\n",
       "      <td>7308.65</td>\n",
       "      <td>42199.9</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.042102</td>\n",
       "      <td>-0.014642</td>\n",
       "      <td>0.046609</td>\n",
       "      <td>0.0191016</td>\n",
       "      <td>-0.06022</td>\n",
       "      <td>0.000256552</td>\n",
       "      <td>-0.0189074</td>\n",
       "      <td>-0.045852</td>\n",
       "      <td>0.0257338</td>\n",
       "      <td>-0.0278645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.312117</td>\n",
       "      <td>0.020625</td>\n",
       "      <td>0.096356</td>\n",
       "      <td>0.153295</td>\n",
       "      <td>0.036916</td>\n",
       "      <td>0.190774</td>\n",
       "      <td>502371.389193</td>\n",
       "      <td>2089.670684</td>\n",
       "      <td>8012.89</td>\n",
       "      <td>48668.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.215760</td>\n",
       "      <td>0.158822</td>\n",
       "      <td>0.048254</td>\n",
       "      <td>0.0228513</td>\n",
       "      <td>0.282803</td>\n",
       "      <td>-0.00217676</td>\n",
       "      <td>0.0718628</td>\n",
       "      <td>0.127529</td>\n",
       "      <td>0.0137504</td>\n",
       "      <td>0.164171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.180232</td>\n",
       "      <td>0.003547</td>\n",
       "      <td>0.113319</td>\n",
       "      <td>0.104115</td>\n",
       "      <td>0.103461</td>\n",
       "      <td>0.241694</td>\n",
       "      <td>592914.798591</td>\n",
       "      <td>2097.083604</td>\n",
       "      <td>8920.9</td>\n",
       "      <td>53736</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066913</td>\n",
       "      <td>0.076117</td>\n",
       "      <td>0.048443</td>\n",
       "      <td>0.0136201</td>\n",
       "      <td>0.164373</td>\n",
       "      <td>-0.00993734</td>\n",
       "      <td>0.0983592</td>\n",
       "      <td>0.0892793</td>\n",
       "      <td>0.088634</td>\n",
       "      <td>0.225009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>0.284689</td>\n",
       "      <td>0.000450</td>\n",
       "      <td>-0.044160</td>\n",
       "      <td>0.009334</td>\n",
       "      <td>0.189143</td>\n",
       "      <td>-0.037544</td>\n",
       "      <td>761710.833617</td>\n",
       "      <td>2098.027709</td>\n",
       "      <td>8526.95</td>\n",
       "      <td>54237.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.328849</td>\n",
       "      <td>0.275354</td>\n",
       "      <td>0.051322</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.199522</td>\n",
       "      <td>-0.0658728</td>\n",
       "      <td>-0.107526</td>\n",
       "      <td>-0.0575775</td>\n",
       "      <td>0.110311</td>\n",
       "      <td>-0.101349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>-0.180090</td>\n",
       "      <td>0.020248</td>\n",
       "      <td>-0.178282</td>\n",
       "      <td>-0.144883</td>\n",
       "      <td>0.073033</td>\n",
       "      <td>0.005494</td>\n",
       "      <td>624534.546333</td>\n",
       "      <td>2140.508136</td>\n",
       "      <td>7006.75</td>\n",
       "      <td>46379.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001808</td>\n",
       "      <td>-0.035207</td>\n",
       "      <td>0.050613</td>\n",
       "      <td>0.0642</td>\n",
       "      <td>-0.229552</td>\n",
       "      <td>-0.0413007</td>\n",
       "      <td>-0.227854</td>\n",
       "      <td>-0.19647</td>\n",
       "      <td>0.00830042</td>\n",
       "      <td>-0.0551643</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      S&P 500 (includes dividends)  3-month T.Bill  US T. Bond  \\\n",
       "Year                                                             \n",
       "1928                      0.438112        0.030800    0.008355   \n",
       "1929                     -0.082979        0.031600    0.042038   \n",
       "1930                     -0.251236        0.045500    0.045409   \n",
       "1931                     -0.438375        0.023100   -0.025589   \n",
       "1932                     -0.086424        0.010700    0.087903   \n",
       "...                            ...             ...         ...   \n",
       "2018                     -0.042269        0.019363   -0.000167   \n",
       "2019                      0.312117        0.020625    0.096356   \n",
       "2020                      0.180232        0.003547    0.113319   \n",
       "2021                      0.284689        0.000450   -0.044160   \n",
       "2022                     -0.180090        0.020248   -0.178282   \n",
       "\n",
       "       Baa Corporate Bond  Real Estate     Gold*  \\\n",
       "Year                                               \n",
       "1928             0.032196     0.014911  0.000969   \n",
       "1929             0.030179    -0.020568 -0.001452   \n",
       "1930             0.005398    -0.043000  0.000969   \n",
       "1931            -0.156808    -0.081505 -0.173850   \n",
       "1932             0.235896    -0.104664  0.212778   \n",
       "...                   ...          ...       ...   \n",
       "2018            -0.027626     0.045327 -0.009295   \n",
       "2019             0.153295     0.036916  0.190774   \n",
       "2020             0.104115     0.103461  0.241694   \n",
       "2021             0.009334     0.189143 -0.037544   \n",
       "2022            -0.144883     0.073033  0.005494   \n",
       "\n",
       "      S&P 500 (includes dividends)3  3-month T.Bill4 US T. Bond5  \\\n",
       "Year                                                               \n",
       "1928                     143.811155       103.080000     100.835   \n",
       "1929                     131.877782       106.337328     105.074   \n",
       "1930                      98.745288       111.175676     109.846   \n",
       "1931                      55.457774       113.743835     107.035   \n",
       "1932                      50.664911       114.960894     116.444   \n",
       "...                             ...              ...         ...   \n",
       "2018                  382870.937409      2047.442590     7308.65   \n",
       "2019                  502371.389193      2089.670684     8012.89   \n",
       "2020                  592914.798591      2097.083604      8920.9   \n",
       "2021                  761710.833617      2098.027709     8526.95   \n",
       "2022                  624534.546333      2140.508136     7006.75   \n",
       "\n",
       "      Baa Corporate Bond2  ... Stocks - Bonds Stocks - Baa Corp Bond  \\\n",
       "Year                       ...                                         \n",
       "1928               103.22  ...       0.429757               0.405916   \n",
       "1929              106.335  ...      -0.125018              -0.113158   \n",
       "1930              106.909  ...      -0.296646              -0.256634   \n",
       "1931              90.1445  ...      -0.412787              -0.281568   \n",
       "1932              111.409  ...      -0.174327              -0.322320   \n",
       "...                   ...  ...            ...                    ...   \n",
       "2018              42199.9  ...      -0.042102              -0.014642   \n",
       "2019              48668.9  ...       0.215760               0.158822   \n",
       "2020                53736  ...       0.066913               0.076117   \n",
       "2021              54237.6  ...       0.328849               0.275354   \n",
       "2022              46379.5  ...      -0.001808              -0.035207   \n",
       "\n",
       "      Historical risk premium  Inflation Rate  S&P 500 (includes dividends)2  \\\n",
       "Year                                                                           \n",
       "1928                      NaN      -0.0115607                       0.454932   \n",
       "1929                      NaN        0.005848                      -0.088311   \n",
       "1930                      NaN      -0.0639535                      -0.200079   \n",
       "1931                      NaN      -0.0931677                      -0.380674   \n",
       "1932                      NaN        -0.10274                      0.0181843   \n",
       "...                       ...             ...                            ...   \n",
       "2018                 0.046609       0.0191016                       -0.06022   \n",
       "2019                 0.048254       0.0228513                       0.282803   \n",
       "2020                 0.048443       0.0136201                       0.164373   \n",
       "2021                 0.051322           0.071                       0.199522   \n",
       "2022                 0.050613          0.0642                      -0.229552   \n",
       "\n",
       "      3-month T. Bill (Real) !0-year T.Bonds Baa Corp Bonds Real Estate3  \\\n",
       "Year                                                                       \n",
       "1928               0.0428561       0.0201483       0.044268    0.0267808   \n",
       "1929               0.0256023       0.0359796      0.0241891   -0.0262625   \n",
       "1930                0.116932        0.116835      0.0740896    0.0223851   \n",
       "1931                0.128213       0.0745222     -0.0701784    0.0128613   \n",
       "1932                0.126429        0.212472       0.377411  -0.00214508   \n",
       "...                      ...             ...            ...          ...   \n",
       "2018             0.000256552      -0.0189074      -0.045852    0.0257338   \n",
       "2019             -0.00217676       0.0718628       0.127529    0.0137504   \n",
       "2020             -0.00993734       0.0983592      0.0892793     0.088634   \n",
       "2021              -0.0658728       -0.107526     -0.0575775     0.110311   \n",
       "2022              -0.0413007       -0.227854       -0.19647   0.00830042   \n",
       "\n",
       "            Gold  \n",
       "Year              \n",
       "1928   0.0126762  \n",
       "1929 -0.00725764  \n",
       "1930   0.0693587  \n",
       "1931  -0.0889714  \n",
       "1932    0.351646  \n",
       "...          ...  \n",
       "2018  -0.0278645  \n",
       "2019    0.164171  \n",
       "2020    0.225009  \n",
       "2021   -0.101349  \n",
       "2022  -0.0551643  \n",
       "\n",
       "[95 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "download_df = download_df[:-3]   # drop extra rows\n",
    "download_df.set_index(download_df.index.astype(int), inplace=True)\n",
    "download_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load gold data from Quandl (formerly FRED) API & save copy locally to CSV file\n",
    "\n",
    "# series = ['GOLDAMGBD228NLBM']\n",
    "\n",
    "# gold_temp = pdr.data.DataReader(series, \n",
    "#                                 'fred', \n",
    "#                                 start='1968-12-31')\n",
    "\n",
    "gold_temp = quandl.get('LBMA/GOLD', start_date='1968-12-31', authtoken=quandl_token)\n",
    "\n",
    "# convert daily to annual\n",
    "gold_temp = gold_temp.resample('A').last().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "gold_temp2 = gold_temp.copy()\n",
    "gold_temp2['year'] = gold_temp2['Date'].apply(lambda d: d.year)\n",
    "gold_temp2['year'] = gold_temp2['year'].astype(int)\n",
    "gold_temp2.set_index('year', inplace=True)\n",
    "gold_temp2.set_index(gold_temp2.index.astype(int), inplace=True)\n",
    "gold_temp2['return'] = gold_temp2['USD (PM)'].pct_change()\n",
    "\n",
    "# line up dates\n",
    "gold_download = pd.DataFrame(index=download_df.index)\n",
    "gold_download['gold'] = gold_temp2['return']\n",
    "\n",
    "gold_download.to_csv('gold_fred.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>-0.011454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.188312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.241694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          gold\n",
       "year          \n",
       "1928  0.000000\n",
       "1929  0.000000\n",
       "1930  0.000000\n",
       "1931  0.000000\n",
       "1932  0.000000\n",
       "...        ...\n",
       "2018 -0.011454\n",
       "2019  0.188312\n",
       "2020  0.241694\n",
       "2021       NaN\n",
       "2022       NaN\n",
       "\n",
       "[95 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# splice older gold data from other sources (Ibbotson yearbook)\n",
    "longrun_data = pd.DataFrame({'year': list(range(1928, datetime.now().year)), 'gold': [0,0,0,0,0,0.563618771,0.082920792,0,0,0,0,0,-0.014285714,0.028985507,0,0.028169014,-0.006849315,0.027586207,0.026845638,0.124183007,-0.023255814,-0.035714286,-0.00617284,-0.00621118,-0.0325,-0.082687339,-0.007042254,-0.002836879,0.001422475,0.001420455,0,0,0.035460993,-0.02739726,-0.004225352,-0.002828854,0.002836879,0.004243281,-0.002816901,0.002824859,0.225352113,-0.057471264,-0.051219512,0.146529563,0.431390135,0.667919799,0.725864012,-0.242041683,-0.03962955,0.204305898,0.291744258,1.205670351,0.296078431,-0.327618087,0.1175,-0.149888143,-0.189473684,0.061688312,0.195412844,0.244563827,-0.156937307,-0.022308911,-0.036907731,-0.085577421,-0.057057907,0.176426426,-0.021697511,0.009784736,-0.046511628,-0.222086721,0.005748128,0.005368895,-0.060637382,0.014120668,0.23960217,0.217359592,0.04397843,0.17768595,0.231968811,0.319224684,0.043178411,0.250359299,0.292413793,0.089292067,0.082625735,-0.273303167,0.00124533,-0.114238,0.091174,0.118540,-0.011454,0.188312, 0.241694, np.nan, np.nan]})\n",
    "longrun_data.set_index('year', inplace=True)\n",
    "longrun_data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>-0.009295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.184324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.246146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>-0.043309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>0.004375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          gold\n",
       "Year          \n",
       "1928  0.000000\n",
       "1929  0.000000\n",
       "1930  0.000000\n",
       "1931  0.000000\n",
       "1932  0.000000\n",
       "...        ...\n",
       "2018 -0.009295\n",
       "2019  0.184324\n",
       "2020  0.246146\n",
       "2021 -0.043309\n",
       "2022  0.004375\n",
       "\n",
       "[95 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold_download.loc[1928:1968, 'gold'] = longrun_data['gold']\n",
    "gold_download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>GDPCA</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.011000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>1929-01-01</td>\n",
       "      <td>1110.206</td>\n",
       "      <td>0.065200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>1930-01-01</td>\n",
       "      <td>1015.751</td>\n",
       "      <td>-0.085079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>1931-01-01</td>\n",
       "      <td>950.686</td>\n",
       "      <td>-0.064056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>1932-01-01</td>\n",
       "      <td>828.061</td>\n",
       "      <td>-0.128986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>18609.078</td>\n",
       "      <td>0.029454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>19036.052</td>\n",
       "      <td>0.022944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>18509.143</td>\n",
       "      <td>-0.027680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>19609.812</td>\n",
       "      <td>0.059466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>20015.379</td>\n",
       "      <td>0.020682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           DATE      GDPCA       GDP\n",
       "DATE                                \n",
       "1928        NaT        NaN  0.011000\n",
       "1929 1929-01-01   1110.206  0.065200\n",
       "1930 1930-01-01   1015.751 -0.085079\n",
       "1931 1931-01-01    950.686 -0.064056\n",
       "1932 1932-01-01    828.061 -0.128986\n",
       "...         ...        ...       ...\n",
       "2018 2018-01-01  18609.078  0.029454\n",
       "2019 2019-01-01  19036.052  0.022944\n",
       "2020 2020-01-01  18509.143 -0.027680\n",
       "2021 2021-01-01  19609.812  0.059466\n",
       "2022 2022-01-01  20015.379  0.020682\n",
       "\n",
       "[95 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series = ['GDPCA']\n",
    "\n",
    "gdp_download = pdr.data.DataReader(series, \n",
    "                                   'fred', \n",
    "                                   start='1926-12-31')\n",
    "gdp_download.reset_index(inplace=True)\n",
    "gdp_download.set_index(pd.DatetimeIndex(gdp_download['DATE']).year, inplace=True)\n",
    "gdp_download['GDP'] = gdp_download['GDPCA'].pct_change()\n",
    "# https://fortunly.com/statistics/us-gdp-by-year-guide/#gref\n",
    "gdp_download.loc[1928, 'GDP'] = 0.0110\n",
    "gdp_download.loc[1929, 'GDP'] = 0.0652\n",
    "gdp_download.sort_index(inplace=True)\n",
    "gdp_download.to_csv('gdp_fred.csv')\n",
    "\n",
    "gdp_download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP</th>\n",
       "      <th>CPI</th>\n",
       "      <th>S&amp;P</th>\n",
       "      <th>T-Bills</th>\n",
       "      <th>T-Notes</th>\n",
       "      <th>Baa Corps</th>\n",
       "      <th>Gold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</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>1928</th>\n",
       "      <td>0.011000</td>\n",
       "      <td>-0.0115607</td>\n",
       "      <td>0.454932</td>\n",
       "      <td>0.0428561</td>\n",
       "      <td>0.0201483</td>\n",
       "      <td>0.044268</td>\n",
       "      <td>0.0115607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>0.065200</td>\n",
       "      <td>0.005848</td>\n",
       "      <td>-0.088311</td>\n",
       "      <td>0.0256023</td>\n",
       "      <td>0.0359796</td>\n",
       "      <td>0.0241891</td>\n",
       "      <td>-0.005848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>-0.085079</td>\n",
       "      <td>-0.0639535</td>\n",
       "      <td>-0.200079</td>\n",
       "      <td>0.116932</td>\n",
       "      <td>0.116835</td>\n",
       "      <td>0.0740896</td>\n",
       "      <td>0.0639535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>-0.064056</td>\n",
       "      <td>-0.0931677</td>\n",
       "      <td>-0.380674</td>\n",
       "      <td>0.128213</td>\n",
       "      <td>0.0745222</td>\n",
       "      <td>-0.0701784</td>\n",
       "      <td>0.0931677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>-0.128986</td>\n",
       "      <td>-0.10274</td>\n",
       "      <td>0.0181843</td>\n",
       "      <td>0.126429</td>\n",
       "      <td>0.212472</td>\n",
       "      <td>0.377411</td>\n",
       "      <td>0.10274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.029454</td>\n",
       "      <td>0.0191016</td>\n",
       "      <td>-0.06022</td>\n",
       "      <td>0.000256552</td>\n",
       "      <td>-0.0189074</td>\n",
       "      <td>-0.045852</td>\n",
       "      <td>-0.0283967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.022944</td>\n",
       "      <td>0.0228513</td>\n",
       "      <td>0.282803</td>\n",
       "      <td>-0.00217676</td>\n",
       "      <td>0.0718628</td>\n",
       "      <td>0.127529</td>\n",
       "      <td>0.161472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>-0.027680</td>\n",
       "      <td>0.0136201</td>\n",
       "      <td>0.164373</td>\n",
       "      <td>-0.00993734</td>\n",
       "      <td>0.0983592</td>\n",
       "      <td>0.0892793</td>\n",
       "      <td>0.232526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>0.059466</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.199522</td>\n",
       "      <td>-0.0658728</td>\n",
       "      <td>-0.107526</td>\n",
       "      <td>-0.0575775</td>\n",
       "      <td>-0.114309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>0.020682</td>\n",
       "      <td>0.0642</td>\n",
       "      <td>-0.229552</td>\n",
       "      <td>-0.0413007</td>\n",
       "      <td>-0.227854</td>\n",
       "      <td>-0.19647</td>\n",
       "      <td>-0.0598253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           GDP        CPI        S&P      T-Bills    T-Notes  Baa Corps  \\\n",
       "Year                                                                      \n",
       "1928  0.011000 -0.0115607   0.454932    0.0428561  0.0201483   0.044268   \n",
       "1929  0.065200   0.005848  -0.088311    0.0256023  0.0359796  0.0241891   \n",
       "1930 -0.085079 -0.0639535  -0.200079     0.116932   0.116835  0.0740896   \n",
       "1931 -0.064056 -0.0931677  -0.380674     0.128213  0.0745222 -0.0701784   \n",
       "1932 -0.128986   -0.10274  0.0181843     0.126429   0.212472   0.377411   \n",
       "...        ...        ...        ...          ...        ...        ...   \n",
       "2018  0.029454  0.0191016   -0.06022  0.000256552 -0.0189074  -0.045852   \n",
       "2019  0.022944  0.0228513   0.282803  -0.00217676  0.0718628   0.127529   \n",
       "2020 -0.027680  0.0136201   0.164373  -0.00993734  0.0983592  0.0892793   \n",
       "2021  0.059466      0.071   0.199522   -0.0658728  -0.107526 -0.0575775   \n",
       "2022  0.020682     0.0642  -0.229552   -0.0413007  -0.227854   -0.19647   \n",
       "\n",
       "           Gold  \n",
       "Year             \n",
       "1928  0.0115607  \n",
       "1929  -0.005848  \n",
       "1930  0.0639535  \n",
       "1931  0.0931677  \n",
       "1932    0.10274  \n",
       "...         ...  \n",
       "2018 -0.0283967  \n",
       "2019   0.161472  \n",
       "2020   0.232526  \n",
       "2021  -0.114309  \n",
       "2022 -0.0598253  \n",
       "\n",
       "[95 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df = download_df.copy()\n",
    "data_df = data_df.rename(columns = {'Inflation Rate': 'CPI',\n",
    "           'S&P 500 (includes dividends)2': 'S&P',\n",
    "           '3-month T. Bill (Real)': 'T-Bills',\n",
    "           '!0-year T.Bonds': 'T-Notes',\n",
    "           'Baa Corp Bonds': 'Baa Corps',\n",
    "          })[['CPI', 'S&P', 'T-Bills', 'T-Notes', 'Baa Corps']]\n",
    "data_df[\"GDP\"] = gdp_download['GDP']\n",
    "data_df[\"Gold\"] = gold_download['gold'] - data_df['CPI']\n",
    "# reorder\n",
    "data_df = data_df[['GDP', 'CPI', 'S&P', 'T-Bills', 'T-Notes', 'Baa Corps', 'Gold']]\n",
    "data_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in data_df.columns:\n",
    "    data_df[col] = data_df[col].astype(float)\n",
    "\n",
    "# compute correlation matrix\n",
    "my_cmap = sns.diverging_palette(10, 220, sep=80, n=50)\n",
    "sns.heatmap(data_df.corr(), annot=True, fmt=\".02f\", cmap=my_cmap);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>S&amp;P</th>\n",
       "      <th>T-Bills</th>\n",
       "      <th>T-Notes</th>\n",
       "      <th>Baa Corps</th>\n",
       "      <th>Gold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</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>1928</th>\n",
       "      <td>0.454932</td>\n",
       "      <td>0.042856</td>\n",
       "      <td>0.020148</td>\n",
       "      <td>0.044268</td>\n",
       "      <td>0.011561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>-0.088311</td>\n",
       "      <td>0.025602</td>\n",
       "      <td>0.035980</td>\n",
       "      <td>0.024189</td>\n",
       "      <td>-0.005848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>-0.200079</td>\n",
       "      <td>0.116932</td>\n",
       "      <td>0.116835</td>\n",
       "      <td>0.074090</td>\n",
       "      <td>0.063953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>-0.380674</td>\n",
       "      <td>0.128213</td>\n",
       "      <td>0.074522</td>\n",
       "      <td>-0.070178</td>\n",
       "      <td>0.093168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>0.018184</td>\n",
       "      <td>0.126429</td>\n",
       "      <td>0.212472</td>\n",
       "      <td>0.377411</td>\n",
       "      <td>0.102740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>-0.060220</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>-0.018907</td>\n",
       "      <td>-0.045852</td>\n",
       "      <td>-0.028397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.282803</td>\n",
       "      <td>-0.002177</td>\n",
       "      <td>0.071863</td>\n",
       "      <td>0.127529</td>\n",
       "      <td>0.161472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.164373</td>\n",
       "      <td>-0.009937</td>\n",
       "      <td>0.098359</td>\n",
       "      <td>0.089279</td>\n",
       "      <td>0.232526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>0.199522</td>\n",
       "      <td>-0.065873</td>\n",
       "      <td>-0.107526</td>\n",
       "      <td>-0.057578</td>\n",
       "      <td>-0.114309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>-0.229552</td>\n",
       "      <td>-0.041301</td>\n",
       "      <td>-0.227854</td>\n",
       "      <td>-0.196470</td>\n",
       "      <td>-0.059825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           S&P   T-Bills   T-Notes  Baa Corps      Gold\n",
       "Year                                                   \n",
       "1928  0.454932  0.042856  0.020148   0.044268  0.011561\n",
       "1929 -0.088311  0.025602  0.035980   0.024189 -0.005848\n",
       "1930 -0.200079  0.116932  0.116835   0.074090  0.063953\n",
       "1931 -0.380674  0.128213  0.074522  -0.070178  0.093168\n",
       "1932  0.018184  0.126429  0.212472   0.377411  0.102740\n",
       "...        ...       ...       ...        ...       ...\n",
       "2018 -0.060220  0.000257 -0.018907  -0.045852 -0.028397\n",
       "2019  0.282803 -0.002177  0.071863   0.127529  0.161472\n",
       "2020  0.164373 -0.009937  0.098359   0.089279  0.232526\n",
       "2021  0.199522 -0.065873 -0.107526  -0.057578 -0.114309\n",
       "2022 -0.229552 -0.041301 -0.227854  -0.196470 -0.059825\n",
       "\n",
       "[95 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop CPI, GDP which are not assets\n",
    "data_df.drop(labels=['CPI', 'GDP'], axis=1, inplace=True)\n",
    "    \n",
    "df = data_df.copy()\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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.plot.line();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute correlation matrix\n",
    "my_cmap = sns.diverging_palette(10, 220, sep=80, n=50)\n",
    "sns.heatmap(df.corr(), annot=True, fmt=\".02f\", cmap=my_cmap);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['S&P', 'T-Bills', 'T-Notes', 'Baa Corps', 'Gold']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop T-Notes, allocations are small, favors corps \n",
    "# df.drop(labels=['T-Notes'], axis=1, inplace=True)\n",
    "labels = list(df.columns)\n",
    "labels\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Long-only optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S&amp;P</th>\n",
       "      <th>T-Bills</th>\n",
       "      <th>T-Notes</th>\n",
       "      <th>Baa Corps</th>\n",
       "      <th>Gold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</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>1928</th>\n",
       "      <td>0.454932</td>\n",
       "      <td>0.042856</td>\n",
       "      <td>0.020148</td>\n",
       "      <td>0.044268</td>\n",
       "      <td>0.011561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>-0.088311</td>\n",
       "      <td>0.025602</td>\n",
       "      <td>0.035980</td>\n",
       "      <td>0.024189</td>\n",
       "      <td>-0.005848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>-0.200079</td>\n",
       "      <td>0.116932</td>\n",
       "      <td>0.116835</td>\n",
       "      <td>0.074090</td>\n",
       "      <td>0.063953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>-0.380674</td>\n",
       "      <td>0.128213</td>\n",
       "      <td>0.074522</td>\n",
       "      <td>-0.070178</td>\n",
       "      <td>0.093168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>0.018184</td>\n",
       "      <td>0.126429</td>\n",
       "      <td>0.212472</td>\n",
       "      <td>0.377411</td>\n",
       "      <td>0.102740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>-0.060220</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>-0.018907</td>\n",
       "      <td>-0.045852</td>\n",
       "      <td>-0.028397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.282803</td>\n",
       "      <td>-0.002177</td>\n",
       "      <td>0.071863</td>\n",
       "      <td>0.127529</td>\n",
       "      <td>0.161472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.164373</td>\n",
       "      <td>-0.009937</td>\n",
       "      <td>0.098359</td>\n",
       "      <td>0.089279</td>\n",
       "      <td>0.232526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>0.199522</td>\n",
       "      <td>-0.065873</td>\n",
       "      <td>-0.107526</td>\n",
       "      <td>-0.057578</td>\n",
       "      <td>-0.114309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>-0.229552</td>\n",
       "      <td>-0.041301</td>\n",
       "      <td>-0.227854</td>\n",
       "      <td>-0.196470</td>\n",
       "      <td>-0.059825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           S&P   T-Bills   T-Notes  Baa Corps      Gold\n",
       "Year                                                   \n",
       "1928  0.454932  0.042856  0.020148   0.044268  0.011561\n",
       "1929 -0.088311  0.025602  0.035980   0.024189 -0.005848\n",
       "1930 -0.200079  0.116932  0.116835   0.074090  0.063953\n",
       "1931 -0.380674  0.128213  0.074522  -0.070178  0.093168\n",
       "1932  0.018184  0.126429  0.212472   0.377411  0.102740\n",
       "...        ...       ...       ...        ...       ...\n",
       "2018 -0.060220  0.000257 -0.018907  -0.045852 -0.028397\n",
       "2019  0.282803 -0.002177  0.071863   0.127529  0.161472\n",
       "2020  0.164373 -0.009937  0.098359   0.089279  0.232526\n",
       "2021  0.199522 -0.065873 -0.107526  -0.057578 -0.114309\n",
       "2022 -0.229552 -0.041301 -0.227854  -0.196470 -0.059825\n",
       "\n",
       "[95 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S&amp;P</th>\n",
       "      <th>T-Bills</th>\n",
       "      <th>T-Notes</th>\n",
       "      <th>Baa Corps</th>\n",
       "      <th>Gold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rows</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>real return</th>\n",
       "      <td>0.082703</td>\n",
       "      <td>0.003059</td>\n",
       "      <td>0.018803</td>\n",
       "      <td>0.039114</td>\n",
       "      <td>0.034383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>0.195024</td>\n",
       "      <td>0.038355</td>\n",
       "      <td>0.089697</td>\n",
       "      <td>0.089563</td>\n",
       "      <td>0.203529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  S&P   T-Bills   T-Notes  Baa Corps      Gold\n",
       "rows                                                          \n",
       "real return  0.082703  0.003059  0.018803   0.039114  0.034383\n",
       "vol          0.195024  0.038355  0.089697   0.089563  0.203529"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute covariance matrix\n",
    "Sigma = np.cov(df.transpose())\n",
    "# number of assets\n",
    "\n",
    "n = Sigma.shape[0]\n",
    "# average returns\n",
    "mu = df.mean().values\n",
    "# asset STDs\n",
    "asset_vols = np.sqrt(Sigma.diagonal())\n",
    "# variable to optimize over - portfolio weights\n",
    "w = cp.Variable(n)\n",
    "\n",
    "# objectives to optimize\n",
    "# portfolio return\n",
    "ret = mu.T @ w \n",
    "# volatility\n",
    "vol = cp.quad_form(w, Sigma)\n",
    "\n",
    "z = pd.DataFrame([mu, asset_vols], columns=labels)\n",
    "z['rows'] = ['real return', 'vol']\n",
    "z.set_index('rows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable((5,))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.03803418,  0.00045325,  0.00168844,  0.0076717 , -0.00310605],\n",
       "       [ 0.00045325,  0.00147108,  0.00197492,  0.00161778, -0.00050053],\n",
       "       [ 0.00168844,  0.00197492,  0.00804558,  0.00578051, -0.00065466],\n",
       "       [ 0.0076717 ,  0.00161778,  0.00578051,  0.00802145, -0.0006397 ],\n",
       "       [-0.00310605, -0.00050053, -0.00065466, -0.0006397 ,  0.04142417]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max return portfolio weights\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>S&amp;P</th>\n",
       "      <th>T-Bills</th>\n",
       "      <th>T-Notes</th>\n",
       "      <th>Baa Corps</th>\n",
       "      <th>Gold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   S&P  T-Bills  T-Notes  Baa Corps  Gold\n",
       "0  1.0      0.0      0.0        0.0   0.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solve max return portfolio (corner solution)\n",
    "prob = cp.Problem(cp.Maximize(ret), \n",
    "                  [cp.sum(w) == 1, \n",
    "                   w >= 0]\n",
    "                 )\n",
    "prob.solve()\n",
    "wts = [float('%0.4f' % v) for v in w.value]\n",
    "maxretvol = vol.value\n",
    "maxret = ret.value\n",
    "print(\"Max return portfolio weights\")\n",
    "pd.DataFrame([wts], columns=labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min vol portfolio weights\n"
     ]
    },
    {
     "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>S&amp;P</th>\n",
       "      <th>T-Bills</th>\n",
       "      <th>T-Notes</th>\n",
       "      <th>Baa Corps</th>\n",
       "      <th>Gold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.028259</td>\n",
       "      <td>0.925803</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.045937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        S&P   T-Bills  T-Notes  Baa Corps      Gold\n",
       "0  0.028259  0.925803     -0.0       -0.0  0.045937"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# solve min vol portfolio (other corner solution)\n",
    "prob = cp.Problem(cp.Minimize(vol),\n",
    "                  [cp.sum(w) == 1, \n",
    "                   w >= 0]\n",
    "                 )\n",
    "prob.solve()\n",
    "# round to not get x.xxxxE-22\n",
    "wts = [float('%0.6f' % v) for v in w.value]\n",
    "\n",
    "minvol = vol.value\n",
    "minvolret = ret.value\n",
    "print(\"Min vol portfolio weights\")\n",
    "pd.DataFrame([wts], columns=labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 462 ms, sys: 6.25 ms, total: 468 ms\n",
      "Wall time: 472 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# solve points in between\n",
    "# maximize return subject to volatility constraints between minimum volatility and max return volatility\n",
    "\n",
    "# specify a Parameter variable instead of creating new Problem at each iteration\n",
    "# this allows the solver to reuse previous work\n",
    "vol_limit = cp.Parameter(nonneg=True)\n",
    "\n",
    "prob = cp.Problem(cp.Maximize(ret),\n",
    "                  [cp.sum(w) == 1, \n",
    "                   w >= 0,\n",
    "                   vol <= vol_limit\n",
    "                  ]\n",
    "                 )\n",
    "\n",
    "# define function so we can solve many in parallel\n",
    "def solve_vl(vl_val):\n",
    "    vol_limit.value = vl_val\n",
    "    result = prob.solve()\n",
    "    return (ret.value, np.sqrt(vol.value), w.value)\n",
    "\n",
    "# number of points on the frontier\n",
    "NPOINTS = 200\n",
    "vl_vals = np.linspace(minvol, maxretvol, NPOINTS)\n",
    "\n",
    "# iterate in-process\n",
    "results_dict = {}\n",
    "for vl_val in vl_vals:\n",
    "    # print(datetime.strftime(datetime.now(), \"%H:%M:%S\"), vl_val)\n",
    "    results_dict[vl_val] = solve_vl(vl_val)\n",
    "    \n",
    "# parallel implementation\n",
    "# NPROCESSES = 8\n",
    "# pool = Pool(processes = NPROCESSES)\n",
    "# result_values = pool.map(solve_vl, vl_vals)\n",
    "# results_dict = dict(zip(vl_vals, result_values))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>i</th>\n",
       "      <th>vol</th>\n",
       "      <th>return</th>\n",
       "      <th>std</th>\n",
       "      <th>S&amp;P</th>\n",
       "      <th>T-Bills</th>\n",
       "      <th>T-Notes</th>\n",
       "      <th>Baa Corps</th>\n",
       "      <th>Gold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.001352</td>\n",
       "      <td>0.006755</td>\n",
       "      <td>0.036766</td>\n",
       "      <td>0.028326</td>\n",
       "      <td>9.257119e-01</td>\n",
       "      <td>3.774632e-08</td>\n",
       "      <td>5.675939e-08</td>\n",
       "      <td>4.596251e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.001536</td>\n",
       "      <td>0.012975</td>\n",
       "      <td>0.039193</td>\n",
       "      <td>0.075711</td>\n",
       "      <td>8.076785e-01</td>\n",
       "      <td>1.484428e-07</td>\n",
       "      <td>4.939905e-02</td>\n",
       "      <td>6.721162e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.001720</td>\n",
       "      <td>0.015854</td>\n",
       "      <td>0.041478</td>\n",
       "      <td>0.088898</td>\n",
       "      <td>7.427346e-01</td>\n",
       "      <td>4.992977e-09</td>\n",
       "      <td>9.317261e-02</td>\n",
       "      <td>7.519515e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.001905</td>\n",
       "      <td>0.018100</td>\n",
       "      <td>0.043643</td>\n",
       "      <td>0.099208</td>\n",
       "      <td>6.920751e-01</td>\n",
       "      <td>6.915012e-08</td>\n",
       "      <td>1.273053e-01</td>\n",
       "      <td>8.141171e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.002089</td>\n",
       "      <td>0.020008</td>\n",
       "      <td>0.045706</td>\n",
       "      <td>0.107924</td>\n",
       "      <td>6.490217e-01</td>\n",
       "      <td>4.998250e-09</td>\n",
       "      <td>1.563451e-01</td>\n",
       "      <td>8.670957e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>195</td>\n",
       "      <td>0.037297</td>\n",
       "      <td>0.082266</td>\n",
       "      <td>0.193124</td>\n",
       "      <td>0.990954</td>\n",
       "      <td>7.068287e-09</td>\n",
       "      <td>1.184676e-08</td>\n",
       "      <td>3.207979e-08</td>\n",
       "      <td>9.046423e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>196</td>\n",
       "      <td>0.037481</td>\n",
       "      <td>0.082376</td>\n",
       "      <td>0.193601</td>\n",
       "      <td>0.993231</td>\n",
       "      <td>3.596520e-08</td>\n",
       "      <td>6.008441e-08</td>\n",
       "      <td>1.613587e-07</td>\n",
       "      <td>6.768475e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>197</td>\n",
       "      <td>0.037666</td>\n",
       "      <td>0.082486</td>\n",
       "      <td>0.194076</td>\n",
       "      <td>0.995498</td>\n",
       "      <td>1.704891e-08</td>\n",
       "      <td>2.847333e-08</td>\n",
       "      <td>7.626422e-08</td>\n",
       "      <td>4.501649e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>198</td>\n",
       "      <td>0.037850</td>\n",
       "      <td>0.082595</td>\n",
       "      <td>0.194550</td>\n",
       "      <td>0.997754</td>\n",
       "      <td>6.377374e-09</td>\n",
       "      <td>1.061023e-08</td>\n",
       "      <td>2.768640e-08</td>\n",
       "      <td>2.245535e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>199</td>\n",
       "      <td>0.038034</td>\n",
       "      <td>0.082703</td>\n",
       "      <td>0.195024</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.171861e-08</td>\n",
       "      <td>1.462185e-08</td>\n",
       "      <td>2.167183e-08</td>\n",
       "      <td>1.996587e-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       i       vol    return       std       S&P       T-Bills       T-Notes  \\\n",
       "0      0  0.001352  0.006755  0.036766  0.028326  9.257119e-01  3.774632e-08   \n",
       "1      1  0.001536  0.012975  0.039193  0.075711  8.076785e-01  1.484428e-07   \n",
       "2      2  0.001720  0.015854  0.041478  0.088898  7.427346e-01  4.992977e-09   \n",
       "3      3  0.001905  0.018100  0.043643  0.099208  6.920751e-01  6.915012e-08   \n",
       "4      4  0.002089  0.020008  0.045706  0.107924  6.490217e-01  4.998250e-09   \n",
       "..   ...       ...       ...       ...       ...           ...           ...   \n",
       "195  195  0.037297  0.082266  0.193124  0.990954  7.068287e-09  1.184676e-08   \n",
       "196  196  0.037481  0.082376  0.193601  0.993231  3.596520e-08  6.008441e-08   \n",
       "197  197  0.037666  0.082486  0.194076  0.995498  1.704891e-08  2.847333e-08   \n",
       "198  198  0.037850  0.082595  0.194550  0.997754  6.377374e-09  1.061023e-08   \n",
       "199  199  0.038034  0.082703  0.195024  1.000000  1.171861e-08  1.462185e-08   \n",
       "\n",
       "        Baa Corps          Gold  \n",
       "0    5.675939e-08  4.596251e-02  \n",
       "1    4.939905e-02  6.721162e-02  \n",
       "2    9.317261e-02  7.519515e-02  \n",
       "3    1.273053e-01  8.141171e-02  \n",
       "4    1.563451e-01  8.670957e-02  \n",
       "..            ...           ...  \n",
       "195  3.207979e-08  9.046423e-03  \n",
       "196  1.613587e-07  6.768475e-03  \n",
       "197  7.626422e-08  4.501649e-03  \n",
       "198  2.768640e-08  2.245535e-03  \n",
       "199  2.167183e-08  1.996587e-08  \n",
       "\n",
       "[200 rows x 9 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_df = pd.DataFrame(enumerate(results_dict.keys()))\n",
    "ret_df.columns=['i', 'vol']\n",
    "ret_df['return'] = [results_dict[v][0] for v in ret_df['vol']]\n",
    "ret_df['std'] = [results_dict[v][1] for v in ret_df['vol']]\n",
    "for i, colname in enumerate(labels):\n",
    "    ret_df[colname]=[results_dict[v][2][i] for v in ret_df['vol']]\n",
    "ret_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot efficient frontier\n",
    "def plot_efrontier(ret_df, df,\n",
    "                   xlabel=\"Standard Deviation of Real Returns\",\n",
    "                   ylabel=\"Real Return\",\n",
    "                   title=None):\n",
    "\n",
    "    Sigma = np.cov(df.transpose())\n",
    "    n = Sigma.shape[0]\n",
    "    mu = df.mean().values\n",
    "    asset_vols = np.sqrt(Sigma.diagonal())\n",
    "\n",
    "    plt.figure(figsize=(8, 4.5))\n",
    "\n",
    "    # plot the data\n",
    "    plt.plot(ret_df['std'], ret_df['return'])\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plot_title = \"Risk vs. Real Return,  %d-%d\" % (df.index[0], df.index[-1]) if title is None else title\n",
    "    plt.title(plot_title)\n",
    "\n",
    "    # plot the markers\n",
    "    plt.scatter(asset_vols, mu)\n",
    "    xoffset = 0.0025\n",
    "    yoffset = 0.0015\n",
    "    labels = df.columns\n",
    "    for i, label in enumerate(labels):\n",
    "        plt.annotate(label, xy=(asset_vols[i]+xoffset, mu[i]+yoffset),  xycoords='data',\n",
    "                     horizontalalignment='left', verticalalignment='top',\n",
    "                    )\n",
    "        \n",
    "        \n",
    "plot_efrontier(ret_df, df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# stacked area chart of weights vs. returns\n",
    "# for given vol constraint and corresponding real return, show portfolio weights\n",
    "def transition_map(ret_df, labels, startyear, endyear):\n",
    "    \n",
    "    x = ret_df['return']\n",
    "    # absolute values so shorts don't create chaos\n",
    "    y_list = [abs(ret_df[l]) for l in labels]\n",
    "    pal = ['red', 'lightgreen', 'darkgreen', 'navy', 'gold', 'cyan']\n",
    "    \n",
    "    fig = plt.figure(figsize=(8, 4.5))\n",
    "    ax1 = fig.add_subplot(111)\n",
    "\n",
    "    ax1.stackplot(x, y_list, labels=labels, colors=pal)\n",
    "    ax1.set_xlabel(\"Portfolio Real Return\")\n",
    "    ax1.set_ylabel(\"Portfolio Weight\")\n",
    "    ax1.legend(loc='lower right')\n",
    "    ax2 = ax1.twiny()\n",
    "    ax2.set_xlim((ret_df['std'].iloc[0], ret_df['std'].iloc[-1]))\n",
    "    ax2.set_xlabel('Portfolio Vol')\n",
    "    \n",
    "    \n",
    "    plt.title(\"Optimal Portfolio Transition Map, %d-%d\" % (startyear, endyear), y=1.16);\n",
    "\n",
    "transition_map(ret_df, labels=df.columns, startyear=df.index[0], endyear=df.index[-1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1972 - present (post gold standard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yYsQIhg8fzkMPHcshZDQaeeyxx5gwYQJbtmwhKSmJhx56iPHjxzN+/HhvaoYvvviC4cOHk5mZybRp0077dcupFQYYh82FKIL6uCgdkKteyfwyqHrmGWxH+jY9snboEKIeeaTHNhaLhaysLKxWK5WVld7kZTqdjm+++YaAgADq6uqYOHEil156KYIg8M477xASEoLFYmHcuHFcccUVhIaGevvMy8sjISGBgICuMSgVFRU89NBD7Nq1i+DgYGbPns2iRYuYP38+bW1tDB8+nCeffNLbPiAggO3bt/PBBx/w29/+lsWLF/Pkk0+yfPlyYmNj+ySlspw8bYBxHJcLH6Q4fJAXbWVk+hOPpJOdnc2yZctYsGABoigiiiKPPPIII0eOZNasWZSXl1NdXQ3AK6+8QmZmJhMnTqS0tJTc3NxeX2/Hjh1Mnz6d8PBwVCoVN9xwgzelslKp5IorrujU/rrrrvP+u2XLFgAmT57MzTffzFtvvYXLdfr2QfbwBxhbe+K0zpKOXNdW5pfDiTzxgWDSpEnU1dVRW1vLkiVLqK2tZdeuXajVapKSkrBaraxbt45Vq1axZcsWDAYD06dPx2q1duonLS2NkpISTCYT/v7+nY71lKdMp9N10e2lfamd///GG2+wbds2fvjhB7Kysti7d2+nGcbJInv4A4wnF35HSUcOy5SRGViys7NxuVyEhobS3NxMREQEarWatWvXUlxcDEBzczPBwcEYDAays7PZunVrl34MBgO33XYb999/v7cYemVlJR999BETJkxg/fr11NXV4XK5+PTTT70plX3x2Wefef+dNGkSAPn5+UyYMIEnn3ySsLAwSktLuz2/N8ge/gDj8OTC97VoK9e1lZHpNzwaPkje9/vvv49SqeSGG27gkksuYezYsWRlZTFkiLRF6IILLuCNN95g5MiRDB48mIkTJ/rs96mnnuLPf/4zGRkZ6HQ6/Pz8ePLJJ4mOjubvf/87M2bMQBRFLrroIm+6ZV/YbDYmTJiA2+32FlD5wx/+QG5uLqIoMnPmTDIzM0/rPZDTIw8webtqWP7WQa7583jC4oyAlFvnrQc3cM4VaYw6P+EMj1BGpu+R0yP3TFJSEjt37iQsLOykzjsj6ZFles/x5Q2hw6KtrOHLyMj0I7KkM8D4itJRKASUKrnqlYzML5WioqIBuY7s4Q8wNm95w84r9CqtQl60lZGR6Vdkgz/A2K1OVFolCmXnt14tFzKXkZHpZ2SDP8A4LM4u3j1IkTpy8jQZGZn+RDb4A4zd6uqk33tQyWUOZWRk+hnZ4A8wdouzUwy+B7Vc5lBGpl+prq7m+uuvJyUlhTFjxjBp0iS++eabbtuvW7eOiy++2OexpKQk6up+fgl/ZYM/wByfC9+D7OHLyPQfoigyf/58pk2bRkFBAbt27WLhwoWUlZWd6aENKL8og7/if4fYvbz4jI7BbnV14+HLYZkyMv3FmjVr0Gg03HXXXd7nEhMTue+++7Bardxyyy2MGDGCUaNGsXbt2i7n19fXM3v2bEaNGsWvf/3rHvPk/JT5xcThW1sd5O6opmh/HcOmxqA1qM/IOLqVdOQoHZlfCBs/z6GutLVP+wyLNzL16vRujx86dIjRo0f7PPbaa68BcODAAbKzs5k9ezY5OTmd2jzxxBNMmTKFxx57jB9++MFnbvyfA78Yg1+e2whI+egPbihnzAVJZ2Qc9m6idFRaJU45SkdGZkC455572LRpExqNhri4OO677z4AhgwZQmJiYheDv2HDBr7++msA5s6d6y2e8nOjTwy+IAgXAC8jlTh8WxTFZ320mQ68BKiBOlEUu08b1w+U5zSh0iiITApg/5oysmYmoFQPrKIlukXsNt9ROrKHL/NLoSdPvL8YNmwYX331lffxa6+9Rl1dHWPHjiU2NrZXfXRMX/xz5bQtniAISuA14EIgA7hOEISM49oEAf8BLhVFcRhw1ele92QpP9pIdGogYy5IwtxiJ2dH1UAPQdLoRXxKOiqtEpfDjdv989QGZWR+ypx33nlYrVZef/1173NmsxmAadOm8fHHHwOQk5NDSUkJgwcP7nR+xzZLly6lsbFxgEbet/SFizseyBNFsUAURTuwEDg+B+j1wNeiKJYAiKJY0wfX7TUWk52GijZiBwcTNzSY0Dgje1aWIg6wcfUmTvO18UouZC4j028IgsCiRYtYv349ycnJjB8/nptuuonnnnuOu+++G5fLxYgRI7jmmmt47733vPVpPfz1r39lw4YNjB49mhUrVpCQ8PPMatsXkk4s0DErfxkw4bg26YBaEIR1gD/wsiiKH/jqTBCEO4E7gT57U8tzmqSBpgcjCAKjzk9g1buHKT5UT9KIk0tHejrYfeTC9+ApiOLoRvKRkZE5PaKjo1m4cKHPY++9916X56ZPn8706dMBCA0NZcWKFd5jL774Yn8Msd/pCw/fl7B1vOusAsYAc4E5wF8EQfAp5Imi+KYoimNFURwbHh7eB8OD8pxGVFol4YlSCbK0sREYg7XsWVHSJ/33lmMevi8NX/ooZA9fRkamv+gLg18GxHd4HAdU+GizTBTFNlEU64ANwOmVbjkJyo82EpMWiLI9YZlSqSBzZjwVuU1UF7UM1DCwezJldqPhA3I+HRkZmX6jLwz+DmCQIAjJgiBogGuB745r8y0wVRAElSAIBiTJ50gfXPuEmFvsNFaZiU3vHEaVMSUGjV41oF6+3ZsLv3sNX958JXO28nPdrPRT5VTez9M2+KIoOoF7geVIRvxzURQPCYJwlyAId7W3OQIsA/YD25FCNw+e7rV7Q3mOtJp+vMHX6FQMnxZDwZ4ammstAzGUXnn4ck58mbMRnU5HfX29bPT7CFEUqa+vR6fTndR5fbI6KIriEmDJcc+9cdzj54Hn++J6J0N5ThNqnZLwBGOXYyNnxLN3VSn7Vpcy7dr+jw0+Vt7Qdxw+IMfiy5yVxMXFUVZWRm1t7ZkeylmDTqcjLi7upM4568NBJP0+qEvBEQC/IC3p4yM58mMF4y9ORmfs33QLHg9frfWdDx9kSUfm7EStVpOcnHymh/GL56xOntbWZKOpuqt+35Gs8xNw2t0cWN//WfPsFhdqrRKFomtgk0qOw5eRkelnzmqD78mfEzs4qNs2oTFGIhL9qcht6vfx2K2+E6eBlC0TZA9fRkam/zi7Df7RJjR6FWHx/j220xk1XrmlP+kuFz50WLSVPXwZGZl+4iw3+I3EDAryKaF0RKNXekMm+5PucuGDtDdAoRTkOHwZGZl+46w1+K2NVpprLcSmB52wrUanGhgPv5tc+B7UWjljpoyMTP9x1hr8jvlzToRGr/KGTPYn3eXC96DSKOU4fBkZmX7j7DX4RxvRGlSExXWNvz8ejU6J0+7G7epfOcVu7Tkxmuzhy8jI9Cdnr8HPkfR74QT6PRxLZtbfOv6JJB2VRiF7+DIyMv3GWWnwTQ1WWuqsvZJzQFq0BfpVx3e7xfbUx91LOrKHLyMj05+clQbfmz9ncG8Nfv97+J74+h4XbTVKOUpHRkam3zg7Df7RRnR+akJj/HrV/pik038evjdxWg8avkqrlOPwZWRk+o2z1OA3EZPeO/0eOhj8fpR0esqU6UHy8GWDLyMj0z+cdcnTnA4XMYOCiM8I6fU5Xg2/Pz38HnLhe5A9fBkZmf7krDP4KrWSWbdknNQ5Xg3f0n/GtncevkL28GVkZPqNs1LSOVkGRNLpoZ6tB8nDdyO65SIRMjIyfU+fGHxBEC4QBOGoIAh5giD8qYd24wRBcAmCcGVfXLevUGkUCMIALdrqewjL9KRIdsiROjIyMn3PaRt8QRCUwGvAhUAGcJ0gCF00lfZ2zyGVQvxJIQhCe3qF/pR0PBp+zzttQU6RLCMj0z/0hYc/HsgTRbFAFEU7sBCY56PdfcBXQE0fXLPP0ehUOPpb0hF8V7vyIBdBkZGR6U/6wuDHAqUdHpe1P+dFEIRY4DKgU53bnxIavRJbPxt8jVbZY6io7OHLyMj0J31h8H1ZsONXHV8CHhJF8YSWTBCEOwVB2CkIws6BLHis0fWzpNNDLnwPKk171SvZw5eRkekH+iIsswyI7/A4Dqg4rs1YYKEgCABhwEWCIDhFUVx0fGeiKL4JvAkwduzYAQtXUetUWFvt/db/iRKnwTEPX06gJiMj0x/0hcHfAQwSBCEZKAeuBa7v2EAURW+5ekEQ3gMW+zL2ZxKtXklLXf/G4fe06Qo6SDp2OUpHRkam7zltgy+KolMQhHuRom+UwDuiKB4SBOGu9uM/Wd2+I2q9qp81fBc6w4kkHdnDl5GR6T/6ZKetKIpLgCXHPefT0IuieHNfXLOv6fcoHYuTgFBdj22OefiywZeRkel75J227Wj1SpwON65+qnplt/ZC0tHIUToyMjL9h2zw21G3b4hy9FM+HbvVhfpEUTpa6eOQ4/BlZGT6A9ngt9OfOfHdLjdOW8/1bAGUKinFg6OfSy3KdOaSf2/i7Y0FZ3oYMjL9jmzw2+nPFMme+H7tCTx8QRBQyWUOB5Rms4MD5c18tqP0xI1lZH7myAa/nf5Mkey5iahPoOGDtHArR+kMHCUNZgBya1opqG09w6ORkelfZIPfTn+mSO5N4jQPao1SjsMfQIob2rz/X36oulfn1Jis1Lfa+mtIMjL9hmzw2/FE0PSPpCP1eSJJB6Sc+HKUzsBRXC95+GkRRpYfqjphe1EUuf6tbZz/4gYOljf39/BkusHqcLEmu3c3aJljyAa/Ha+k0w8Lpp5Zg7qHXPge1Bq5zOFAUlJvJsyoYX5WDHtLm6hqtvbYfnthA3k1rbTZnFz/1lb2lDQO0EhlOvL+j0Xc+t5O+aZ7ksgGv51jGn7/efi9knS0cpnDgaSkwUxCiIE5w6IAWHm4Zy9/4Y5S/LUqfrh/KsF+Gn719ja2FtQPxFBlOrA6W8qyvjG37gyP5OeFbPDbUakVCAqhk8H/Zk8Zr6/LP+2+PRp+rySdU/Twv8//niZr00mf90unpMFMYqgfaRFGUsL8etTxm80OlhyoZN6oGNIijHz+60lEB+m5+d3trM8ZuMyuZ4KKvCZMDT3PfnqDpdVOc635tPpoNjvYVSzNrDbm/nTe97s/3sW/Vhw908PoEdngtyMIAhqdspOk88XOMp5fnk1RXVsPZ56YyvwmNDolOqP6hG3Vp6Dhl7aU8simR/gy98tTHeIvEpvTRUWzhYQQA4IgMHtYFFsL6mk2O3y2X7S3HJvTzbXjEgCIDNDx2Z0TSQ4zcsf7O1l5+OzUlGtLTHz7wh62Ljo958disvPVc7v49qW9p9XPhtxaXG6RsYnB7CxqxPITkEDdbpE12TW8s6mQVlv/pWg5XWSD3wEpJ/6xD6uhzY5bhP9uOPUvurXNQf6uWtLHR6FUnfjtluLwTy5KJ7cpF4D8ptOfjXy3r+In5a1aHS4+3FKEqx8Ku5c1WhBFSAgxAHDB8CicbpHVPhYDRVHk0+0lDI8NYHhsoPf5UKOWhXdMZGhMAL/5aBc/7K/s83GeSVxON6s/OILbLVJTbDrlfhx2Fz/8Zz/NtRZM9VZaG099trA2u4YQPw33nJeG3eVmW+GZl9RqTDasDjdtdhff7T0+O/xPB9ngd0CjV3WSdJraPb0vd5WdcDGvO45uq8LldJMxNaZX7dWak4/D9xj60zX4oijyxHeHePCzvbT1k5dSVNfGO5sKe91+xeFq/vLtoX6Zunti8BNDJYM/MjaQqACdz2id/WXNZFeZvN59RwINaj66bTwj4wL545f7qDUNbMhmfm0rI/66nKNVp26Qu2PXsmLqy1qJTgukqdp8SmtcbrfIqncOU13Uwug5iQBUF7ac0nhcbpF1ObWcmx7OpJRQNCrFT0LHL66XVACNSsEn24vP8Gi6Rzb4HdDolV4PXxRFGsx25o6Ixi3CW6ew9V4URQ5vqiAi0Z/weP9enaNu32krir33aPObJUNf0FyAy33q09uqFiv1bXYa2uy892PRKffTEy+tyuHJxYep62Uce3G7nLa3tKnPx1LSHpKZ0G7wFQqB2cMiWZ9T20UmWLijFL1ayaVZvm/c/jo1/7wqE5vTzUurcvp8rD2RXWnCZHOy6kjfSkp1ZSZ2LSli0LhIxl6YBEjyzskgiiKbv8ilYG8tU64cxPhLklGqFFSdosHfV9ZEQ5udGUMi0KmVjE8KYdNPwuBL36WbJiVysLyF/WVNZ3ZA3SAb/A5odCrvAqvF4cLudDMyLpB5mTF8sq2EhraTq4hVXdhCQ0UbGVN6591De5lDEVyO3ss6xbW5/PVjF6kFFspby09qjB05WC79COND9Px3fT7NFt9a9qlidbi8OrfnB3Iiitu98D0lTX06Fs8Y9Gol4Uat97k5w6KwOtydZK02m5Pv9pYzd2Q0Abru12FSwo3cMCGBhTtKya3ue2+7O2pM0uxzS37fSRsul5vV7x9B66di2jXphCdKDsvJyjr7Vpeyf20ZmTPjyZwZj1KlIDzBSHXhqYVTrs2uQSHAtEFhAEwdFMbRahPVLae/oHw6FNW3oVII3D09Db1aySfbSs7oeLpDNvgdkBZtJQ/fY9yDDRrump6KxeE6aa/38KYKVFolg8ZF9vqck82J73K7EHMKGFYiMn+LSF5T3kmNsSMHy5sRBHjpmixarM5OCcUsTgs21+lJFeuO1tDW/rpKGnq3EO7xwveWNp3UrKdXfTe0eRdsPYxPDiFQr2ZFB1nnh/2VtNldXDsu3lc3nbh/5iAMaiXPLs3u07GCFN57eHNFlxTeHglpR1EDNmffLGDuWV5MXWkr068fgs6oRm/U4B+io7ak95553q4aNn+ZR+qocCZfkeZ9PjI5kJpi0ymlIl+TXcOYxGCCDBoAprQb/jMt6xQ3mIkL1hPsp+GSzGi+21dBi1VymIoO1FF+9KexX0M2+B3oqOE3tkkfVrCfhvRIf2ZnRPLe5t6vwNstTnJ3VpM+NqJX8fceVCeZE7+8tZy4CunmNLJQpDRvb6+vdTyHKppJDTcyJjGEuSOjeWdTPqsKN/PIxkeYtnAav1v3u1PuG2Dx/kqCDWoEAYrqeuvht6FTK2i2OCg8zWip4ylpMHvlHA9qpYKZQyNYdaQaR7tB+nRHCWkRRsYkBp+wz1CjlnvOS2N1dg0/5vWtEcrdUc3aD7NZ8/4RxA6L2DXtBt/mdLO3D2ZC9eWt7PihiEFjI0gZFe59PiLRv9ce/sKluSz/3yGiUgKZdUsGguLYTTUyOQCXw0192cnlLqpusXKoooUZQyK8zw2NCiDMqGHTGQ7PLK5vIzHUD4DrJyRitrv4dk85DRVtLPvvQbacZoRTX9EnBl8QhAsEQTgqCEKeIAh/8nH8BkEQ9rf//SgIQmZfXLevkaJ0JEPbaPZ4+NIU/u4ZabRYnXy8tXcLMjk7qnHa3WRMiT2pMRwrZN477yevKY+kahFRr0UBqJZtOKnrdeRgeQsjYgOpaqsiLmkTxD/HgxvuYm3pWkL1oRysO3jKfZvtTlYfqeGiEdHEBOq9C6Y9YXW4qG6xMWuoNEPqS1lHFEUpBj/E0OXYnGFRtFidbCto4GiViT0lTVw7Lr7TTKAnbj4nidggPU8vkaJb+oqaYhMIkLO9mk1f5HpnPLUmG0mhBgQBtpzmJjC3R8oxqJh6bXqnY+GJ/jTXWrB1E7bakYKVZTTjIuv6NK8T4yEqRYpyOtmF27Xtm63O62DwFQqBKWlhbMqr69P3+mQQRZHierN38T8zLpCM6AA+2VrM6vcP43K6aa6xnJGxHc9pG3xBEJTAa8CFQAZwnSAIGcc1KwTOFUVxJPA34M3TvW5/oNErcTncuJzuYwbfT5o6ZsUHMSUtjLc2FmJ1nNj7PrypgtBYIxFJvVus9eCtetVLSSe/KZ/kKhF9Vhal6UEkbyxAdJ/8VLnWZKOqxUqd5nNmfzmbT3LfIkwXhbPqWhZesJSrB19NvbUek/3UtOk12TVYHC4uHhlDYqiBovoTe+ul7TeFmUMjMGpV7Cntu2mxJ4wuMbSrwZ82KBydWsHyQ1Us3FGCWilw+ei4XvetUyv54wWDOVTRwjd7Tn1N5XhqS0zEDQ4m87x49q8tY9fSIkB6LWkRRobFBJy2jr9nZQm1JSbOvW4weqOm07GIhADvOHrCZnYQYBY5pHbx1rauDpIxWIshUEPVSer4a7JriAnUMTiy829qyqBw6lrtHKk6tYXg06XR7MBkdXo9fEEQuH5CAgHFVmqKTUSlBGBtc/Rrzeze0hce/nggTxTFAlEU7cBCYF7HBqIo/iiKoufXuhXo/a9nAPFWvbK6vBp+iOHYl/7uGanUtdr4YldZj/3UlpioLTGRMSWm116hB5X25AqZF9TlkFAHfsOGUzczi+BGB61btpzUNQEOVjQjqBvY17KYOUlzWHr5Uj6Y+QbxxeGsfuljRny2h3FH3RS3HPsBu90iBXtrKT3ccML+F++rJNxfy/jkEBJDDb1atPW0SQr1IzM+sE8jdTx9x4cYuGvlXSzKW+Q9ptcoOTc9nOWHqvhmTzmzh0UR4qfppiffXDIyhpFxgfxzxdE+2RjkcripL28lPMGfyVemMXhCFNu+K+TghnJqTTbC/bVMSgllT0lTrxwSX9QUt7D9+0JSR0eQOjqiy3FfC7e5jbldAgXKc5oQAMFWyZe7yrw3bg+CIBCVHEh1Qe8NtM3pYlNeHTOGRHT5TU1t1/HPVLSOx3lJ6uA8TIsMZLJVRWuEhqxZUihvS21nL7+iydLlvelv+sLgxwIdq0eUtT/XHbcBS7s7KAjCnYIg7BQEYWdt7cDqcp7UBzaLk0azA0GAAP2xqIxJKaFkxQfx3/X5OHtYcDq0qQKlWkH6+N4v1no4WQ+/NecIKhfoMjIwzJxBqw4qP/vopK97qLwZddB2xuaK/PpzE7Zrfo35/Km8vOZFJn3+KsYvVnHjGjdFLUW4HG4Ob67g0ye2sfSNA6z58EjPY7Q5WXu0hrkjolEqBBJD/Whos3sXtbqj2Bsn78eo+GCOVJr6bFelJ246xN/J5orNLCtc1un4nGFR1JhsNJkdXOcj9v5EKBQCj140lMpmK+9s7v2+g+6or2jF7RIJT/BHUAjMWDCExBGhrP/0KGGNTsL9dUxKDcXucrO7+ORnQtY2B8vePIghUMP0Gwb7bKPzUxMQpvMafJfbxa9X/ppntz3bqV3JkQYEl50H9vwPlSDy6pqugQSRyQE011qwtPYu8m17YQNmu6uTnOPtK0Dy+vt64bas0YzdeeLZsiewwDNbdLncbP00F9QKPnWZUARINqS53eCLosgXO0uZ+a/13PXRrj4d84noC4Pvy4X1KaYJgjADyeA/1F1noii+KYriWFEUx4aHh3fXrF/oWOawsc1OkF6NssNikyAI3DMjjbJGC9/v972bzm51krO9irQxEej8TpxK4Xg8dW17s2jrcrtQ50n3Wu3QoaRGDmVThoBz7SZczSc3Xd5f1kAy2/i/b1yIB7PRpCQTeucdGJ5+lntmP8TOWVcRZtJSurKBD//8I2s/zEalUZAwLITWJhuuHn4Yqw5XY3O6uXhkNIBXNy85gZdfUt+GUasi2KAmKz4Il1vkQB9lRyxtMKMQwC5IYaIH6g50igKaOSQSlUIgPkTPOamhp3SNCSmhzM6I5D9r8057M5ZHRolo97KVSgVz7hhOWKI/F7WpCTa5GJcUglIhnLSOL4oiaz44QluTjTl3DO/xexueEOCN1Nlds5taSy2FLZ1vaCWH6wlszsPYXMf9oSa+3F3W5bOOSpHkod7q+Guya9CqFJyTGubz+JRBYWwvajjl2c3xFNe3MeOf63i/F5F5RfVtCALEBUvf6z3Li6ktMTH80mSaXG7WlUkz4OZaM602J//3+T7+8OV+XO3a/0DSFwa/DOgYrxYHdLGGgiCMBN4G5omieOb3QvvAk77YYXXSYLZ79fuOzBwSweBIf97e6Ntry9tVg8PqOqnY+05jaPfwe5NAray1jLhKBy69Bk1iIilBKazJVCA4nDR//z1bC+r5y6KDvdoEsrdhAwtWtyLodCR/9SXxr75KxAMPkHjFPKafP57N9jR+nPgU7j1xBEX5cen9WVz9yDhp6i9CW1P3Bm3x/gqiA3WMTpCiXDxa54m+7MXtmSwFQSArIUgaZx/p+MUNZmKC9JS2FgHQYm+hxHQsdjrQoOahC4bwl7kZKBQnJ8t15E8XDumTzVi1JSa0BhUBYXrvc2qNksFXpVCvELGsrUJsdTI8NvCkdfx9q0sp3FfHOZenEZUc2GPbiER/WuqsWNscLC9aDkC5qRynW9KnWxttmGqthDVIYalzKveiUgj8e01up37CEwIQFEKvDf7a7BompYai1/hOMT51UBh2p5vthSeWF3vDq2vycLhEdvci/XVJvZnoAB06tZK6smMRTuedn8SI2EA+3VOG3l9NUXELl/x7E9/uLefBWek8OCudVpvzhDPdvqQvDP4OYJAgCMmCIGiAa4HvOjYQBCEB+Bq4URTFgd2GeBJoO5Q5bDLbCTZ0NfgKhcCcYZFkV5l8xjwf3lRBcJSB6NSefzjd4YnS+XZn+QlDQPOb8kmuFhEGpSAoFPip/bCkRFMRbWTH6x9w7Ztb+XBrMW+s7zkkrMlsJ718KaMLRCLvvRdVWGcv6q5pKaQoY9BZ6xGUbzP/wVHEZ4QgCAL+IToATPW+N740Wxysz6ll7ohor+H0TH1PtHBb0iHyIcyoJSHE0GeROsX10s2kqKWI25e5GJPrZn/t/k5t7piWwuz2tMmnSkq4kfmjYvl2b8Vp7SOoLTERFu/fRb9ucDhZ5GdHdIjk765lUkooe0ubep0ao6qgmS1f55OSFc7I8068tObR8auKmlhZvBKdUodTdFLZKuUQKjsqGdzgpqM404fiXLuaG0dH8vWe8k5JCNVaJaGxflQVnHjGVlDbSlG92aec42FCcigapaJPUnCU1Jv5ek85ggCHKk58QypqD8mUNqsdRuunZtq1kix2/YQEcqpbaVaK7DhQg9nu5JM7JvLArEHEBks371NN23IqnLbBF0XRCdwLLAeOAJ+LonhIEIS7BEG4q73ZY0Ao8B9BEPYKgrDzdK/bH3gkHZvFSUObw6fBB+lH7HKLXaap9eWtVBe2nNJirQfPou3ugoYTLkLlN+SSVA0BwzPZVdzAvZ/spqI2kB+GqEioL+XVcX5cPjqW9Udre9yQs+rQLm7bWElzVDChv7qxa4MmO36iAkPjZkKL9ncyXF6D303q3BWHqnC4RC7OPDbj8dOqCDNqe5R0XG6RskZLpzj5UQlBfWbwpbTIBqpLspm9R2TedoEDdQf6pO/jyYwLpNXmpOoUd4O6nG7qyluJSOga8VVrstGsFAmIMlB8qJ5JqaE43SI7e6HjW1rtLH/rIMYQLectGNKr76wnRcjew9k0WBu4Mv1KAIpN0mJ+WXYjKsGOxlyN+ra7cLe2chPl7V5+Zy0/KjmQ6qKWE4ZTrmkPx5wxuHuDr9coGZsU3Cc6/mtr81AqBG6cmEhJg/nEa03tjsnuZZ7NaoO9mXEvzYzBqFWR22YlUqFk6QPTmJgiSYQxgdJvp/LnZPABRFFcIopiuiiKqaIoPt3+3BuiKL7R/v/bRVEMFkUxq/1vbF9ct6/xFBl3tGv4Id1omSnhkiSRX9vZQ83dUY1CITB44ql7hUqVgAioRcir6Tn8rSZnPzoHkDqU697cxsbcOoaGDmL7KBuCRsPYQxuZOyKaNruLbQXdT3WrP/wnUU0Q9tAjCOqur7lwXx0IUK6pI6XcRY25xnvMGCKlJejO4C/eX0l8iJ7MuM4znqQThGZWtVixu9wkhvh5n8uKD6KqxUpl8+nFNJusDhra7CSE+CEclCac6WUucov3nFa/3ZEaYQQgr+bUiqQ3VLThdkoLtsdT0yJJaYnDQ6nMayIryh+VQjihrCO6RVa9ewSzyc6cO4ajNfRuvUnnpyYgXE9BbgV6lZ4bht4AQElLCaIoUnakAX9LKcUBkYScOwVVRASsWsavJibyzZ6yTpvnIlMCcFhdNFb1PNNbe7SGQRFG4n3smejI1EHhZFeZqDmNNAulDWa+2l3G9eMTvDeYIz14+Sarg/o2OwkhBvatLiUlK5yUrGNrj35aFc9eMYIxw8LROEQCNcc2YUZ5DH7TwMXon3U7bV1ukfmvbe6UFqC3aDpF6fiWdEDy8EHKUtiRmhITIbF+XeKXT4YdRY04EFFzYgPhypaMVWl4AnaXm9dvGM2NYybQrHWgmH4OzYsXMynOiE6t6DaxlqmsiHPWHmZ7ajBpF17ss03h/joiUwLJD47AaIXSw9u8x1RqJfoAjU+D39hmZ3NeHXNHdJ3xJJwgNNMTRZMYasDVIv3gRrWvAfS0m9RucfL5Mzt6lAo8m77iQjSE5EkSgMINfruOnnb6CF+knabB9yzY+jL4ta02AvVqUkeG4XaJ1BeYyIoPOuHC7e4VxZQcqmfKlYOISAw4qfGEJfjhrFYxPX46scZYDCoDJaYSmqrNtDXbCa3cS35gLMH+OgLmzqV1wwbuzJQyW/579TEt37Ne0JOO32pzsr2woUc5x4M3PPM0dji/tjYPhSBw17mpDIuR3pfDld2Pz/MdjhaV2MzOTjuTPVw8MoYpo6JBhJb6Y8Y9MkCHIPwMPfyfEkqFQHWLlexTSBWrUitQKATMbQ5sTrfPRVsAy39f56Uf/0NBhwRZoihSVyrprKfD5zvLcCog1qgjr7Z7A+FyuzAUVuNSKcjWSF/0QZH+pAamAlB93gjcLS041q1lSlo4q4/U+NSQDz35EAoRtsy53ud1Wuot1Je1kpoZjjpjPACNu7Z1auMfovOp4S87VIXTLXqjczqSFOpHVYu126gKj9wTc3QPORMm0rhwIRnRAWhUCvb0EI9fVdBMbYmJvJ013bbx9K3VNTGozIVlaCKukABG5Tg52tD3FYvCjVoCdKrTMvganZLAcH2XYzUtUgx+VGogGp2S4oOSrHOwvBlTN1KEqcHKtu8KSRsTwfBzT24nOIA1uAk/WzAzI+YgCAIJAQmUtJRQekSSkcKr91EZFo9aqSDw0kvA6USzeS03Tkxk0d5yr6MUGKFHa1BR3cPNeVNuLQ6X2CmdQndkRAcQ6qc5ZVmntMHMl7vKuHZ8PFGBOsL9tYQZNT3q+B7nQd8kvdex6UE+23k+u+YOsfhqpYJwo/a0Z6wnw1ln8AHigw292rp/PIIgoNYrMbV2TqvQEWdjI/XvvsvgmgIUe3Z4n29rsmMxOQiPN57yuE1WqYSeRqskVKcmv6atW32zrLWM+EonjsQochssBBvUhBk1pAZJBv9wogJ1XBxNX33FrKERlDdZutwEzTt2ELhhP4vGGsnImO3zOkX7JU8xOTOMsVPOxaKB1j2dUyz4h+hobezqGS/eX0FymJ/XU+qIZzG2u8+puMGMSiGgWrUURJGqJ/+Gbf1ahsUE9Fg4vGKXFD1VdrD7QiSe+H6nq4yUKtBkjcQwbSpZBSIHK/d2e96J2FC2gRVFK7o8LwgCaRHGLgbf5rJ5Fzt7oqbE5I2/P57aVhsR/lqUSgVxQ0MoOVTPxOQQXG6RHUW+Zbyj26oQ3SIT56d2mXm12pwn1NQPti/BpTmGAZDgn0CJqYSy7AaMfqC31tMQkwSAdsgQtIPSaP7ue359bipaldIbly8IApHJgd5Uyb5Se684XE2ATtWrPEYKhcDktDA25tad0gL5f9bloxAEfjM91Tu+jJhADvdg8D2ypL3CQmCYDr9A306iJ7rq+BQL0YE62cM/XeJDDKe8g02jU2FubU+c5kPSafz0U0SLBbvOQOaO5d4vVl2pZExPx8P/YX8lFocLf6MGo1KJxeGivBt9L78xj+QqEc3QIeRUtzIoUorgMKgNxPjFkN9SQODll2HeupVzjdIXalWHEnyi00nRE3+hNgA+HzSDEfFBPq9TtL+WoEgDQZEGZg6LJT9Ci19u552V/iFaTA3WTj+yWpONLfn1XDwyupNR2V65nYc3Pkx8+2Jvd7JOSb2ZZH8lbevXE3DpJeiGD6f8/37HLFc1B8qbvYnNOtK2fTslP0i7jOtrut/KXtJgJtigpvXQTtQuCJ8wlfDZF2GwQc2P63yecyJWFa/i/jX389z253weT4swdpEA3z7wNpcsuoSK1u4rJLlcUpKx8AR/CpoL+OOGP9LmOKZ515ishPtL6yiJw0NpbbSRrNGgUSp86viiKHJ0axXRaYFdZgwtVgdTnlvDy6tzu5znweFysNq8GIDGUul7lRCQQEVLJeU5jYRrmxARMMclA5LRDLj4Eiy7dxPQVMs14+L5YX+lN/V2VEoADZVtPLHub8z/dj5217GNWHanm1WHq5mVEYla2TtTNXVQGHWtNm+q795S3mThy12lXDMunujAY+/LsJgAcmtM3W7AKqk3E+6noTq/mYDSXRTOm4e9uGs6Cb2/GrVWSXPd8QZfLxv80yUhxNCjXNATGp0Ki1kyFMdvp3dbrTR++BHGc8+l5vz5jKo4QvURyVupLZUSW4XFnbqH//nOUtIijAT4adC3f8G7k3XK8vfib4WQkWPJqTaRHnnsuqlBqeQ35xN02WWgVNJ0xXw+XvMs8c89TNVTT9Pw8cfUvvwyQl4xH8/U0NY2nuExXcNIbRYn5TlNJI+UJKPIAB3FMcFEV7Xhth3z6P1DdbgcbiymYxLCsoOVuEVJv/TgcDt4cuuTLC5YTI1DCoEs7mbhtrihjXOb8xHNZlYNc+P/0jOoo6I49/1nCWuo6lLdqWXFCkpvvwOTfwI6dysgUJXvWyooqTeTEOqHc580UwkdOwm/SZNwqhXotx3yeU5PbCrfxB82/AGNUkONpYZac9fQwLQII3WtdprMxwzanpo92Fw2Xtv7Wrd9N1a24XK6CU/w55mtz7C0cCkri1cCkvGuNdlItjXQ+NnnxKVIs6aq7CZGJfjW8WuKTDRVmxkysavM9tWuMprMDj7YUtTtb2dL5Rbq3bVoQo6tLST4JxBsisJucRHSnENtYATG4GOzusCL5wLQsngx87JisLvc3roIkckBIMKW/XspainyvjaQEsG1WJ1cOLzrWLvj/IxINCoFn+8sPXHjDvxnrfQ79nj3HjKiA3C4RHK6qW9QVN9GhkGP3eLEv3A7trx8Cq+6mtaNGzu1EwSBgHB9l/QKUYG6n1dY5k+RhFA9oki33nFPaPRKbO3eR9BxHn7zN9/gamyEX11G3QVZuAWBqg+kNAZ1pa0EhutPKhVyR/JqTOwuaeLqsXGotEq0ouQV53ej+7Yc3AeAK2UYVouTmPUNFO2XtMu0oDQKmwsRIsNJ/PBDwu65B3t6BormJhq//prqvz1F/VtvcyhJSVHGZCKNQV4vsSMlh+pxu0SSMo/F5VvSUlC5oWLnsYiW40Mz3W6R97cUkxHlT5of2IuKMO/ezeoPnyFtfSFTsgVWlH5PgE7VbaROSb2ZsQW7sPvreMG9jPv3/IWw119BpVbz1I9vcXDfsRC/xk8/pfyB3yIMG4NV5U+NegOC20XZbt8/+uL2PPiG7BIaw7SowsJQ6PW0jExiyOEWGi2939y1o2oHv137WwYFDeL5ac8DcLj+cJd2xy/ciqLI4brDaJVavs//npxG39tTPGkMijTZbKvahkJQsKxwGW6rleqvv+Xxta8x5+/3UvXXv9L0lz8QGuvn1fEPVbR0usEAHN1aiVKtIHVMZ03c7Rb5cEsxIX4aGs2ObmvzLi9ajr/an/iUcGqKJS86ISCB2GYp7tw/bwuFQTGdclCpY2MxjB1L83ffkxkXSFywnu/3SbOaQq30Xp2jnEmCfwKfHf3Me96yg1X4aZTexdjeEGTQcNHwKBbtKe91Go6KJguf7yzl6rHxxAQd8+5rS0wMaQ/Q6G7htrjeTIpbiu4Lbsol/q23UMfEUHrnr6l7861Os97AcH0nDR8gJkhHq83Z7XpLX3N2GvyQnvXhntDoVTjaUyR39PBFl4v6d95FlzmSv1sX8WLVU2yMHYFq+WLcbW3Ulpp6XcbQF1/sKkOpELhsVBxqrRK3002on6bbhT5FThFuAQoCowlwC4gWF+U5kqFKDUrF4XZQairFMHoU4ffeQ9Tzz3P/jAfZ9/rXpK1fT+5TC/jnfLA1TPTp3QMU7a9D56f2prMFiJo4EYA9K49p1f6hnTdfrc6uYejWZTz/9r3kjBtP/gUXUnz9DST+fSG/Xubm/m8cFG5fTXy426ek02S2Y22zEHd0N1vTISk4lUP1h3i44CVi//saAQ4zcf94FJfJRO0rr1D1xJMYzz2X3Fukhef90bn4m4opP9i1Nq3D5aaiyUpisJ6ogmaa0495j4bp5xLRDEd2Lu/2cwIwrVlDy7Ll7K/dz72r7yXOGMd/z/8v46LGISD4Nvjh0nfD83mWmkoxOUzcnXU3Ro2Rl3e/7PNatSUm1Dolrxa+QFJAEvf4zWXIexvJmTqNxkcfJsLcRO01txLxp4do27SJkMrdVOU3My42CFGEbR12nrqcbnJ2VpOSGebdZOhhc34dBXVt/HnuUFLC/fjARxpwu8vO2pK1nJdwHlFJgbQ22jC32EkMSCS2OR1liB1FcQ5HjdGEHBepFnDJJdgLCrAdPsLFI2PYnFfHvqocHt72EG3GBrLEiVw9+Gr21OzhaMNRXG6RlYervKUMe4vFaSEyZgcmu5XF3aQ/OZ7X10kbE++ecaxIy65lRXz+zA5qfqzGoFH61PGtDheVzVZCzCJ+mDCG+eE3+RySPv2EgAsvpPaFFyh/8P/YU7SFguYCAsP1tNRbOq2RRLXLRwMl65yVBt8Tr3sqOr5Gp8JpcyMIENghcZpp5UocpaXoFlzLlsqtWJxmlo9IQmkxU/Pld5jqrYSd4oKtw+Xmq13lzBgcQbi/FrVGgdPmIjXCSK4Pg+9yuwgsbqAtOoicJgcGtzQbaKqWXq9n4bZjUfMhUf7EBulZlV2LKiKcd9XbiY/NoKQqlGGxXQ2+y+Wm+GA9SSNCO6UWGJc1jnp/MO8/5uEbgzt7+G+uzeWa/PXoUlOIeOghYv7xHAcens8fb1Fi/+hfYNBz4VYH6sA9Pg1+cb2ZsdXZKG1W1g+y8/CEh/nzxD+zsXwjz7V8yQ9X3E9QdSn5F11E3X9eJ/Dyyyl55Dq+3rESERHV8CB0tkLqWlRdUlSUN1pwuUVi7eUEtomIw48lCku96BoAGlZ3XXj1flaVlZQ/+H+U//a3bH7gRiLUIbw5+02CdcEY1AaSA5N9GvzYYD1alcJr8A/VS9LROTHncOvwW9lQtoGdVV33I9aWmBBDLVTUFfDE+gimPvoNM/a6aRyVROszL3Pb+Q+huvEWQm++mchHHsa4awlut0iIyYVW1VnHLz5Yj63NyWAfcs4HW4oJ9dMwd2Q0N05MZF9pU5eUHD9W/IjJYWJO0hxvTp+a4hYCFUFEm5JBK82ocgI6e/gAARfMQVCrafn+ey4eGY0TM/evfQC1Us3QQXFUH6ljyvNruGGDwLovXmDHwWLqWu1cMPzk9rSs//51Lrz7RcZpV7Nwx4llnfImC5/tKOXKMfHEBukRRZEti/LZuqgAlUZB7o5qhkb5+zT4pQ1mBBGU9XaCag/jN3kygiCg0OuJ+dc/Udx7C83Ll1Fz4608v+j3BIbrcTvFTmlIBnrz1Vlp8MONWnRqxQmTc/lCo1MiOlwEdkicJooi9W//D3ViAhuTbThFJ0pBSfUQE5VRyZQsWi9d9xQ9/PVHa6lrtXH1WGlru6q9kPmg9siO4yMOylrLSKxy4U5PIq+mlUiNdGNqbDf4KYEpAJ3KHQqCwKyhEWzKq2VV8TrymvKYGnEZbhGG+4iiqcprxmZ2dpJzAJICksiLEYgoLfHu3tUaVKh1SlobrOwsasC1azuhbY2E3/VrQm+5GdfsKbygXkvq+Flkjr2IkKuuYnK2iKtlLeVN5i4LsMUNZqaW76PNT0nb8GQmRE3gqvSruDvzbr7N/5a8rBJeGHUNroZGQu+8k9J7L+GBDf9Hom0wQZF6xiWMocqQi4iCqrzOkSqeWV9gsZSlMHDseO+xgNhEyuJ0GLZ2X+il9qWXEEU368brmb7bzr8WagluPnZTyQjN8BrzjigVAinhRu+azKG6Q2gUUlTVDUNvIEIfwYu7X+z0WbtdburKTJS0beeV99Xol28h5I7beebRFN66wkhl6nBEQeGV40IWLCDt5rmonGbyP1nDuMRgtnbQ8Y9urUIfoCF+aOeIl/ImC6uPVHPNuHi0KiVXjInDoFHywZbOXv6yomUEaAKYGDNRCk4QpBtSVUELSlGN2CqtyxQExnRZ/1IGBmKcfi7NS35gcJiWkOTPaWmr4JXa2fh9+zF2lwpzk4tLtjiZ9uIGAq65iDfW/JPML/5L66bN3X4exyN8uQStE+YcWs+ukuputXcPL67MIba1hruHGnA7nGz8PJfdy4rJmBrDeQuG0tZsJ0ur53Bl1x3BxfVmIlwCot1NYO0h/KZMAaDOUseTW5/k6oCPefEGI+GtSmZ/mI02WDK3zTXH7NJAb7466wy+q7WNsnvu5eqaPZQ2npqkIzjETh6KefsOrAcPEnrLrSwpWUZyYDLT4qbh1B1kWfpUGk3SlPNUI3Q+31lKmFHjjTVWa5Q4bS7SIow0WxzUHZdCtrBoL6EmMA4bTk61iQSD9INvqbPicroxqA3EGmM7efgAM4dGYnW4eHnn68T4xWB0jgNgRFxXD7/wQB0KlUD80JBOzwfpgiiK1RFlMrNtj7S5zZNTx9Rg5Y31+VxcthNFUBDGmTMBeHP/m1idVu4ffT8AwTcuQEBgyrZyRE0J5Y2dv+xllQ1MqD7Ej4PcXD3sOm+Uz12Zd3HFoCvY3fIVGzNtVH/yAyXXT+W+tfcT7x9PvDWNqKQgsiKyOBhbAKKbks2dtXFPSKY+9wBmDcSPPKfT8caxaUQWmXD4SM1tOXiI5m+/Y/05/iyc64/muT8jFpdRePkVtLXXIBgWOoxaS223C7cdPfwhIUNQK9ToVXp+k/Ub9tfuZ03pGm/7hnITLofIjK15BCuNJH7wPpG/+x3nZsxlZ9VOChskySLCX+c9J/yuXxMVZKOyWc+v9n5LdmUL9a02rK0Oig7UkT4+EsVxES+eKm43TEwEIECnZv6oWL7fV0Fje10Iq9PK2pK1zEqchVqhRqNTERxpoKbYRFl2I6Lgxq98D2JIKI26gC6SDkDAxZfgqq3j048fZnzuIV58Q4Xm1Y+IiJbaah7+J6z4kCeuV/DluGGIYeGYly6h9PbbseV2HznkwVlXR+yecqwagQmH7ETp1/Hp9u6LiWdXtbB/9RZeW/EcpssuYdHVz3NgbRkp4lGGFH5N4OFVqLVKoptctNqcXexJUX0b8c52/b4lH8XYTN7Y9wYXfX0Ri3IXcf2Q6/nn71fivvZihpSJVDZJ624ddfyB3nx11hl8hZ8BR3k552VvOEUPX4VChOAOi6/1/3sbZWgo1vMnsrt6NxcmX8iM+BnYaOC7yCDaQlPRYcEQcPI7bGtNNtZk13D56Dhv6Jnk4btJbU/hcLyOX7tvOwBRWeeQW33MwxfdIi3tYV+eSJ2OTEgJwRhYTHHbEW4ZfgtHKtoI9dMQFaDr1E4URQr31RE3OMTnIrR1sDQTObz6R+9z/iE66qrNbN9byISKAwTNuxSFRkOpqZTPjn7GZYMu8848NHGxGM6fyay9IgGGLV0Wbt3btqB3Otg9TMelqZd6nxcEgT9P/DNTY89FG/k9/8t7n3tW30O0XzT/nvA6lhYnNSo3/15iY2+KFWNbBeVHOm/CKalvQ6NSoD9aQH6sgtiAzoXJDdPPRQGULF/U5T2pee45XIFG3hnVzB/H/ZHUeTeQ9OUXKENDKLntdureeouMkKHSe+NTxzdS3mShzebgcP1hMkKPFYabnzafpIAkXtn9Ck63E0dlJQf+8k8AzAk20r9bjGGcdIO+IOkCRET2NKxHo1IQ0EGPFwSBQZdNwq4NImTrDq4/upKFO0rJ3VmN2yUy5Li0H1aHi4U7Spk1NJLYDguWCyYlYnO6vdEum8s3Y3aamZM0x9smPNGf2uIWyrIbECMshJc1YE2UPuPjJR0A4/RzcfvpyHxhGQ9856ZVEcTR3z1Fxlv/RK1VUl3QzLC4MViGD2PhlHranvgnkV8uxu4fTsMHH3Tp73gqPv8YpRuK/m8+KjdcXLKer/bkdxtx9PyyoyzIWQWBweRf9S8qIycwWJFNes1KWr7+ivpnniJGUYGzpA2l2DWRWnG9mWRRiZ+ricAhidz64328tvc1psROYdH8RTw0/iGCdEEkzbsOgIaN36JQCt7fKAz85quzzuALgkDQNVcTWV2MJu/oSW/A0LSnSA7TSUbUevQobRs2EnLjr1hRtQ4RkYuSL2Jq3FQEBNyBObREDMavPh976cmFggEs2lOO0y1y1ZhjmQrVGiWiWyQl1GPwO09LbYelgiP26FjmO5cwov5YHdvG5f+D+nxSg1Ipai7ypq0F0KqUBMduBJc/81Lnc7CihWGxgV023zRWmWmptZCc6Ts6QjtsKG4BWvbs876//iE6muoszK7YjcLpJPCKKwB4dc+rqAQVv8n8Tac+Im+7A4MNZhftJa+uc/hg+O51tOghZcYl+Gs6z5pUChX/mv48Wlcye1u/INwQztuz32b/Punm/tq+UnYU2LGrI1GLpdS36XB1kIyK680MMirwL2mgJjUYpaLzgmDauFnUBUDD6s4Lt61r1mDesYNl5wUQFp7gNXza5GSSP/uMgAvmUPuvFwh76XMUgsKnrJMWYUQUYVPxEcxOM8OCh2Les4eWFSswffYFfz40iBmf5bLrtmspmDefxiYFbqyMf+1llIHHZmEpQSkMDh5MXtsmwo3aLp9f4nApOVfb1Ku4MXsFha/9lx3rSgmNMxIW1/n9XHKgkoY2OwsmJXV6fkhUAOOTQvhoWzEut8jyouUEa4MZH3VMAotICKCt2U5NsYnAJIG4OpHaaGmW6qtCmEKrZdvEYFoC1UQ++wyvXvsXPnZJWVQjkgIo3F/HyncOcc72BSw49Buq383j42cPsWXsX2j4finO+u7TRYiiSPOXX3E4HpIvuhr3pNHM3G3DqVnLMh+L99sLGyjZsovRFYc5Ou0hSmq1nHNFGrP+czep33xN+q6dBFx8MSH7vsdlczPIpeRQRecw3+L6NuIcAoHVBykfGsaRhiM8NfkpXpj+AokBid52YUMyqY7UYNi8h4CwrpE6A7n56qwz+ACBl16KS6tjeu4mGntRcLkjHo82pD2JWMM77yAYDARfey1LC5eSEZpBYkAiYfow0gKHoTXk0ubU499WTuPHn5zUtURR5POdpYxKCGJQhzqdnhTJoVo1Ru1xW/LdbjS5xTQHKoj6ZDJ/U7+Hw+1PgFFaCGo6uAf+PZrUA9/hcDsoacgDUYS2Og4c+IQm8RCTGvywfriA+XVvMi24awiiJ7wzaYTvwh9xEWmUhENMZS5H2zVS0U+J0ikyv+YgupEj0aWnc7j+MEsKl3Bjxo1EGDqHAepHjsQ1Ip25uxxsKVt97OVZraTn72PbYIFrMnyne9Cr9EwPeggaZ/GHkS/yf58W8PGSXNzAb68cxsvXZmExxVLml4dLUFN96Fi0RkmDmfHWShQi2Icmd+l7UPAg9g1So9t11LvXQHQ4qHn+n7gSovgwvZrbht+GSnHMq1b4+RHzr38RevtttC36jtlVET49/EHteyW2lu8FYMibayi+7nrK73+AqieeJOCjpUw9qqAt7yjWEakcTE5AEe0myr/rwuUFyRfQLOYRFNB1Ud8vUEtYvJHG2DEYZs/m2oIdWKosRIwI6dL2gy3FpIT7MTmt62d946REShssfLB7AyuLVzInaU6n1+1JlQyQEOBE5Yai9lmpL4Nvd9n5z6QWtrz0K0LmX8bczFh2FDVS2WwheWQYZpOdyvxmzFYdVf4lNA7OZejkaFyiErMigMaFC7v06cG8bTuqilrWj9JIv9Hb7ybIDOfVrOOj7dmd2oqiyLNLj3Br/mpM0cOoaDEy6bJURp1/rLKZIAgEXX0VQRV70WvdjEXbZeG2paINlVsguCmHD/0PkhGa0WlG2pG6CenE5DdjDFD4MPj6AYvFPysNvtJoxDp1JtPL9lBS4jtpWHd4EqgFalQ4Kipo/mEJwVddSZmimUP1h7go+SJv2/MTZxDhFkGEiLRQmr76CnfbiYtze9hV3EhuTStXj+0sK3gMvtPuJjXi2EIf9fm4/j2KsHIzllAXR2IuY67tGcxBYwhKjEEfoKFx8L0w8zHSWqXFyvwPL4LnkuD5VN7e+Bf8XW6ebd2DteIwtykWc/u+q+GdC2DPx2CXxl64r47wBH+MygbY+wl8dQf8Mx3eOg/q8kgMSCQvRmBwYymrD0ne0/ZqyfvRNpsJavfuX9r1EkHaIG4ZfovP159w571ENIPfnkXe5xrWrkPndHF0RCKDQ3yX2gOYkBCPqWoWN72Vw4HyZqaEBhAa7cdVExO4NDOGOMNQdsVLP/TitZK3LYoiJQ1mhtQX4AZ0I0d26VepUNIwNhWV3Yl561YAGhd+hr2oiK9n+xNujPL5oxYEgbD770edmMAVPzSRXd114Tcp1A+lQuBQ3WHGlKoRf1hN8PXXkfzN16RtWM+QA/txL3mXB26HO6bnE2iPI2Nwis/X75VW/Pb5PJ44LJSqQhORT/6dmrFXguhm7ZJFnWos7C9rYm9pEwsmJiIIAqLDQfN332Evk3ZSzxkWRXiAm1cPPk6EIYJ7R93b6RphcUYEQfq+Jlulzz87BLQqBQYfhUoO1x/G5rIxOmI0gDdl9g/7K8mcGc9d/57O2HuG877ORtsEE18Gv070WEluFMedS+Mnn3ba8NeRpi++wKJX0jZ5JBqlBr9zzkFMiuPCXVb2t3zfaZfzisPVmPYdIKvsILbpVwEw9JyukUuGcePQJiUS1XKIqDaR3LJjBt/hcqNtkJxJo62YrcF1PDDqgW5TTPvPPh+FCDQX01xr6aQ8RMke/unjf/XV6FwOmr/7/qTOE1XSBxagVNLw4UcgioTcdBNLC5ciIHTSMGcnzyTULEkxCZfPwG0y0fx9765ndbh4+OsDRPhruyQX85Q5dNpdpIUbya1uBUsjfHI1peZWohtBOXEmHwTdS5UhHVurA0OAmuBIA00NwNTfkXyHpK/nxWTAsMvIm/571vgZuGHo9fwm8hsmtT7HJNu/aTznUWirhW/vhn8OxvzF76kqaCLZ+jW8MBQW/Qby10DiOdBQCP+dRmJVNrkxAkaHjT1bD9BsdrC8UJoV2AKiCJh7EVsqtrClcgt3jryzsyzjdoND+nL7n3cetcFGZm0rp6BJWgDO/eojWvQQNf66Ht+/aenhDI70554Zqaz/w3S0JieRSdJ1BEHggSmzOBJnQmeppSKvCYC6Vjtmu4uo8sOUhUN8zBCffQdMnIRVDc1rVuNqaaHutddwjc7gi7ACbhl+C2qQPg+nXZo9taPQaIh69FECq1uZsLG2UxppAI1KQWKIgZrmbO5c5kadmEDEH/+IbuhQ1BERCGo146LGMTV2KrrWAFRuNTFJvnPIxPvHI9gSaFZs93k8YXgoolukNM9EbUgWwc4Krln/Dq//9Q1vtMkHW4oxaJRcPiYO8549FF55FRV/fIiSBQtwVNegVgpEpX6PjQYezHqCQG3nxX2NTkV4gj/xQ0PQFpZiVUOuQVoX8mX49tbsBSArIguA5DA/hscGsLh9k5cgCCw7WIlCgHvH3YhbdLOmqT1Edvx5uOrraVn8Q5d+nY2NtKxYwYYMGBE3xttX9M23k1INI80b+HCbJIM6XW7+sSybOwrXoAgIoNE/hZAYP/T+XWckgiAQdNVVhB74AUGEkAant1RlRZOFWIcCvbWWnBgTo6PHMSlmks/PAiBj/IVUBoNQvB+H1YW19ZjyMJCbr85ag58wcQw5QXEYln93Ujq+pb0cr1GpwLRmNcYpU1BFR7O0cCmjI0cT5Xdsep0SmEJkaxo2pY3waWPQZWTQ8OFHiK4T7/D714qj5Na08o8rR+Kv65ykzVvIvD1Sp8HUhnPhAmgspjT+WgCCM8eRU2NiUIQfFpMdQ4CGoCiDNxbfoDVKkTpRQ+CSl/if0izlLx91D+dlSJ6VTRdO0Pl/gHt3wi1LYeglFO8uBQSSouvg/L/BXZvg97lw1XvS/6MzSVj+GHkx7SGrhw/x8upcGm3SdYXRk1Eajbx/+H0iDBFck3YFVOyBLa/Bwhvg+VT4exx8dQdCxS6Kps9jUCWs++EN3FYr+u172TZIw+xBc+iJqEAdyx+cxh/mDEFpldI6hCccCy+9cPAoFAoddlcpdRYjbpebkoY2BNFNcGE+R2MFkv0ToXw3bHoRdn8ITVJEx/CY0exLFmhes5q619/A1dzMwvN1hGj8ufzoJvhHqjRreiocngyFZ+Lg+UHw0kiM+c/hGh7BlZvcZB9e32XcKeF6zt+cT3CdjejHH0eh03Vp89ikx7g18m6ATq+pI3anG0vTCJrdRRS3dN0kFZUcgNagYvv3hbQ22Rlz63TaBmVwwbev8+mLH9HYZue7fRVcMziQtmeeovi663E1NxP58J9wNTVReuedfLXnQ4qsW3DWzWHXUd/juPi+TGbeNBRrdjbVMXqaXJXdZpndXbPbK4d6zx8Zw97SJu+emaUHqxiXFEJmVCqTYyfzVclnqHVKrH4RaAcPpuH997v8nlu++w4cDlZmwejI0d7nA+ddCv5G5u628k3Bp9icLr7cVYYiJ5sRJQcIuvkWqopaiR3cfWK2wMvm42+rQq8wM9Su9O64LaptI8EpEFKfzY5EBw+M7t67B4jzj+PAMD8C86VsrB1lnYHcfNUnBl8QhAsEQTgqCEKeIAh/8nFcEAThlfbj+wVBGO2rn75Er1GycchUjBXFWPb0vrCF2S0t8PmZTDiKSzBMmkhOYw4FzQWd5Bxo9yIsqdQZSrE4LYTefhv2/Hyav/3OV9dethbU8/amQm6YkMB0H1V81N5i6i4GhfvxuOp9VMUb4JKXaWkviJww5lzyqlsZHGLE7RIxBGgJjjRgbXN4vYe0oDTym/IpNZWytHApV6dfTZAuiJlDIwEY7lmwFQTJg7/sdQrj/4QxWEPYXW/C5PshagQo2r8mgbFw0/fop/4BV6ALhxomNB7inc2FXO8uQHC7cCWkUr33Q7aU/8g8ixPNP9Phzemw/BGoPgSDL4IxN0HOMvjf+cwN/o42nYDui+UUr1iE1u5iY9wwBlMN+z6DZY/AZ7+Cne9KXrUPPOkHIjpoyiqFimFhwygLLMSp0lO19QglDWZGmPJRW6zkxAokvT8f3poBqx6H7+6Fl0bAy1kM3/sFOwcJCLUNNLz3HuLoCL5V7OfG6jL0h7+DIRfBnGfgvL/AlAdh9I3Sc/ETwN5KfMphFG5wPfEovH8JrH8eDn4F2/7LpeUvcuk2Jy0jDPjtfwheHQfvXwqL7oY1T8POd4mqOECqKR6VVklQlO+iH3WtNpwtkiS1rHBZl+MKpYL4oSE0VZvR6JSkjotlzCfv0hCbzIj/PcdLz37IlIIdXP3v39H01VeE3HILqZ+/S0hKHbEPXoEtPw/Fn55jWvgkzou6ms93lvlMVaA3alBrFdiOZNOaFI5ZrPap34uiyJ6aPYyKGNXp+bkjpNnt4v2V5NW0klvTyoXtm62uHXwttdZaFAFOWuqshNx0E7acHMztIbCefhu/+IKWtEhKIgQywzOPvQd6PaHXXsv4HJFg11o+23WUF1fl8JvitSgCA3BMnYfT7iYuvXuDrwoJIWDWTKLLNpPgUnIoV/r9FeY2oEZBUFMOukkTvbOW7hAEgbYpI/Frn/V1NPgDufnq1BK/dEAQBCXwGnA+UkHzHYIgfCeKYsdVqwuBQe1/E4DX2//tV8qyJmPdvYjGhQsxjO7dPaa1PUWrvkLSMf0mTOCDwiWoBBXnJ57fqa3b5SagLZCiyD0sL9jAZRdeiP79D6h98UUC5sxG4efXpX+T1cHvPt9HQoiBR+cO9TkGT4pVc7ONUa1fEKpaTXbqrQwZdQPuV96nxaggwC8Sk+0wyf562gB9gNq74NxYbSbaGEhKUAqbKzbz1v63UAgKFgxbAEBquB+zhkYyfXDnYg2iKFJV2ErSiLDuvRWlCs57lATTHsqjDjGzaReh6laytpWyPulRrEVH+H7t/3CHBDG/1Qwjr4LEydINJaBDYfdZT8D+z9BtfgNxsJnMfXpyXnmKID08F7CaoPfap+4qPRhC4cj3sPSPkH4BZF4LaeeDSnqfaopbEBRCl8R146JGsTJlETG5V3D00y84J2kZ003FVBFEbbSIMWUWpM2ClBlgroOCdVCwnqjDSyhNNCIiolC4+TCrHH/8uHbmP2DofFB3zUvfkUBLExtbZjNznYm2nBr8Cp+S3l83xK2LxKxVEjBRDcYo6TWYqiB/LbRWSY2A2vpnCDeEomgsgNDULteoNdkQnYGkGEewrGgZv878dZc2sUMDydtVQ0ymPyqNEjT+jF34AVvnX8u1X0ghn/rMTKIeegBd3Q/w5kRwWlAIAp/Oiea6HyDr6zZqHkpgycFqvt9XwdXj2tebnDaoOggVu3GIUbjb2hAHjcEhbCPI0FW/L2wppMnW5NXvPcSHGBiVEMTi/RW42z33Oe0Gf0rsFGKNsVSpSjHU+RFw51xqXniB+vffx+8caf+EZc9e7Hn5bL8unbSg4C6yU/D111P/zjtcsNfOs7xBTMkwhhbtJ/S3D1BUagUBYgYF9fh5Bl91FRF3P0RBzPlUHayHuVBbYMIA2Mjj5vNPHDIKkDBuBq3qHSCKnUIzB3Lz1WkbfGA8kCeKYgGAIAgLgXlAR4M/D/hAlOZiWwVBCBIEIVoUxRMnAz8Vvrod4saREDGCLSnjOG/ZcpwPP4wq+MQ5tVvad48qKqpQBgaiSU9n2Te/ZWLMRIJ1nc9vrDYjuAXq9NUsL1rD5UMuJOJPD1F83fXU/+8dwu+/r0v/f1t8mMpmC1/cdQ4Gje+33y9I2kjVmneIQbmPsco9lu0ht/MIYCyqoSEhEEt75E6sTkMOYPDXYGxPYtZU3UZ0aiBpQWk43U6+yfuGq9Kv8kbKCILA2zd1rTJpbpZy+vdmA1liZBaH4/NI2gozWg9QXemHf4YNU+B4vo3bzGi/SBJu+qj7DrRGGHcbDSnX8nrx8/z24GcklLnYN1xJhXYOKRddBNGZEDoIFEpJFtr/GRz4Eo58B/pgyJgHQYnU7k8nJFiDqnAl6AJAqYHS7YzMW8r/QuvRHmqgsVlHa7yKkvoh+PvVYhw2Di5889h4jOEQMRQm/gZcTpKX38aGCfuYkT6W7yO28euRt2Mc2fO6ghd9EDVXzaB+72K0+yNJ/vh7BEstjUs2o//8Rd6aq+GS2V8zdERM5/NcTmitwt1YRu3zJjJcK+G1e2DMLXDuQ9IY26lp15Knxszi/ZwXyW3MZVDwIEC6ca8oXsGrZa8zIuBCvrB8zVfLY7kk5RJmJc4i4+P3+PG+h0idPpYhGTUISy8HpxVGXgNTf88zO5/ju+qtzK9thU27STRdwN3DZ2LbdQBq6qB8F1QdALc0k7SW6YFg/IYOg7of0eu77m7dUy3Nso/38EGSdf62+DD1rXay4oO86YmVCiVXpl/JtkO5hNYlIKjUBF93LXX/fhVbfj7a1FSavvgCwWDgy8QqZkV0rdimjo7G//zZzF63ii/O2cg9ZUUoAgMI/tWv2PRWLqGxRm/9WQ9u0c2Wii0sKVzCiLARXD7+MgLDdOisVejKpZuRvaKZUHM1rcNCegwu6MioyNEsTXcRZG+iqeLYezSQm6/6QtKJBToGoJe1P3eybfoGawtHm/JxLP0jTxZcgyGuAdFup3nRt706vcnmxIWIq7oOw/jx7K8/QEVbBRdFToC9n8Kie+DlTHh+EHWLXgWgFjV76n6UqlCNGkXARRdR/847OKo6x/+uOFTF5zvL+M301B4LOmh0KjRagdY9KxEihvFq8B/JqzXjsJgJr7LhTE3whmqGqaSbhj5AQ0CoDoVS6JJTRykou42U6UitN6f/iXMCJQUkcSjKAW5oqZ0GKhVBI4bTYDFQZK5i/qArTtgHQEywgY2G8RzMlOKW1yRfyvK4ByQvPmKoNKMQBIgdDRc+B7/Lhuu/gNSZsP8LxFVPUFPhJMK8Dj69Bt69EN6eCcsfZmSzVItXoaqkXjeYS+t/j7IRcuIUJAX5jn6R3jAVI+Kn8dp5Ll6aIO2E/dXQX/Xq9XgYHDOSd84DW24ujYuW4yCM2lffJD/dn3UpCRTW+dgUqFRBYBxNmhE4XUrC594KoxfAznfglSxY9yzYpM/Is3h4UcpsKYNmkSTr7K/dz4KlC/j9+t+jNiiYcU8K106+jBpzDY/9+BgzPp/Bv448RshtGvTO58nd/SZ56bMoWPA1hec9xOf1u/m2eit3jLyT0W/sIuSC0TTts3Dr/k+4servsO9T0PjBpLvh6g/g3l1YlcNAEAlrkHYJC5quFad21+wmRBfSKT7dw9wR0QiCVMf4wuNy51yWdhmt+gbcTmhrthF83XUIGg0NH3yIy2SiZelSxFmTaRDMjIqUbibHa/whCxagtzq5dY2bIXm5LJmg5qOCr6kqaOok57Q52vjkyCfMWzSPu1bdxcrilTy97Wku++5yas5NJ6FkPYF2qH5+PoHNNkIac8kKaYGProQvboZv75U+o4NfQ/VhaRbUgfSQdPZk6NBbamksOBY96Nl8NRChmX3h4fua+x+/StqbNlJDQbgTuBMgISHBV5MeacLNLXobg0fN4o+VLhY4VlISFkbT//5NyNxJCBE9340bzQ7cuHFYnRhSg1iy+o9oRTjv699KERm6IEiaAiottdsbUGLjD+L3POoKZP+mZxk1/DrC770T06pV1L74IjHPSQUx6lptPPz1ATKiA3hgZnr3A3C7oWgjfmIlbe4wuO5TYpfUcqCsmaIfl6MUQTd8GDnVJsKMGhQ2SQIwBGhQKBUEhutprDqWU0elUHFB0gXE+8d3f8126srabyKxvTD4gUnehVvLzl0YZ80kICYI275GDAo/Zif5rqB1PEqFQHywgR2xvyF++Do2tJ3DTaE9FKtWqiF9tvQniphqmrH+dTcRs66CrCvA2gIOM0SNJCwontivLqA5rglNxTCmVuwkvLGKFZkKhgR2jcHvyMhwSR/fVL6JBRkLCNIF9er1eBgWOoxn0wVsY4ZS+8ortK5bh+h08p9ZCvQk91jusKpQCnEMT4+D2Bdh4t2w+klY93f48d+gD+Yim8g4jUjqV0GM0yv5Yd//KD36HUttVYRpg3h8wl+Yn34FSoWS8xNm8evQcRzY9x6Ly9ezrOJHliuVENlu7Mx7YcP93uuPjhgtbZRTqIh44UOc6oeo/X4x7+ku5/fvvyTNuDpgYxDaGCshxRshPgalo2uZSI9+L9jbpJmB/pihjQrUMS4phO2FDV1y34fqQxmUkAT5UFPVRMrQKALnXUrzt9+iDvFHtFo5Olh6L0dv/4jab97nm7ybuSLyr4QqS0B0o3e70IWEcN4+cGnhQJabsnVfM89xP6uc3+IsHcuWii18m/8tbY42Rgal8/fBNzFbFcLWopW82HSQh0KKeaVOR86gK1lVMx8BPYHNuSQlBkpRbo2F0s24tQavaROUEJIC4YMh8zrUQy9Gl5WFalcDLY2dbVt0oI6KAdht2xcGvwzoaE3igOPzkvamDQCiKL4JvAkwduzYk65TFqQL4pGJj/DY5se4LyQKW8Wf+eiczdi/y8b81yn4TZ8DMx6VvEcfNJrthLptOJV6tMXPszwgimnqYPwuuFfSoiMyvIuYdSW7CPVrxixOQSnuZ+2eNxm15h9ogJC0QOq//Y5gzUp0SWEUN2i40p7Ededdj0bh42W1VMLej2HPh9BYhFHxN1oDx0JQPIMiLCw5UEnRVx8Tpobhc3/Fa59VMCjCH7PJjkIhoGsvxxgUeSxSR6/S88EFH5B8AuPmoa60lYAwnXcvQk8kBiTS6C/gCAtEXddM0BVX0KBUIIgCc8Ln4qfuun7RbV+hBo62KEm9+xmsz64hoSeD3xFBoLZcyvUSPiwVYrtGkmSGZ7Ipaj/nVUxmVq20LnM0TuCCwCQA6stb2fRFLmljIhg29dikMyM0AwEBlULFTcNu6vVrAcnDTNKlEm5OZMeMWSTUZaPNrSfq1msoDfyERNegbg2+0+5i19IigqP9CI5ufw/DBsE1H0LpDsnDdlgoKq6lxtbKoOAQLnTW8ri7inpLJb9ubuHW5lIMefdD9NsQkgpFmxCaSxipUDEyZTp/GDKXXSGxtCqVuEU3btyIoogoigiCwNTYqd4NVoJCQczTT5NfWM3cHauofOVVoh64v9MajzU7G8OEyZjP/xO6ww/jX/o+HB0Lgy8ASxO1eSsoNZVyTWMDbEgARIifCEPmSn8hyTw4K50tBfXHPvvmMijaBIUbmVNTxF7GseXLR0jRrCHEbKPJGkTt62+hDXKy2bKOSL2W6Oaj7HNdgUPUc8hwH9NG5YOgRBAUhOiKqXhvM1ETw3inupiljSPJx826ts/4as37qES4wObm+vpaRhSWAKsAmKYLYnLCJH6IjeBoynoCmo/QJAwHQBEiovz1ks4foMMCdblQlwO12VB7FCr2wtGlcMPnjIoaTbUxHyU6bE2taIMk5yo6UN+lIlp/0BcGfwcwSBCEZKAcuBY4fovkd8C97fr+BKC53/R74OKUi4n2i+b+NQ/QlPgFn8U9wrx1T9PYkohfwXo4shhGXAXT/9R5QczlJKvsY9ocg3FrtOybcyMN1au5aNrjkDir0zVEUaSurI3UMTF8JfweofVp1sW28n9TfwWWRkLH1ND0xPdUb1NiiIQw01EeVm6Erz6ExQGQMFG6gQTGSbp07nJpwS5pKsx4FL/dQ2k4Inl6aRFGlC4HwVuOUDgijKzQJPJqcrhidCyWZjt6f7W33mlwlIHig/W4XW4USgUjwkf0+n2rK+t9EfYYvxhUChU1g8NJVBswTp1KzoYVgI5zg2ad8PyOJIb6sb2wwZsqOTGk9zeLmmITCqXQ7awkMzyTJQVLmCNaEXQRiEoF+VGQ5J/EnpUlbP02H7dLpCKnidBYozf3v5/ajxnxM0gNSu2yS7g7jvxYyb41pZjqLNitLq7gd9iBvGQp3r8yoxkqYEhwBkuKWnG7xU6ppwH2rCyhpc7KvN9mdTlG/DjpD3jtg52UYmb2ddO4xGXHlvMl58XNIMrWJq13eP5yl0PcOJjxMAy+EPTBqIGJvX6HQdBoqPvjk+z582PMfuMNxLpaoh9/HEGlwtnQgLO6Gt3QDApDJmG3R1Csa5DktfChUJvNHoMOIsMZLRikqCZBIRnAFY9KfxHDmDRkLpPCU+DbVyRD3yjVJkYXxISoMezGxX5FLDcMvxKtxoBf4WbaDlcQdPU17A7bxJiocQjT/0nDh0egoJKcqmTOOfdX0mI1EDDdBcOX4X/+LFCrsf7jRyI0rSyNGMeOmj1kaIIJi0yG9DgIiofAeOnfoCSUCgWXAg2aVez76/scDhqOX1slfuO6roOh1kP0SOnPg80E71wIX9zCqEueY2HUVoY0QdXKH0m8SpoJRwXq2JR3asXXT4bTNviiKDoFQbgXWA4ogXdEUTwkCMJd7cffAJYAFwF5gBk4saB8moyJHMPHF37M3C9u5uOqx5l63jjcP2zF+exaVEc/gm3/lULlsq6Hc/8oRUosfpDLag7yse153CHRfKkrxU/tx5TYKV36N9VbsZmdhMf7k2pTsKJsMIXa7ylOm0ZiQCJKINw8kqq//pX/5NzAnqRRLLs1DWXJj1C8CYo2Q277phJjJEz+LYz6lfcGZCwqwNxSi9vlJi3CyJimzfhZ3PhffBEVzVZabU4GRfpjLmtC3yFpW1CkAbdLpKXOSlBkLz1lwG510lxrYfCE3uUfVyqUJPgnsPzyWJ4ZJ/341zeuYigXEyN21Wl7IjHUQJvdxe724uSJvfXwkSJ0QmONKNW+l6MywzNBAIN/M03WNJoTQwhAx/a3qqnMbSY5M4xzLk/j+3/vZflbB7nm0fHeRbyXz/NdlKQ79q4qwW6V8s37h+pYXPs1u8xb+fCK9/j2hT3ULrcSPDKUzKhUvrQforLF2ilhWUudhV3LikkbE0HckK5pEDpSa7J50yJrlBquH9ruY/kDYWlSdFQfEhcewP+NuprJE4fA5x/iqq0j9sUXsB6RNjTphg6hvs2GzR5JUZQOYs6XvNxhl7HHWY6u6keG3LgcFO0LpOc9Co1FkL0Esn+Ajf+UHB5dICROgfF3QvJUiBiGSqFAdWQFNnMAhyZcy7CwYYRH7EN88SXM1y+gZvl3jIqSNlw1VLShNaiwmZ3k76n1fp8FpdJbatFpd1FV5mDE9FT08/7DtF6+B8HTZqCxPY3SaSGk4Qih587v3Ylaf7h+Ibw1k5Gr/s5f4+IY0gQ1m/d7DX7HzVfH78vpS/okDl8UxSWiKKaLopgqiuLT7c+90W7sESXuaT8+QhTFrpUe+oGkoETCTb/Hn1Qej/gRHA6alqyGWY/D/XulL9X+z+CV0fC/88HSyMuue1DZLbRqtawsXslNGTehU3XdHFNX2q53xxtJCffDYZIkonWl67xtgq68AmdSChds+pI7J8WhDIqVfoiXvAz37YTf5cCtK+DBwzDrr51mG35BWkQRzC12ksP8mFG5jVYdTJr/G2+O7/RIf8zN9k5ZOoMiJe/YI+v0loaKNhBPLsVzYkAiuY4K1BERVLZWsrlFeu1tjb63v3dHUnuSuI25tagUAtGBXd9vX4iiSG2JifCE7secHpKOVqmlObYJqz6cIwnTuWzv76grbeW8BUO58K4RBEUamHPHcMwmO6veP4zoPmklEZvZQUNlGxmTY5h2bTqjzk8gdVQE+epDtGmamHlzBopWDTMrrvfmTTpe1tn8ZR6CAOdckebrEp3oaPAHgrhgPQgCOXN/RdRfH6N140aKb7mFth+lHd26IUNoaLPjtodSbanEdcGzsOBbmP4Qu63VjAzPRK04zpAFJ0mLv7f8AL/Pg99sgT8WwnWfSM932AMSGRVCkD2cz3M+B6Rw0sT33mVPm5T+enTEaERRpKGyjUHjIgkI13Nks++KV1WFLbidYo/x974QlEqqJ57LhB1PE1a+irgJWb0/OTAOrl+If1sdYdomABpyK3HbJUnSu/mqySzN+Fc+dlJj6y1n7U5bD4lB4QS23MPY8fM4mChQ9N5/cdqs4B8JFz4L9++B8XfAtD/APdupLtOicllpcDuYHDOZO0fe6bPf2lITggChsUZSw42IjhCidMmsLV3rbSMolXw+7nKizfVMO7i2ayf+kZAwQYrOOA5jcHtoZpMN0drKhKJa9g6JxGgIIq9aMhTpkUZpl22HbeHB7V5940ka/DpPhM5JFGFPCkyixFSCy+3iu/zvcCocaI1Kb6nD3uLRbXcVNxIbrEel7N3XsqVOmmV13HB1PGqFmmGhw9gXIUUJW5iBI8TEtX8Zz9Bzor1adERiAJOvGETxgXr2rjr5rKdVBS0gQnTqsThwT/rjw/WHCU3Ssz92HWFFaRjqpR95R4Nfcqiegr21jL0oyVsjuDs8xcsH0uBHBuhQKQTKGs0EX3cdca+8jC37KA3/ewdVTDTKoCAa2+yIjjCcopPKNkmxbXO0kd2Q7TMcsxN+oRCZ0WVB2ENIhJFQexRLCpbQYj+W02ZPzR6MaiNpQWm0NtpwWF2ExhrJmBxNeU6TT8en/GgjggDRJ4i/94XmknmobU3kxQxCrT5JgSRmFFz+FsOtlTiFNszKAO8NMzpAy2zFDmI+nQVf3QZ5q6X1gD7mrDf4CSEGyhocPDX5KdxXXYRfo4U3/30bjvYYYgLj4IK/w3l/Bq2R5PKDuAQrKvQ8O/XZLulzPdSVmgiK8kOtUZLSniEwXjORXdW7eG77czjdTo5UtvChI4q64WNp+u9/e0zvejyeWPy2Rhu7vvovOgdsS5oMQE61iXB/LYF6NWaTvZOkozOq0RnVJ+3h15a1ojWovDea3pAUkITD7aCirYJv879lfNR4AkMN3lKHvSUuWI9CAIdL9NYj7g2eItoRib63/XvIDM9kl2sLyWNC+THpG3TzqwkI7bp5asT0WFJHhbNlUT6V+c0+euqeyvwmBIVAZPIxgz84ZDAKQcHh+sPkNuWyLW4xmnCRXV/mE6lTew2+y+lm4+e5BIbryZp54si0ZosDu8vdqfBJf6NUCMQE6SltL1bjP2sWCe++gyIwEMMYScuub7Pjp5B2cZeYpDQV+2v34xbdXTZcnSwBYXoUNg0um8j3+cfyVe2p2UNmRCZKhZL6cun9DIn2Y8jEaASFwJEfuy4Vluc0Ep7g36Wub28YPCKNv024mZ2zrjm1FzL0YkYNmkeDoQ6LIQLTihWQu4rMpfN5U/Mibqcd84XvUHPBDyfc4Hcq/CIMfrPFQYvFydW3/gNrVBCxS3bzf+v+D5urs/RgstgYXn+EBqMNnduvx1C82tJWwtvj1Q0aFTGBOvxtc/jV0F/x0ZGP+M2q3/CfDfsxaJQMffIvuK1Wal58sdfj9nr4jTZaflhMg1Fgo2I8TpebnJpWBkUYsZmduJ1il8IrwR0idXpLXWkrYfHGHvOBHI8npvqb3G8oNZUyL20e/qE6TA0nJ+loVUpi2rXsk9Hva4tNKFQCITE9L/KODB+JXbRhnZ7P/uh1JAf5jloSBIEZC4biH6JlxdsHOyW4OhFV+c2ExRm9mU5BipJKDZIKsB+qO4Rb4WLSDYlYTQ7mWDXktxv8fatLaao2M/Wa9G7XIjq97vYY/IH08EG6MZd1qPpkGD2atNWriX7icUCKcAtSS2GVJS2Swd9TsweFoPCGuZ4qgeHS92OMfiKfH/1cyn9vayavKc97M2molLK9hsT44RekJXF4KNlbKjvVQ3DYXVQXthB7knKOh5RwI4eTM4lI72EfxwkYNfmPNOvqaDOGYPphEeIHV6C2N/F7x695Z9gnLFmXyPevHsBudZ64s5PkrDf43oLmjWYEhYLE2+4hvQLKt67lvtX3YXEemza9u/hvBJtdCJExiC5wOdw++7SY7LQ12Trp3SnhRorqLDw0/iGePOdJdlbtZE3Ln7lglIKwjHRCFiyg+auvsRw40Ktx6/zUKFUK6ivriDtYS8HoQdjcKorqzeRVm0iP9MdikqSB4w1+UKThpCQdt1ukoby1S3GME+Ex+B8d+Qg/tR+zEmbhH6KjtcHabcI6Z/sP7vj6oB5Df1IROiUthMUaUap6/hp7jM23edLmu6T2kExfaPWqk9bzXS431YUtneQcDxkhGRyuP8yh+kMEagMZNjiVsXOTiGxy4yxppbXRxo4lRSSNDPMWLjkRnl22EWfE4HeWGZRGPxQG6bOrb7UTro9Ap9R5PfzdNbsZHDwYo6b3UqEvAsIkgz8jcA4FzQXsrN7JvlopNbRHLmqsaMMQqEHnJ60VZEyJwdxip/jAsZl1VX4zbpfYY8K0nlAqBBbdM5n7Zg465dcSbYzB5W/BpgrBaRdoTbgH4d5dbNTPxrGlkeqiFmbcOMRntbnT5aw3+B6JwFO8Oujyy1D4+/NQYQbbqrbxm1W/oc3RxpqSNZRs/AaA0DRpxb+7O6xnR2p4hx2pqeF+5Ne2IYoilw26jBmBjyEobGwy/4UNZRsIu/s3KMNCqXrqKUS37xtJRwRBwC9IQ9mBw6jcEH6xFIWxIaeWNruLQZFGzC2Swdf7MPiWFjs2S+88hOYaM06Hu1c7bDsSqgvFqDZicVqYkzQHg9qAMUSH0yFlr/TFtu8L+fK5nXzw8GY2f5Xn3eyV2L5w29sYfNEtUlvSSvgJ5ByACEME0X7R7K7ZDUByQM/7EiISA5hypaTn719XdsL+60pacTrcRKcFdTmWEZpBg7WBDWUbGBY6DEEQGH1BIkKIhkmNClZ+cBjRJTLlqt4bkDPl4ccHG6g12botGdjQZifUT0t8QDwlLSU43A721+4/sX7fCwLaPfw05VD8Nf58fvRzdlfvRqVQMTxMiotvqGwjJPqYw5A4LARDoKbT4m350UYEhUB0Wtebc29JDTcSqD/1SBpBEAiLDEBAiT0skZZcJ6JSzblmFYZaO1OvHkRKVviJOzoFznqDHx8ifVE8Bl/h50fQlVcS8ONhnh/8EHtr9nLr8lt5dNOjjC/zo1ofhF+0lL61O4N/LEKns4ffapPyZbfanKzcY2CC9kmSAhK5d/W9vFu4kNAHf4t13/4TZtP0YAzW4aixUBeq5pzzLwNg6UFJk0yP9PcafIN/V4MP0FTVOy/f+3pOYsEWpC9uUkASAPNS5wF4FxxbG7vq+C6Hm+wtlUSlBBCeGMD+1aV89tR2Fv5tO/HVDozu3kk6oiiSs6Mau6XnBduOeLIoRhoiMahPfI3h58YSnRbIoY2+Iz06UpnfBODTwx8WNgyAOksdw0Kl/yuVClIuTkAjQsWRRkbNSfBKFr2hxiS9twPu4bf/lsq7SfLVaLYT7Kch0T+RElMJOQ05WJwWb8qD00GrV6HzU2OudzIvdR6rSlaxtnQtGaEZ6FV6RLcUodNR3lMoFQydFE3xwXpa2yPHynMaiUj07xfv+WRIim9PUT5+Gq2rVrN3eSGJDW7yggVGzjjxrvhT5aw3+P46NSF+Gq/BBwj51Q0gimRtrOCF6S+Q25iLGiVDS0T2h6URGCj9kOwW355MbYkJ/xCdd+oI0l0fIK+2lc92lGKyOrl32ljev/B9ZifN5uXdL3OV/d+0DIqm+p/P4zJ1TTDVBZUZ3P5YZowlQK8hOlDHzmIpVj09ooPBP17Db0+p21Tdu+pbdWXS5qXgqN7LKR5GhI9gcPBgrxfnHyoZfF+ROoX767C2Ohg3N5m5d4/k5n9MZtq16ag0Ctx7m7jLpKd8RXmnTILHY2qwsuT1A6x697AUEpvZO0/II+v0JOd0RBAE0sZE0FjZdsL1kKr8ZvxDdd6F9o4MDh6MUpB0fY/BBxg2JIyVegeqGD2j55zcvoVakw2dWoFRO7BGKy5Y+l4dL+uAJAs2mh2E+mmID4inzFTGzmop+npU+OkbfJC8/JY6C1cNvgqn20lBc4G375Z6K067m9CYzk7L0MnRiCJkb6nEbnVSU2Q6Zf2+LxmZKoVxl8XHUuU3mB8XFWKL0rJSbe/X6571Bh8kHb+0g8FXx8biP3s2jZ9/wfTQCXw691PeGfQ4qtZW9oenEuQ1+L49/OqiFiKSOksJnkid3OpW3tlUyPikEEYlBKNX6Xl+2vP89/z/kh46hGcm1+Csb+CHv9xMqann8D9T2T7s2iAyrpFS33qKYEf4awk0qLG02BEUQqcbD0h6p6AQvDl1TkRdaSshMX4n1MJ98dC4h/hk7ifexV6Ph+8rUufw5gqMIVrihkqbivRGDSOmx3HlQ2O54YmJZE6PI3d7NR8/tpW1H2XTUn/MsLhdbvauKuGTJ7ZRlt3AOVekcdWfxnbJdNgdHg//RHJOR5LbbyYF+2q7bSOKIpX5zT69ewCdSudNYufx9gFig/TkG6F6VKC34E1vqTHZiPDXndQCe18QFyx5+B0Xbj20WB243KLXw3e4HSwpXEKsMZZIv8g+uX5gmI7mWgspgSneYuqe2UPHBdtO54QbiB0czJEfK6jMb8btFokdHNQn4zkdhicMwamwU95o5PDQmwgRGvGbFkGrvX8rX/0yDH6wvpOHD1IGPXdLC03ffsvgkMGEHJbyrOwLSyOkfeOPL0nHYrJjqrcSkdRZSogK0GHQKHlrYwHlTRbumHZsFV8QBM6JOYc3zn+Df9z5FflTk0hecZg735rL79b9joLmgi7XEUURTUE2boWa0GTpS50WIXkvnmLYZlPntAoelCoFAWG6Xkfq1Ja1nrSc472WQolGeWyGoTWoUGuVXQx+S52F0iMNDD0npmvKACQZauo16fzqb5MYNi2W7K2VfPzYVtZ9cpTiQ/V8+dwuNn+ZR+ygIK57bAKjzk9A0ct4fYAhIUMYHjqcybGTe32Of4iO8AR/Cvd2v+W9pc6KucXercEHaVNQjF8MkYZjhk+hEEgJ9ztWr/gkqGkZ2Bh8DxH+OtRKgdKGrh5+fZvkmYb6aUgIkEJLD9cfPu1wzI4EhOsxNdhwu9zcNvw24v3jGRsphYQ2VEjvY3B011lqxpRoWuqs7FhciEIheNNnnEnUSjUOoxlVdSAGtZNhO14ixiD9Lvoza+YvwuAnhBgob7Tg6hBxoR+VhW7kSBrf/wDR7aZt23ZMoZHYQyPQtycis1u7SjrVRVLsd+RxHr5CIZAc5kdZo4WUMD9mDvGdf2VwyGAuevYj1AY/HtkSyebyTVz+7eU8tfUp6i3Hogn27V1BdLVkaDz6o9fgR0g3G0uLvYuc4yG4l5E6bc02LC32k47Q6Q5BEKTQzOMkHU88tK9i0R0xBmuZdm06v3pyEhmTYziyuYLF/95HW5ONOXcMZ+49I70RGyeDRqnh04s/ZXr89JM6LzkzjKrCZtqafYeaVrXr91GpQd328eCYB/l47sddPPK0CCOHK1rYWdSA3XnihXwPta22Adfv4Vgsvi8Pv7Hd4If4aUjwP7aXoC/0ew8BYZJWb2qwcU7sOSy5fIm34ElDRRvGYK3P2PqUrHC0BhXVhdLM/Ezr9x6CovVYVK0MPt+MurWeuCOSBFYhG/zTIyHEgNMtUtkh/aggCIQsWIC9uJjWdesw79hBaUIGwQa1N1ukL0mnpqgFQcDndn6Pjn/71BSfXqwHVWgoEfc/QNiBMr4O/hNXpl/JlzlfMvebubx94G2sTiu5n7+D1tYEQFtTu8Fv7z+9fWu+ucXeZcHWQ1CUH801li7hj8dTX3ZqC7Y9YQzWdfLw3W6RIz9WkpARcsJdpB78Q3Sce/1gfvW3SZy3YCjXPz6BtDERAy5jpGSFgwhF+317+ZX5zWh0yh73AhjUhk51XD1MSQujrtXGlW9sYeQTy7nh7a38e3UuO4oasDm7r4tc02I9Ix4++A7NhGMefoifhnBDODql9Dn3pYfvWdj2tcZz/IJtR1RqpTenzk9BzvFwxW3n8P2Yl1gcshdVVBR+m6Xd+FX9mCb5F2PwgS6yTsD/t3fm0W2VVwL/XcmyLHmVHTt2YifODklYsjRQUigDDYSt0AbK1sK0PZSeYWZgSmcOnM5MZ2jLoZ0pp6dlejp0paWHdih0oLSF0gBz2CGEBAIhSSELSUy8xZu8yJa/+eO9Z8uyLC/arfs7R0dP3/v03ruyfHXf/e5y/nkUzJ3LB1//OsPd3eydt5xAcSGFPsunGsul03ywm0BdcUwrYePSKpbVlPDJtZP3dglcfRXeZUsJ/sf3uHF7JQ8W/A2XdS7lga3f4dIHL6Ly2V10zbOUtWPhr10Y4Eublo/0Ae3tCo0LyRw5/lw/4aFheibJenXCIquSqPBLq4roiUi+OvRWG8GOAVZ+ZF6cd01wrMoiTjyjDq8/dQWl4lE5r5iyah/vTeDWaXq3k9rF5XF/4CfiivUNvP4vm/jBp9dx9YYFtAcH+faTe7niBy/y0W89Q3tw/AJe/2CYrv6hjFj4YIVmxlL47REK3yUuGsoaKPeWT7k091Rw7uwi+8GCvWDc1DsmJDOaVWfNp6jYk7Jwx5kQKCvnr1acxROH/kTReecQfukFSgd7OdqROgs/O+5tUsxI8lV7L0RUQxaPh8C119Jy990A7JyzhEp/Ie4CFy63jIvSMcZw7EAXjSePt9YArvzQAq780NSatojHQ+2/38GRL99K63e/B1h9IC8FjBxFDASvOQt5c9TC97hd/P25o23servjWPgRNXXiuUBa3x8fcZQopZVe+oODDA6E8Xjd7H6+CV+ph8aTYn9u2YyIsPiUObzxzGFCfUNjegU4BdOWrpta+eRYBIoL2by6ls12p6eO3hDP7Gnhll/v4P6XDo78vR1aRpKu0ldWIZL6gI/WHisWv8gzutgcqfABLltyGX1DfbgkeTZlSYUXV4GMs/C7WvoIDw3HvcuqrCvm898+M2nXkiwuX345D+17iO0nF7P850Oc1/4OH3ROrWXiTMgLC7+u3Cr8FG3hAwQ+dQXi81G4aBGH8FPhL0REKCwqGOfS6W7rp79nkLlTjP2eDP/aNSx76ilWvLGTJU88zoKf/ZS6O+9kzk03UXDtFtZcdzP+ci89HeP9x6E+u6xCeXyFP1ksfuvhnmknXE3GSKROWz/BzgEOvNHKCafXzSgKKBtYdGo1w0OGg2+NrYUUq2BaolT4C7lszXzOXlHNz188MC7JqaUnM0lXDhOFZrYHQ/gL3SM/Atetui5mY/VEEJdQVuWjK8rCbz/qROgk93ucDlZVreKEyhP4Rfh5PA0NnHV4R0o7X+WFhV/gdjE/4ONQjOgCd0UFdXfcgavYz/GnQ1Talm6hzz3OpeMs2EaHZCaKq7CQwoULKVw4Go/t2IzFFV6CMZKYRrJsJ7DwfaUevP6CuJE6g6EwHcd6WZKAhRqLEYV/vJ8Db1qNPk7cGH+xNpupXVyOr9TD/h0tLFs/GmkTq2BasrjhzMVc+6OXeWTHkTF3jc1dmVb4o6GZThABWIu2jnWfSsqrfXRGWfjtTXaETu3U6zBlCyLClmVb+MbL32Dg7EtYev8j9HzQnLLz5abJNQMaAv6YFj5A+SUX4znzo/QNhqnwW1/aQl/BuCidZrtYV9UUer4mi5KAl56O8b7cieroOIjIpDV12o8EMQaqkxSh4xCZfPX280epW1o+o6SubMHlEhadPIcDu9rG1FeKVTAtWZyxpIqVdWX88Nn9YxbeHQs/Uz58x8J/P8rCb0uTwi+bY1n4kbWa2o8GKa0qyprom+ly0eKLKHIX8adlvbjMMIvefiVl58ofhR+VfBXN8d6xPshYLp3mA11UN5Sm1TVRMoGFH+yMr/BhbH/bWLQetmvgJ9ml4y/34nIJe1/+gM7mvhkt1mYbi06tZrA/zOG9VqZzvIJpyUBEuOEsq9n5/+0dTfxq6erHJVBVkhmFX1PqxeOWcaGZ7Wm08EP9YQaCo/+bbUcnjtDJBUoLSzm/8Xx+FXqBnrp6Tj+wPWXJVwlpLhGpFJEnRWSf/TwuZ1lEGkTkaRHZLSJvicjNiZxzpiyo9NMeDE34QTqLTgHHwi8a69IZHjY0H+pOujtnMoorvIT6w+PcS46FP5FLByyFH+wYiFsTqLDIPWKRJwuXSygOeEdCFpesTa7LKBPUnxDA43Wzf4elfFvftwqm1aZI4QNcfPI8asuK+OGzo4l5LT0DVBZ7cc8gKigZuFzC/IrxoZnpUvhlc6zvqhOpEw4P03Gsl6ocVvhgLd72hvt4b90CVrXtp2nfwZScJ1FT9TZgqzFmGbDVfh3NEHCrMeZErN7JN4nIygTPO20WjETqTFD4KWj9EATs8D9PlIV/vCnI0EA4aQu2U8Wpix+MWrjtdcoqxCkt4HS/msjKd5qWpyK23fHjL99QO+3SAdlIgcfNglVV7N/Zihk2fGA3SKmLk3CVKB63i89ubOSFd9vYdcQ6X3NXZpKuImmoHB+a2R4MUelPg8KPisXvbO5jOGzihmTmAqdUn8LSiqU8sbwFF4aOx59IyXkSVfiXAvfZ2/cBl0VPMMY0GWO229vdwG5g8kD1JLMgoi5+LKJdOt4oH36qFmwnwynI1RPVJ7avK4SvxBM3/rsijsI3w4bWI8Gkxt9H4tw1zAZ3jsPiU+fQ2xXi2IEumt7toLSyaFodwmbCVRsWUFzo5ke2ld/Sk5myCpHUB3wcifg/6guF6RsMU1mSHh8+jFr4uRyhE4mzePuqdz9/qajB/fSTKTlPogp/rjGmCSzFzmhwSUxEpBFYA7wcZ84XRGSbiGxraZm4aNV0WRAZix8DR+EHHB9+VJRO88FuCovcVNSkNxJgQgu/e3DCpCuH8hofbo+LbX88SMuhsdU5O1v6GBoIJzXDNpLlG+Zy6sca4jYYzzUWrq7C5RLee73FSrhKoTvHodzn4coPLeCxN5po6uzLCgu/PuCntSdEX8gyiNodYykNFr6n0I2/vHDEwm8/2gOSmxE60Vyy5BIKXYU8vaqEzooazGDy/fiTKnwR+bOI7IrxuHQ6JxKREuAh4BZjTNdE84wx9xpj1htj1ldXJy8rrtzvoayogP2tsUsGOz78Ct+oS2d4yDBkx0E32xUyowuVpZqJLPzezoG4C7ZguSEu+OJJDAQH+c1d23j19/tH2r05GbbVDalRyAtWVrHx8pl3BcpGvH4P81dUsPuFJno74xdMSyaf3diIAX7y3H5as8TCBzjSYRlP7T1j745TTfkc36iF3xSkfI6PglngNiz3lrOpcRNPfqSZP3ziBsST/OzySRW+MeZjxpjVMR6PAMdEpA7Afo4ZQCoiHixl/0tjzMPJFGA6rFsY4Kl3mmPWl+noHaSsqIACuwKjd6SeTpihwTBth3vS7s4BS2kXFXvGJV/Fy7KNZOGqKq7+6mksWVfDK7/bz0PffI22oz20vt+NyyUE6nLfMkoni0+tpt9e70mka9J0aKj0c8HqWn7+4kGGhk0WWPiWwnfWwxwLvyoNLh0YrYsPlksnlyN0orl82eXg6md3z3MpOX6iLp1Hgevt7euBR6IniLUi+GNgtzHm7gTPlxCfXFtPU2c/L77XNm5fezA04s4BK0oHrHo6re9byUNzp9BOLxUUB7xjXDrGGPq6JnfpOBQVezjv86vY/IXVdLf38z93vso7L31ARa2fAk/uW0bppPFk667TKpiWPr/xDWcuZsCuqFmdobIKDqPZtraFH7S+m4E0uHTA8uP3dAwQ6huio7kv5xdsI1k3dx0+amkKP5OS4yeq8O8CNonIPmCT/RoRmScif7DnbAQ+A5wjIjvsx4UJnndGbFo5l9KiAh56bXyf0uO9oTFfWE/RaMXMTC3YOpQEvGNaBob6w4SHhid16USzZG0NV//raTSunkOwY2BW+dfTRUnAy/wVFTScWDmjgmkz5ZSGCjYsshrH1JRl1sKvLvFS6HaNROq023c8VcXpua7yah8YOPR2O2bYUDl/9ih8EWGFfzOEywmFk9/9KqHUNGNMG3BujPGjwIX29nNAZoKGoyjyuLn45Dr+9/Wj3HHZ0JgWccd7Q2MKUo2USO4P03ywC395YcojMiaiuMJL84HRZY++CVobTgV/WSGbb1zNkT3HqZg7e/5R0snFN52S9jLNALecu4xbH9w5UoY7U7hcwvyIMsntwQHcLqE0TZmuTqSOU7K6si63I3SiuW/Ll1JmTORNpq3DlrX19A2G+eObTWPGjwcHqYgoweuNqInffKB7XMOTdFJS4aWve3AkrX+i5uVTRUSoP6EyYz9guU5BoRu3J/3/OmcsncOLt5+btsXReFh18R2XjnV3nK47Hqcu/oFdrYhLRvJNZgup/BzzTuGvWxigscrPw9uPjBmPThzx2D78nuP9dBzrzZg7B0YjdZyuSyOF02Zg4StKMqiPqIvfHgxRlcYfIV+phwKvm4HgEOXVvoz8+OYqefdJiQifXFvPi++1jVgo/YNW4sjYRVvLwj/8jlU7JVMLtjAai+9E6vQm4NJRlGRQH/DRFgzRGxqyAx7S16BGRCi3SyzkekmFdJN3Ch/gE2usRN/f2lb+SNJVhIXvdL06srcDgOo0l1SIpKTC+nIH7Vj8vu4QIsQtq6AoqWS0THKfbeGn1z3o+PEDqvCnRV4q/IZKP6cvruTh149gjBmpo1MZYaUUeNy4CoRQ3xDlNb6kdoSaLsWBsclXvV0hikrT5zNVlGgiQzPTbeHDaE2d2RSSmQ7yUuGDtXi7vzXI9kMdMS18GHXrZHLB1roONx6veyQWP17zckVJBw22hX+wrZeOvkEq02zhOyVO0tmbYjaQtwr/gpPq8HncPLT98Ghp5OJohW+5dTK5YAuWz7K4wkuP3dy4tyuEv0zdOUrmmFPipbDAxa4jXRhDWhdtAVacVsvmG1erhT9N8lbhl3gL2Ly6lsd2HuVYl6VIx1n4vuyw8MFauHUs/L6ukEboKBnF5RLqAz7eONwBjDeWUo3H62bJmtzvs5Bu8lbhg+XW6eof4sFtVuZtZBw+WC4dl0tSVlFyOpRUeOk5PoAxZsp1dBQlldQH/PylxSrCl24LX5kZudkEMkl8eEkVdeVF7DnWTWlRAR732N8/Z2EoGyrxFVd4CXaGCPUNER4cxp/h9HpFqQ/4cFrLpquOjpIYea3w3S7hE2vm8/1n3o2ZvXj21SsYNuMra2aCkoAXM2xoO2JZVOrDVzKNE5oJ6auUqSRGXrt0ALasqwegIoaF4va4sqY9n5Nt23zQamSiPnwl0zihmaAWfq6Q1xY+wJLqEjYuraK2zDf55AxSErCSr5zOVZplq2Qax8Iv9RZQWJD3tmNOkPcKH+Bnn92AOwPVD6eDY+E7Ct+ni7ZKhnEUfjp62SrJQRU+jFuszUZ8JR5cbuH4sV5EVOErmae6xIu3wKXunBwi+zWdAoC4rOQrjFVDR8sqKJlGRFg0p5jassx24FKmjlr4OURJwEt3W7/675Ws4Z5r1lCkbTJzhoQsfBGpFJEnRWSf/RyIM9ctIq+LyGOJnDOfcfz46s5RsoWlNaVjonWU7CZRl85twFZjzDJgq/16Im4Gdid4vrymxFb4auErijITElX4lwL32dv3AZfFmiQi9cBFwI8SPF9eM2Lhq8JXFGUGJKrw5xpjmgDs54mqGX0H+CdgeLIDisgXRGSbiGxraWlJ8PJmF04svlr4iqLMhEkXbUXkz0BtjF1fmcoJRORioNkY85qInD3ZfGPMvcC9AOvXr8+OugZZgtPqUBW+oigzYVKFb4z52ET7ROSYiNQZY5pEpA5ojjFtI/BxEbkQKALKROR+Y8ynZ3zVeUr1glLWbFrAwtVVmb4URVFykERdOo8C19vb1wOPRE8wxtxujKk3xjQCVwFPqbKfGe4CF2dsWYpPMxsVRZkBiSr8u4BNIrIP2GS/RkTmicgfEr04RVEUJXkklHhljGkDzo0xfhS4MMb4M8AziZxTURRFmRlaWkFRFCVPUIWvKIqSJ6jCVxRFyRNU4SuKouQJqvAVRVHyBFX4iqIoeYIYk73VC0SkBThov5wDtGbwctJNvskLKnO+oDKnloXGmOpYO7Ja4UciItuMMeszfR3pIt/kBZU5X1CZM4e6dBRFUfIEVfiKoih5Qi4p/HszfQFpJt/kBZU5X1CZM0TO+PAVRVGUxMglC19RFEVJgIwpfBH5iYg0i8iuiLFTRORFEXlTRH4nImX2+LUisiPiMSwip9r71tnz/yIi3xURyZBIkzJNmT0icp89vltEbo94z2yVuVBEfmqP74zskJYrMotIg4g8bf/N3hKRm+3xShF5UkT22c+BiPfcbsu1R0TOjxiflTKLSJU9v0dE7ok61myVeZOIvGbL9pqInBNxrPTJbIzJyAM4C1gL7IoYexX4qL39OeBrMd53EvBexOtXgA8DAvwRuCBTMiVTZuAa4Ff2th84ADTOcplvAn5qb9cArwGuXJIZqAPW2tulwF5gJfAt4DZ7/Dbgm/b2SmAn4AUWAe8C7lkuczHwEeCLwD1Rx5qtMq8B5tnbq4EjmZA50x9aY5Qi6GJ0XaEBeDvGe+4EvhHxob8Tse9q4L8z/WVIhsy2LL/D6llQZX+hKme5zP8FfDpi3lZgQy7KHHGtj2A1B9oD1NljdcAee/t24PaI+U/Y//yzVuaIeX8dqfDzQWZ7XIA2rB/5tMqcbT78XcDH7e0rsJRBNFcCD9jb84HDEfsO22O5xEQy/wYIAk3AIeA/jTHtzG6ZdwKXikiBiCwC1tn7clJmEWnEsuxeBuYaY5oA7Ocae9p84P2ItzmyzWaZJyJfZN4CvG6MGSDNMmebwv8ccJOIvIZ1mxSK3CkipwG9xhjHHxzL15VrYUcTybwBCAPzsG71bxWRxcxumX+C9YXfBnwHeAEYIgdlFpES4CHgFmNMV7ypMcZMnPGsZRoyT3iIGGOzSmYRWQV8E7jRGYoxLWUyJ9TiMNkYY94BzgMQkeXARVFTrmLUugdLOdRHvK4HjqbyGpNNHJmvAR43xgwCzSLyPLAeeJZZKrMxZgj4B2eeiLwA7AOOk0Myi4gHSwn80hjzsD18TETqjDFNIlIHNNvjhxl7J+vIllPf7WnKPBGzWmYRqQd+C1xnjHnXHk6rzFll4YtIjf3sAv4Z+EHEPhfW7f+vnDH7lqlbRE63V7avw/Kl5QxxZD4EnCMWxcDpWL6+WSuziPhtWRGRTcCQMebtXJLZvr4fA7uNMXdH7HoUuN7evp7R638UuEpEvLYbaxnwyiyXOSazWWYRqQB+j7Ve87wzOe0yZ3CR4wEs//Qg1q/c54GbsRYn9wJ3YS/s2fPPBl6KcZz1WD7hd4F7It+TbY/pyAyUAA8CbwFvA/+YBzI3Yi167Qb+jFX1L6dkxoo+McAbwA77cSHWwvtWrDuWrUBlxHu+Ysu1h4gIjVku8wGgHeixvxcrZ7PMWIZNMGLuDqAm3TJrpq2iKEqekFUuHUVRFCV1qMJXFEXJE1ThK4qi5Amq8BVFUfIEVfiKoih5gip8RbGxcx6eE5ELIsY+JSKPZ/K6FCVZaFimokQgIqux8h/WAG6seOnNZjQzcjrHchtjwsm9QkWZOarwFSUKEfkWVpJMsf28EKssdwHwb8aYR+yCWb+w5wD8rTHmBbFq+H8VK9nsVGPMyvRevaJMjCp8RYnCLu+wHauo22PAW8aY++30+FewrH8DDBtj+kVkGfCAMWa9rfB/D6w2xuzPxPUrykRkVfE0RckGjDFBEfk1Vtr/p4BLROTL9u4iYAFWgat7xOq8FgaWRxziFVX2SjaiCl9RYjNsPwTYYozZE7lTRP4NOAacghX80B+xO5ima1SUaaFROooSnyeAv3P6jIrIGnu8HGgyxgwDn8Fa4FWUrEYVvqLE52uAB3hDrEbsX7PHvw9cLyIvYblz1KpXsh5dtFUURckT1MJXFEXJE1ThK4qi5Amq8BVFUfIEVfiKoih5gip8RVGUPEEVvqIoSp6gCl9RFCVPUIWvKIqSJ/w/HkLCLww052AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = data_df.loc[1972:]\n",
    "df.plot.line();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>S&amp;P</th>\n",
       "      <th>T-Bills</th>\n",
       "      <th>T-Notes</th>\n",
       "      <th>Baa Corps</th>\n",
       "      <th>Gold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rows</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>real return</th>\n",
       "      <td>0.077076</td>\n",
       "      <td>0.004029</td>\n",
       "      <td>0.025717</td>\n",
       "      <td>0.047959</td>\n",
       "      <td>0.063045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>0.171865</td>\n",
       "      <td>0.026286</td>\n",
       "      <td>0.107164</td>\n",
       "      <td>0.094134</td>\n",
       "      <td>0.256768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  S&P   T-Bills   T-Notes  Baa Corps      Gold\n",
       "rows                                                          \n",
       "real return  0.077076  0.004029  0.025717   0.047959  0.063045\n",
       "vol          0.171865  0.026286  0.107164   0.094134  0.256768"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute covariance matrix\n",
    "Sigma = np.cov(df.transpose())\n",
    "# number of assets\n",
    "\n",
    "n = Sigma.shape[0]\n",
    "# average returns\n",
    "mu = df.mean().values\n",
    "# asset STDs\n",
    "asset_vols = np.sqrt(Sigma.diagonal())\n",
    "# variable to optimize over - portfolio weights\n",
    "w = cp.Variable(n)\n",
    "\n",
    "# objectives to optimize\n",
    "# portfolio return\n",
    "ret = mu.T @ w \n",
    "# volatility\n",
    "vol = cp.quad_form(w, Sigma)\n",
    "\n",
    "z = pd.DataFrame([mu, asset_vols], columns=labels)\n",
    "z['rows'] = ['real return', 'vol']\n",
    "z.set_index('rows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max return portfolio weights\n"
     ]
    },
    {
     "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>S&amp;P</th>\n",
       "      <th>T-Bills</th>\n",
       "      <th>T-Notes</th>\n",
       "      <th>Baa Corps</th>\n",
       "      <th>Gold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   S&P  T-Bills  T-Notes  Baa Corps  Gold\n",
       "0  1.0      0.0      0.0        0.0   0.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solve max return portfolio (corner solution)\n",
    "prob = cp.Problem(cp.Maximize(ret), \n",
    "                  [cp.sum(w) == 1, \n",
    "                   w >= 0]\n",
    "                 )\n",
    "prob.solve()\n",
    "wts = [float('%0.4f' % v) for v in w.value]\n",
    "maxretvol = vol.value\n",
    "maxret = ret.value\n",
    "print(\"Max return portfolio weights\")\n",
    "pd.DataFrame([wts], columns=labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min vol portfolio weights\n"
     ]
    },
    {
     "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>S&amp;P</th>\n",
       "      <th>T-Bills</th>\n",
       "      <th>T-Notes</th>\n",
       "      <th>Baa Corps</th>\n",
       "      <th>Gold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0045</td>\n",
       "      <td>0.9506</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0449</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      S&P  T-Bills  T-Notes  Baa Corps    Gold\n",
       "0  0.0045   0.9506     -0.0       -0.0  0.0449"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# solve min vol portfolio (other corner solution)\n",
    "prob = cp.Problem(cp.Minimize(vol),\n",
    "                  [cp.sum(w) == 1, \n",
    "                   w >= 0]\n",
    "                 )\n",
    "prob.solve()\n",
    "wts = [float('%0.4f' % v) for v in w.value]\n",
    "\n",
    "minvol = vol.value\n",
    "minvolret = ret.value\n",
    "print(\"Min vol portfolio weights\")\n",
    "pd.DataFrame([wts], columns=labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 438 ms, sys: 3.59 ms, total: 442 ms\n",
      "Wall time: 441 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# solve points in between\n",
    "# maximize return subject to volatility constraints between minimum volatility and max return volatility\n",
    "\n",
    "# specify a Parameter variable instead of creating new Problem at each iteration\n",
    "# this allows the solver to reuse previous work\n",
    "vol_limit = cp.Parameter(nonneg=True)\n",
    "\n",
    "prob = cp.Problem(cp.Maximize(ret),\n",
    "                  [cp.sum(w) == 1, \n",
    "                   w >= 0,\n",
    "                   vol <= vol_limit\n",
    "                  ]\n",
    "                 )\n",
    "\n",
    "# define function so we can solve many in parallel\n",
    "def solve_vl(vl_val):\n",
    "    vol_limit.value = vl_val\n",
    "    result = prob.solve()\n",
    "    return (ret.value, np.sqrt(vol.value), w.value)\n",
    "\n",
    "# number of points on the frontier\n",
    "NPOINTS = 200\n",
    "vl_vals = np.linspace(minvol, maxretvol, NPOINTS)\n",
    "\n",
    "# iterate in-process\n",
    "results_dict = {}\n",
    "for vl_val in vl_vals:\n",
    "    # print(datetime.strftime(datetime.now(), \"%H:%M:%S\"), vl_val)\n",
    "    results_dict[vl_val] = solve_vl(vl_val)\n",
    "    \n",
    "# parallel implementation\n",
    "# NPROCESSES = 8\n",
    "# pool = Pool(processes = NPROCESSES)\n",
    "# result_values = pool.map(solve_vl, vl_vals)\n",
    "# results_dict = dict(zip(vl_vals, result_values))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>i</th>\n",
       "      <th>vol</th>\n",
       "      <th>return</th>\n",
       "      <th>std</th>\n",
       "      <th>S&amp;P</th>\n",
       "      <th>T-Bills</th>\n",
       "      <th>T-Notes</th>\n",
       "      <th>Baa Corps</th>\n",
       "      <th>Gold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000550</td>\n",
       "      <td>0.007012</td>\n",
       "      <td>0.023448</td>\n",
       "      <td>0.004576</td>\n",
       "      <td>9.505491e-01</td>\n",
       "      <td>1.183515e-08</td>\n",
       "      <td>2.303986e-08</td>\n",
       "      <td>4.487464e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000695</td>\n",
       "      <td>0.013496</td>\n",
       "      <td>0.026372</td>\n",
       "      <td>0.061194</td>\n",
       "      <td>8.490480e-01</td>\n",
       "      <td>7.719037e-09</td>\n",
       "      <td>1.994387e-02</td>\n",
       "      <td>6.981373e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.000841</td>\n",
       "      <td>0.016466</td>\n",
       "      <td>0.029003</td>\n",
       "      <td>0.072839</td>\n",
       "      <td>7.921899e-01</td>\n",
       "      <td>5.964610e-09</td>\n",
       "      <td>5.632069e-02</td>\n",
       "      <td>7.865010e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.000987</td>\n",
       "      <td>0.018829</td>\n",
       "      <td>0.031414</td>\n",
       "      <td>0.082115</td>\n",
       "      <td>7.469524e-01</td>\n",
       "      <td>6.926451e-09</td>\n",
       "      <td>8.525080e-02</td>\n",
       "      <td>8.568157e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.001132</td>\n",
       "      <td>0.020853</td>\n",
       "      <td>0.033652</td>\n",
       "      <td>0.090049</td>\n",
       "      <td>7.082165e-01</td>\n",
       "      <td>2.888480e-08</td>\n",
       "      <td>1.100346e-01</td>\n",
       "      <td>9.169983e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>195</td>\n",
       "      <td>0.028955</td>\n",
       "      <td>0.076973</td>\n",
       "      <td>0.170161</td>\n",
       "      <td>0.992684</td>\n",
       "      <td>9.199851e-10</td>\n",
       "      <td>1.395444e-09</td>\n",
       "      <td>2.931899e-09</td>\n",
       "      <td>7.316062e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>196</td>\n",
       "      <td>0.029101</td>\n",
       "      <td>0.076999</td>\n",
       "      <td>0.170589</td>\n",
       "      <td>0.994528</td>\n",
       "      <td>1.181944e-09</td>\n",
       "      <td>1.784498e-09</td>\n",
       "      <td>3.668182e-09</td>\n",
       "      <td>5.472251e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>197</td>\n",
       "      <td>0.029246</td>\n",
       "      <td>0.077025</td>\n",
       "      <td>0.171015</td>\n",
       "      <td>0.996362</td>\n",
       "      <td>1.593829e-09</td>\n",
       "      <td>2.395247e-09</td>\n",
       "      <td>4.813086e-09</td>\n",
       "      <td>3.638420e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>198</td>\n",
       "      <td>0.029392</td>\n",
       "      <td>0.077050</td>\n",
       "      <td>0.171441</td>\n",
       "      <td>0.998186</td>\n",
       "      <td>2.387970e-09</td>\n",
       "      <td>3.584662e-09</td>\n",
       "      <td>7.083725e-09</td>\n",
       "      <td>1.814407e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>199</td>\n",
       "      <td>0.029538</td>\n",
       "      <td>0.077076</td>\n",
       "      <td>0.171865</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.556936e-09</td>\n",
       "      <td>7.944732e-09</td>\n",
       "      <td>1.431840e-08</td>\n",
       "      <td>5.284670e-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       i       vol    return       std       S&P       T-Bills       T-Notes  \\\n",
       "0      0  0.000550  0.007012  0.023448  0.004576  9.505491e-01  1.183515e-08   \n",
       "1      1  0.000695  0.013496  0.026372  0.061194  8.490480e-01  7.719037e-09   \n",
       "2      2  0.000841  0.016466  0.029003  0.072839  7.921899e-01  5.964610e-09   \n",
       "3      3  0.000987  0.018829  0.031414  0.082115  7.469524e-01  6.926451e-09   \n",
       "4      4  0.001132  0.020853  0.033652  0.090049  7.082165e-01  2.888480e-08   \n",
       "..   ...       ...       ...       ...       ...           ...           ...   \n",
       "195  195  0.028955  0.076973  0.170161  0.992684  9.199851e-10  1.395444e-09   \n",
       "196  196  0.029101  0.076999  0.170589  0.994528  1.181944e-09  1.784498e-09   \n",
       "197  197  0.029246  0.077025  0.171015  0.996362  1.593829e-09  2.395247e-09   \n",
       "198  198  0.029392  0.077050  0.171441  0.998186  2.387970e-09  3.584662e-09   \n",
       "199  199  0.029538  0.077076  0.171865  1.000000  5.556936e-09  7.944732e-09   \n",
       "\n",
       "        Baa Corps          Gold  \n",
       "0    2.303986e-08  4.487464e-02  \n",
       "1    1.994387e-02  6.981373e-02  \n",
       "2    5.632069e-02  7.865010e-02  \n",
       "3    8.525080e-02  8.568157e-02  \n",
       "4    1.100346e-01  9.169983e-02  \n",
       "..            ...           ...  \n",
       "195  2.931899e-09  7.316062e-03  \n",
       "196  3.668182e-09  5.472251e-03  \n",
       "197  4.813086e-09  3.638420e-03  \n",
       "198  7.083725e-09  1.814407e-03  \n",
       "199  1.431840e-08  5.284670e-08  \n",
       "\n",
       "[200 rows x 9 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_df = pd.DataFrame(enumerate(results_dict.keys()))\n",
    "ret_df.columns=['i', 'vol']\n",
    "ret_df['return'] = [results_dict[v][0] for v in ret_df['vol']]\n",
    "ret_df['std'] = [results_dict[v][1] for v in ret_df['vol']]\n",
    "for i, colname in enumerate(labels):\n",
    "    ret_df[colname]=[results_dict[v][2][i] for v in ret_df['vol']]\n",
    "ret_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_efrontier(ret_df, df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "transition_map(ret_df, labels=df.columns, startyear=df.index[0], endyear=df.index[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1983 - present (post-inflation era)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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 = data_df.loc[1983:]\n",
    "df.plot.line();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>S&amp;P</th>\n",
       "      <th>T-Bills</th>\n",
       "      <th>T-Notes</th>\n",
       "      <th>Baa Corps</th>\n",
       "      <th>Gold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rows</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>real return</th>\n",
       "      <td>0.094305</td>\n",
       "      <td>0.005524</td>\n",
       "      <td>0.037455</td>\n",
       "      <td>0.060945</td>\n",
       "      <td>0.017336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>0.163317</td>\n",
       "      <td>0.024971</td>\n",
       "      <td>0.102395</td>\n",
       "      <td>0.081568</td>\n",
       "      <td>0.151761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  S&P   T-Bills   T-Notes  Baa Corps      Gold\n",
       "rows                                                          \n",
       "real return  0.094305  0.005524  0.037455   0.060945  0.017336\n",
       "vol          0.163317  0.024971  0.102395   0.081568  0.151761"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute covariance matrix\n",
    "Sigma = np.cov(df.transpose())\n",
    "# number of assets\n",
    "\n",
    "n = Sigma.shape[0]\n",
    "# average returns\n",
    "mu = df.mean().values\n",
    "# asset STDs\n",
    "asset_vols = np.sqrt(Sigma.diagonal())\n",
    "# variable to optimize over - portfolio weights\n",
    "w = cp.Variable(n)\n",
    "\n",
    "# objectives to optimize\n",
    "# portfolio return\n",
    "ret = mu.T @ w \n",
    "# volatility\n",
    "vol = cp.quad_form(w, Sigma)\n",
    "\n",
    "z = pd.DataFrame([mu, asset_vols], columns=labels)\n",
    "z['rows'] = ['real return', 'vol']\n",
    "z.set_index('rows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max return portfolio weights\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>S&amp;P</th>\n",
       "      <th>T-Bills</th>\n",
       "      <th>T-Notes</th>\n",
       "      <th>Baa Corps</th>\n",
       "      <th>Gold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   S&P  T-Bills  T-Notes  Baa Corps  Gold\n",
       "0  1.0      0.0      0.0        0.0   0.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solve max return portfolio (corner solution)\n",
    "prob = cp.Problem(cp.Maximize(ret), \n",
    "                  [cp.sum(w) == 1, \n",
    "                   w >= 0]\n",
    "                 )\n",
    "prob.solve()\n",
    "wts = [float('%0.4f' % v) for v in w.value]\n",
    "maxretvol = vol.value\n",
    "maxret = ret.value\n",
    "print(\"Max return portfolio weights\")\n",
    "pd.DataFrame([wts], columns=labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min vol portfolio weights\n"
     ]
    },
    {
     "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>S&amp;P</th>\n",
       "      <th>T-Bills</th>\n",
       "      <th>T-Notes</th>\n",
       "      <th>Baa Corps</th>\n",
       "      <th>Gold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0043</td>\n",
       "      <td>0.9331</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0626</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      S&P  T-Bills  T-Notes  Baa Corps    Gold\n",
       "0  0.0043   0.9331     -0.0       -0.0  0.0626"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# solve min vol portfolio (other corner solution)\n",
    "prob = cp.Problem(cp.Minimize(vol),\n",
    "                  [cp.sum(w) == 1, \n",
    "                   w >= 0]\n",
    "                 )\n",
    "prob.solve()\n",
    "wts = [float('%0.4f' % v) for v in w.value]\n",
    "\n",
    "minvol = vol.value\n",
    "minvolret = ret.value\n",
    "print(\"Min vol portfolio weights\")\n",
    "pd.DataFrame([wts], columns=labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 429 ms, sys: 3.73 ms, total: 433 ms\n",
      "Wall time: 432 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# solve points in between\n",
    "# maximize return subject to volatility constraints between minimum volatility and max return volatility\n",
    "\n",
    "# specify a Parameter variable instead of creating new Problem at each iteration\n",
    "# this allows the solver to reuse previous work\n",
    "vol_limit = cp.Parameter(nonneg=True)\n",
    "\n",
    "prob = cp.Problem(cp.Maximize(ret),\n",
    "                  [cp.sum(w) == 1, \n",
    "                   w >= 0,\n",
    "                   vol <= vol_limit\n",
    "                  ]\n",
    "                 )\n",
    "\n",
    "# define function so we can solve many in parallel\n",
    "def solve_vl(vl_val):\n",
    "    vol_limit.value = vl_val\n",
    "    result = prob.solve()\n",
    "    return (ret.value, np.sqrt(vol.value), w.value)\n",
    "\n",
    "# number of points on the frontier\n",
    "NPOINTS = 200\n",
    "vl_vals = np.linspace(minvol, maxretvol, NPOINTS)\n",
    "\n",
    "# iterate in-process\n",
    "results_dict = {}\n",
    "for vl_val in vl_vals:\n",
    "    # print(datetime.strftime(datetime.now(), \"%H:%M:%S\"), vl_val)\n",
    "    results_dict[vl_val] = solve_vl(vl_val)\n",
    "    \n",
    "# parallel implementation\n",
    "# NPROCESSES = 8\n",
    "# pool = Pool(processes = NPROCESSES)\n",
    "# result_values = pool.map(solve_vl, vl_vals)\n",
    "# results_dict = dict(zip(vl_vals, result_values))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>i</th>\n",
       "      <th>vol</th>\n",
       "      <th>return</th>\n",
       "      <th>std</th>\n",
       "      <th>S&amp;P</th>\n",
       "      <th>T-Bills</th>\n",
       "      <th>T-Notes</th>\n",
       "      <th>Baa Corps</th>\n",
       "      <th>Gold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>0.006648</td>\n",
       "      <td>0.022878</td>\n",
       "      <td>0.004324</td>\n",
       "      <td>9.330292e-01</td>\n",
       "      <td>1.420638e-08</td>\n",
       "      <td>3.055975e-08</td>\n",
       "      <td>6.264680e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000655</td>\n",
       "      <td>0.013318</td>\n",
       "      <td>0.025590</td>\n",
       "      <td>0.053923</td>\n",
       "      <td>8.376991e-01</td>\n",
       "      <td>1.918237e-08</td>\n",
       "      <td>3.959224e-02</td>\n",
       "      <td>6.878592e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.000786</td>\n",
       "      <td>0.016593</td>\n",
       "      <td>0.028040</td>\n",
       "      <td>0.061259</td>\n",
       "      <td>7.850719e-01</td>\n",
       "      <td>2.374742e-08</td>\n",
       "      <td>8.749606e-02</td>\n",
       "      <td>6.617282e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.000918</td>\n",
       "      <td>0.019214</td>\n",
       "      <td>0.030292</td>\n",
       "      <td>0.067111</td>\n",
       "      <td>7.429310e-01</td>\n",
       "      <td>1.477826e-08</td>\n",
       "      <td>1.258517e-01</td>\n",
       "      <td>6.410655e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.001049</td>\n",
       "      <td>0.021464</td>\n",
       "      <td>0.032389</td>\n",
       "      <td>0.072159</td>\n",
       "      <td>7.067962e-01</td>\n",
       "      <td>1.514554e-08</td>\n",
       "      <td>1.587364e-01</td>\n",
       "      <td>6.230793e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>195</td>\n",
       "      <td>0.026147</td>\n",
       "      <td>0.093855</td>\n",
       "      <td>0.161700</td>\n",
       "      <td>0.986527</td>\n",
       "      <td>7.600875e-09</td>\n",
       "      <td>2.501311e-08</td>\n",
       "      <td>1.347265e-02</td>\n",
       "      <td>1.228322e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>196</td>\n",
       "      <td>0.026278</td>\n",
       "      <td>0.093968</td>\n",
       "      <td>0.162106</td>\n",
       "      <td>0.989912</td>\n",
       "      <td>6.783063e-09</td>\n",
       "      <td>2.230826e-08</td>\n",
       "      <td>1.008763e-02</td>\n",
       "      <td>1.095869e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>197</td>\n",
       "      <td>0.026410</td>\n",
       "      <td>0.094081</td>\n",
       "      <td>0.162510</td>\n",
       "      <td>0.993286</td>\n",
       "      <td>1.690119e-08</td>\n",
       "      <td>5.581552e-08</td>\n",
       "      <td>6.713858e-03</td>\n",
       "      <td>2.736591e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>198</td>\n",
       "      <td>0.026541</td>\n",
       "      <td>0.094193</td>\n",
       "      <td>0.162914</td>\n",
       "      <td>0.996649</td>\n",
       "      <td>5.298918e-09</td>\n",
       "      <td>1.674125e-08</td>\n",
       "      <td>3.351406e-03</td>\n",
       "      <td>8.748095e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>199</td>\n",
       "      <td>0.026672</td>\n",
       "      <td>0.094305</td>\n",
       "      <td>0.163317</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.474278e-09</td>\n",
       "      <td>2.692136e-09</td>\n",
       "      <td>5.425036e-09</td>\n",
       "      <td>1.847371e-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       i       vol    return       std       S&P       T-Bills       T-Notes  \\\n",
       "0      0  0.000523  0.006648  0.022878  0.004324  9.330292e-01  1.420638e-08   \n",
       "1      1  0.000655  0.013318  0.025590  0.053923  8.376991e-01  1.918237e-08   \n",
       "2      2  0.000786  0.016593  0.028040  0.061259  7.850719e-01  2.374742e-08   \n",
       "3      3  0.000918  0.019214  0.030292  0.067111  7.429310e-01  1.477826e-08   \n",
       "4      4  0.001049  0.021464  0.032389  0.072159  7.067962e-01  1.514554e-08   \n",
       "..   ...       ...       ...       ...       ...           ...           ...   \n",
       "195  195  0.026147  0.093855  0.161700  0.986527  7.600875e-09  2.501311e-08   \n",
       "196  196  0.026278  0.093968  0.162106  0.989912  6.783063e-09  2.230826e-08   \n",
       "197  197  0.026410  0.094081  0.162510  0.993286  1.690119e-08  5.581552e-08   \n",
       "198  198  0.026541  0.094193  0.162914  0.996649  5.298918e-09  1.674125e-08   \n",
       "199  199  0.026672  0.094305  0.163317  1.000000  1.474278e-09  2.692136e-09   \n",
       "\n",
       "        Baa Corps          Gold  \n",
       "0    3.055975e-08  6.264680e-02  \n",
       "1    3.959224e-02  6.878592e-02  \n",
       "2    8.749606e-02  6.617282e-02  \n",
       "3    1.258517e-01  6.410655e-02  \n",
       "4    1.587364e-01  6.230793e-02  \n",
       "..            ...           ...  \n",
       "195  1.347265e-02  1.228322e-08  \n",
       "196  1.008763e-02  1.095869e-08  \n",
       "197  6.713858e-03  2.736591e-08  \n",
       "198  3.351406e-03  8.748095e-09  \n",
       "199  5.425036e-09  1.847371e-09  \n",
       "\n",
       "[200 rows x 9 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_df = pd.DataFrame(enumerate(results_dict.keys()))\n",
    "ret_df.columns=['i', 'vol']\n",
    "ret_df['return'] = [results_dict[v][0] for v in ret_df['vol']]\n",
    "ret_df['std'] = [results_dict[v][1] for v in ret_df['vol']]\n",
    "for i, colname in enumerate(labels):\n",
    "    ret_df[colname]=[results_dict[v][2][i] for v in ret_df['vol']]\n",
    "ret_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_efrontier(ret_df, df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "transition_map(ret_df, labels=df.columns, startyear=df.index[0], endyear=df.index[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Long/short optimization with leverage constraint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = data_df['S&P'].values\n",
    "n = len(x1)\n",
    "target_corr = 0.6\n",
    "theta = np.arccos(target_corr)\n",
    "mean_return = -0.05\n",
    "x2 = np.random.normal(0, x1.std(), size=n)  \n",
    "# center so actual mean = 0\n",
    "X = pd.DataFrame({'x1': x1 - x1.mean(),\n",
    "                  'x2': x2 - x2.mean()\n",
    "                 })\n",
    "# identity matrix\n",
    "Id = np.diag(np.ones(n))\n",
    "# QR factorization \n",
    "Q, R = np.linalg.qr(X[['x1']])\n",
    "P = Q @ Q.T\n",
    "x2o = (Id - P) @ X[['x2']]\n",
    "Xc2 = pd.DataFrame({'x1': X['x1'], 'x2': x2o['x2']})\n",
    "# divide by l2 norm\n",
    "Y = Xc2 / np.sqrt(np.sum(np.square(Xc2)))\n",
    "retval = Y['x2'] + (1/np.tan(theta)) * Y['x1'] + mean_return\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean return -0.0500\n",
      "vol 0.2366\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1. , 0.9],\n",
       "       [0.9, 1. ]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in order to include shorts, we need an asset with a poor expected return\n",
    "# assume I can identify bad stocks, they are highly correlated with S&P but return negative 5%\n",
    "\n",
    "n = len(data_df)\n",
    "target_corr = 0.6\n",
    "def target_corr(x1, target_corr, mean_return):\n",
    "    \"\"\"given a series x1, return a random series with correlation target_corr to x1\"\"\"\n",
    "    n = len(x1)\n",
    "    theta = np.arccos(target_corr)\n",
    "    \n",
    "    x2 = np.random.normal(0, x1.std(), size=n)  \n",
    "    # center so actual mean = 0\n",
    "    X = pd.DataFrame({'x1': x1 - x1.mean(),\n",
    "                      'x2': x2 - x2.mean()\n",
    "                     })\n",
    "    # identity matrix\n",
    "    Id = np.diag(np.ones(n))\n",
    "    # QR factorization\n",
    "    Q = np.linalg.qr(X[['x1']])[0]\n",
    "    P = Q @ Q.T\n",
    "    x2o = (Id - P) @ X[['x2']]\n",
    "    Xc2 = pd.DataFrame({'x1': X['x1'], 'x2': x2o['x2']})\n",
    "    # divide by l2 norm\n",
    "    Y = Xc2 / np.sqrt(np.sum(np.square(Xc2)))\n",
    "    return Y['x2'] + (1/np.tan(theta)) * Y['x1'] + mean_return\n",
    "\n",
    "stonks = target_corr(data_df['S&P'].values, 0.9, -0.05)\n",
    "print(\"mean return %.04f\" % stonks.mean())\n",
    "print(\"vol %.04f\" % stonks.std())\n",
    "np.corrcoef(stonks, data_df['S&P'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": [
    "data_df['stonks'] = stonks.to_list()\n",
    "labels = ['S&P', 'T-Bills', 'T-Notes', 'Baa Corps', 'Gold', 'stonks']\n",
    "\n",
    "df = data_df\n",
    "df[['S&P', 'stonks']].plot.line();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>S&amp;P</th>\n",
       "      <th>T-Bills</th>\n",
       "      <th>T-Notes</th>\n",
       "      <th>Baa Corps</th>\n",
       "      <th>Gold</th>\n",
       "      <th>stonks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rows</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>real return</th>\n",
       "      <td>0.082703</td>\n",
       "      <td>0.003059</td>\n",
       "      <td>0.018803</td>\n",
       "      <td>0.039114</td>\n",
       "      <td>0.034383</td>\n",
       "      <td>-0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>0.195024</td>\n",
       "      <td>0.038355</td>\n",
       "      <td>0.089697</td>\n",
       "      <td>0.089563</td>\n",
       "      <td>0.203529</td>\n",
       "      <td>0.236624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  S&P   T-Bills   T-Notes  Baa Corps      Gold    stonks\n",
       "rows                                                                    \n",
       "real return  0.082703  0.003059  0.018803   0.039114  0.034383 -0.050000\n",
       "vol          0.195024  0.038355  0.089697   0.089563  0.203529  0.236624"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute covariance matrix\n",
    "Sigma = np.cov(df.transpose())\n",
    "# number of assets\n",
    "\n",
    "n = Sigma.shape[0]\n",
    "# average returns\n",
    "mu = df.mean().values\n",
    "# asset STDs\n",
    "asset_vols = np.sqrt(Sigma.diagonal())\n",
    "# variable to optimize over - portfolio weights\n",
    "w = cp.Variable(n)\n",
    "\n",
    "# objectives to optimize\n",
    "# portfolio return\n",
    "ret = mu.T @ w \n",
    "# volatility\n",
    "vol = cp.quad_form(w, Sigma)\n",
    "\n",
    "z = pd.DataFrame([mu, asset_vols], columns=labels)\n",
    "z['rows'] = ['real return', 'vol']\n",
    "z.set_index('rows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_cmap = sns.diverging_palette(10, 220, sep=80, n=50)\n",
    "sns.heatmap(df.corr(), annot=True, fmt=\".02f\", cmap=my_cmap);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max return portfolio weights (return=0.1159, vol=0.0370)\n"
     ]
    },
    {
     "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>S&amp;P</th>\n",
       "      <th>T-Bills</th>\n",
       "      <th>T-Notes</th>\n",
       "      <th>Baa Corps</th>\n",
       "      <th>Gold</th>\n",
       "      <th>stonks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.25</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    S&P  T-Bills  T-Notes  Baa Corps  Gold  stonks\n",
       "0  1.25     -0.0      0.0        0.0   0.0   -0.25"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solve max return portfolio (corner solution)\n",
    "# update constraints for leverage scenario\n",
    "# sum of weights == 1 as before, net long 100%\n",
    "# remove w >= 0 constraint\n",
    "# new constraint on gross exposure <= 1.5, otherwise optimal weights are unbounded (go infinity long S&P, infinity short stonks)\n",
    "\n",
    "prob = cp.Problem(cp.Maximize(ret), \n",
    "                  [cp.norm1(w) <= 1.5,\n",
    "                   cp.sum(w) == 1]\n",
    "                 )\n",
    "prob.solve()\n",
    "wts = [float('%0.4f' % v) for v in w.value]\n",
    "maxretvol = vol.value\n",
    "maxret = ret.value\n",
    "print(\"Max return portfolio weights (return=%.4f, vol=%.4f)\" % (maxret, maxretvol))\n",
    "pd.DataFrame([wts], columns=labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min vol portfolio weights (return=0.0049, vol=0.0013)\n"
     ]
    },
    {
     "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>S&amp;P</th>\n",
       "      <th>T-Bills</th>\n",
       "      <th>T-Notes</th>\n",
       "      <th>Baa Corps</th>\n",
       "      <th>Gold</th>\n",
       "      <th>stonks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.027</td>\n",
       "      <td>1.0161</td>\n",
       "      <td>-0.0853</td>\n",
       "      <td>-0.0059</td>\n",
       "      <td>0.0446</td>\n",
       "      <td>0.0035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     S&P  T-Bills  T-Notes  Baa Corps    Gold  stonks\n",
       "0  0.027   1.0161  -0.0853    -0.0059  0.0446  0.0035"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# solve min vol portfolio (other corner solution)\n",
    "prob = cp.Problem(cp.Minimize(vol),\n",
    "                  [cp.norm1(w) <= 1.5,\n",
    "                   cp.sum(w) == 1]\n",
    "                 )\n",
    "prob.solve()\n",
    "wts = [float('%0.4f' % v) for v in w.value]\n",
    "\n",
    "minvol = vol.value\n",
    "minvolret = ret.value\n",
    "print(\"Min vol portfolio weights (return=%.4f, vol=%.4f)\" % (minvolret, minvol))\n",
    "pd.DataFrame([wts], columns=labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 473 ms, sys: 3.69 ms, total: 477 ms\n",
      "Wall time: 476 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# solve points in between\n",
    "# maximize return subject to volatility constraints between minimum volatility and max return volatility\n",
    "\n",
    "# specify a Parameter variable instead of creating new Problem at each iteration\n",
    "# this allows the solver to reuse previous work\n",
    "vol_limit = cp.Parameter(nonneg=True)\n",
    "\n",
    "prob = cp.Problem(cp.Maximize(ret), \n",
    "                  [cp.norm1(w) <= 1.5,\n",
    "                   cp.sum(w) == 1,\n",
    "                   vol <= vol_limit]\n",
    "                 )\n",
    "\n",
    "# define function so we can solve many in parallel\n",
    "def solve_vl(vl_val):\n",
    "    vol_limit.value = vl_val\n",
    "    result = prob.solve()\n",
    "    return (ret.value, np.sqrt(vol.value), w.value)\n",
    "\n",
    "# number of points on the frontier\n",
    "NPOINTS = 200\n",
    "vl_vals = np.linspace(minvol, maxretvol, NPOINTS)\n",
    "\n",
    "# iterate in-process\n",
    "results_dict = {}\n",
    "for vl_val in vl_vals:\n",
    "    # print(datetime.strftime(datetime.now(), \"%H:%M:%S\"), vl_val)\n",
    "    results_dict[vl_val] = solve_vl(vl_val)\n",
    "    \n",
    "# parallel implementation\n",
    "# NPROCESSES = 8\n",
    "# pool = Pool(processes = NPROCESSES)\n",
    "# result_values = pool.map(solve_vl, vl_vals)\n",
    "# results_dict = dict(zip(vl_vals, result_values))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>i</th>\n",
       "      <th>vol</th>\n",
       "      <th>return</th>\n",
       "      <th>std</th>\n",
       "      <th>S&amp;P</th>\n",
       "      <th>T-Bills</th>\n",
       "      <th>T-Notes</th>\n",
       "      <th>Baa Corps</th>\n",
       "      <th>Gold</th>\n",
       "      <th>stonks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.001307</td>\n",
       "      <td>0.004882</td>\n",
       "      <td>0.036158</td>\n",
       "      <td>0.027162</td>\n",
       "      <td>1.016016e+00</td>\n",
       "      <td>-8.529612e-02</td>\n",
       "      <td>-5.869646e-03</td>\n",
       "      <td>4.456847e-02</td>\n",
       "      <td>0.003419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.001487</td>\n",
       "      <td>0.024441</td>\n",
       "      <td>0.038557</td>\n",
       "      <td>0.181390</td>\n",
       "      <td>9.514139e-01</td>\n",
       "      <td>-6.485151e-02</td>\n",
       "      <td>-5.780935e-03</td>\n",
       "      <td>5.766434e-02</td>\n",
       "      <td>-0.119836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.001666</td>\n",
       "      <td>0.032550</td>\n",
       "      <td>0.040815</td>\n",
       "      <td>0.245344</td>\n",
       "      <td>9.246206e-01</td>\n",
       "      <td>-5.630277e-02</td>\n",
       "      <td>-5.804070e-03</td>\n",
       "      <td>6.308995e-02</td>\n",
       "      <td>-0.170948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.001845</td>\n",
       "      <td>0.038746</td>\n",
       "      <td>0.042954</td>\n",
       "      <td>0.291347</td>\n",
       "      <td>8.911584e-01</td>\n",
       "      <td>-4.209410e-02</td>\n",
       "      <td>-7.823779e-08</td>\n",
       "      <td>6.749468e-02</td>\n",
       "      <td>-0.207906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.002024</td>\n",
       "      <td>0.043822</td>\n",
       "      <td>0.044992</td>\n",
       "      <td>0.328672</td>\n",
       "      <td>8.497523e-01</td>\n",
       "      <td>-1.337351e-02</td>\n",
       "      <td>1.522011e-10</td>\n",
       "      <td>7.157572e-02</td>\n",
       "      <td>-0.236626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>195</td>\n",
       "      <td>0.036253</td>\n",
       "      <td>0.115449</td>\n",
       "      <td>0.190403</td>\n",
       "      <td>1.241102</td>\n",
       "      <td>4.609381e-09</td>\n",
       "      <td>8.645966e-09</td>\n",
       "      <td>1.585831e-08</td>\n",
       "      <td>8.898107e-03</td>\n",
       "      <td>-0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>196</td>\n",
       "      <td>0.036432</td>\n",
       "      <td>0.115557</td>\n",
       "      <td>0.190873</td>\n",
       "      <td>1.243342</td>\n",
       "      <td>4.244799e-09</td>\n",
       "      <td>7.945236e-09</td>\n",
       "      <td>1.440312e-08</td>\n",
       "      <td>6.657717e-03</td>\n",
       "      <td>-0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>197</td>\n",
       "      <td>0.036612</td>\n",
       "      <td>0.115665</td>\n",
       "      <td>0.191341</td>\n",
       "      <td>1.245572</td>\n",
       "      <td>2.983536e-09</td>\n",
       "      <td>5.491124e-09</td>\n",
       "      <td>9.582897e-09</td>\n",
       "      <td>4.428005e-03</td>\n",
       "      <td>-0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>198</td>\n",
       "      <td>0.036791</td>\n",
       "      <td>0.115772</td>\n",
       "      <td>0.191809</td>\n",
       "      <td>1.247791</td>\n",
       "      <td>1.003553e-09</td>\n",
       "      <td>1.735325e-09</td>\n",
       "      <td>2.769440e-09</td>\n",
       "      <td>2.208817e-03</td>\n",
       "      <td>-0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>199</td>\n",
       "      <td>0.036970</td>\n",
       "      <td>0.115879</td>\n",
       "      <td>0.192276</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>-1.368352e-10</td>\n",
       "      <td>2.660760e-10</td>\n",
       "      <td>8.317431e-10</td>\n",
       "      <td>7.645281e-10</td>\n",
       "      <td>-0.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       i       vol    return       std       S&P       T-Bills       T-Notes  \\\n",
       "0      0  0.001307  0.004882  0.036158  0.027162  1.016016e+00 -8.529612e-02   \n",
       "1      1  0.001487  0.024441  0.038557  0.181390  9.514139e-01 -6.485151e-02   \n",
       "2      2  0.001666  0.032550  0.040815  0.245344  9.246206e-01 -5.630277e-02   \n",
       "3      3  0.001845  0.038746  0.042954  0.291347  8.911584e-01 -4.209410e-02   \n",
       "4      4  0.002024  0.043822  0.044992  0.328672  8.497523e-01 -1.337351e-02   \n",
       "..   ...       ...       ...       ...       ...           ...           ...   \n",
       "195  195  0.036253  0.115449  0.190403  1.241102  4.609381e-09  8.645966e-09   \n",
       "196  196  0.036432  0.115557  0.190873  1.243342  4.244799e-09  7.945236e-09   \n",
       "197  197  0.036612  0.115665  0.191341  1.245572  2.983536e-09  5.491124e-09   \n",
       "198  198  0.036791  0.115772  0.191809  1.247791  1.003553e-09  1.735325e-09   \n",
       "199  199  0.036970  0.115879  0.192276  1.250000 -1.368352e-10  2.660760e-10   \n",
       "\n",
       "        Baa Corps          Gold    stonks  \n",
       "0   -5.869646e-03  4.456847e-02  0.003419  \n",
       "1   -5.780935e-03  5.766434e-02 -0.119836  \n",
       "2   -5.804070e-03  6.308995e-02 -0.170948  \n",
       "3   -7.823779e-08  6.749468e-02 -0.207906  \n",
       "4    1.522011e-10  7.157572e-02 -0.236626  \n",
       "..            ...           ...       ...  \n",
       "195  1.585831e-08  8.898107e-03 -0.250000  \n",
       "196  1.440312e-08  6.657717e-03 -0.250000  \n",
       "197  9.582897e-09  4.428005e-03 -0.250000  \n",
       "198  2.769440e-09  2.208817e-03 -0.250000  \n",
       "199  8.317431e-10  7.645281e-10 -0.250000  \n",
       "\n",
       "[200 rows x 10 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_df = pd.DataFrame(enumerate(results_dict.keys()))\n",
    "ret_df.columns=['i', 'vol']\n",
    "ret_df['return'] = [results_dict[v][0] for v in ret_df['vol']]\n",
    "ret_df['std'] = [results_dict[v][1] for v in ret_df['vol']]\n",
    "for i, colname in enumerate(labels):\n",
    "    ret_df[colname]=[results_dict[v][2][i] for v in ret_df['vol']]\n",
    "ret_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_efrontier(ret_df, df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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36vp7bRPXHVVtsyX3I4FVqrpaVctwHqs5s8YyZwLPq2M2kCr/69c6BzgNeLI1g26kJh+jiCTj9PL1FICqlmngA3y0pmb9HnE6YYoTp/vVeGBTawXeCA0eo6puU9W5OF2xNmrdENHkY1TVzeo8Soeq7gVW4FxAQ1Fzfpdhc92p6xjD6bpT3++RtnHdabPJPRtY7/d5AwdfEOpb5iHgzzjP1Iaq5hxjL2A78IxbBfikOJ2LhJomH6OqbgTux+l2dTOwW1U/CWKsTRXIMQZj3dbUInGKSA+c0em+apmwWlxzj/MhwuO6U5dwuu7Uqg1dd9pscq+vi816lxGR04Ftqjq/5cNqUU0+RpxvlkOAsao6GKfb0VC8X9uc32MazrftnkAXIEFELmzh+FpCIMcYjHVbU7PjFJFE4C3gGq29R71Q0OTjDLPrTl3C6bpT+4pt57rTZpP7BqCr3+ccDq4aqWuZkcAZIrIWpzrmRyLyYvBCbbLmHOMGYIOqVpeA3sT5pws1zTnGH+MMOLJdVctxBis5OoixNlUgxxiMdVtTs+IUkSicxP6Sqr7dwrG1pOYcZzhdd+pbN1yuO3VpK9edNpvc5wJ5ItJTRKJxBqCYWGOZicDFbmvrETjVJ5tV9RZVzVHVHu56X6hqKH7zas4xbgHWi0hfd7kTgeWtFnngmnyMONViI8Tpz1xwjnFFawYfoECOMRjrtqYmx+n+7p4CVqjqg0GMsSU0+TjD7LpTqzC77tSlrVx32mZredX9rai/xWn1eJs7bTQw2n0vwBh3/hJgWC3bOJ4QbbXa3GMEBuEMBboYmACkeX08QTjGe3AGHlmKM8xpjNfH08Rj7IRTmtgDFLjvk+taNxRfTT1GnOFi1f07Xei+TvX6eILxu/TbRlu/7tT39xou1536jrFNXHesb3ljjDEmzLTVanljjDHG1MGSuzHGGBNmLLkbY4wxYcaSuzHGGBNmLLkbY4wxYSask7uIXO51DMHWHo4R2sdx2jGGBzvG8NDWjzGskzvQpn85AWoPxwjt4zjtGMODHWN4aNPHGO7J3RhjjGl32lwnNhERERoXFxfQshUVFURGRgY5Im+1h2OE9nGcdozhwY4xPITaMRYVFamqBlwgD53IAxQXF8e+ffu8DsMYY4xpNSJS3JjlrVreGGOMCTOW3I0xxpgwY8ndGGOMCTOW3I0xxpgwY8ndGGOMCTOW3I0xxpgwY8ndGGOMCTOW3I0xxpgwY8ndGGOMCTNtroc6Y5qqHFgFVHodiDEmrHUHkjyOwZK7CVsFwJfADGAmMBdoVP+NxhjTBBOAMz2OwZK7CRtrcJJ4dTJfBrStYZGMMaZlWHI3bVIlsJADk/kmLwMyxpgQYsndtAmFwCz+l8y/cqcZY4w5mCV3E5I28r8S+QxgMdYQzhhjAmXJ3YSMEuB54BGc++XGGGOaxpK78dw2YAwwFtjucSzGGBMOLLkbzywDHgReAko9jsUYY8KJJXfT6j7BSeofex2IMcaEKUvuplWU4pTQ/wMs9TgWY4wJd5bcTVDtwLmXPgbY6nEsxhjTXlhyN0GxEqeU/gLW5asxxrS2oI0KJyJPi8g2Eam3FlZEjhCRShH5ZbBiMa3nC+B0oD8wHkvsxhjjhWAO+foscHJ9C4iID/gn1raqTSvHeT59MHAi8AHWp7sxxngpaNXyqjpNRHo0sNhVwFvAEcGKwwTPLmAc8F+sX3djjAklnt1zF5Fs4CzgRzSQ3EXkcuBygOjo6OAHZ+q1Cud++nPAPo9jMcYYczAvG9Q9BNykqpUiUu+Cqjoe5xYuCQkJVuPrkWnAA8D7QJXHsRhjjKmbl8l9GPCqm9gzgFNFpEJVJ3gYk6mhAngdp6Q+z+NYjDHGBMaz5K6qPavfi8izwPuW2EPHTuAJnOfT13scizHGmMYJWnIXkVeA44EMEdkA3AVEAajquGDt1zTPUpxR2V7EHmMzxpi2Kpit5c9vxLKXBCsO07Aq4D3gYWCyx7EYY4xpPuuhrh3bDTyF8yjbGo9jMcYY03IsubdDK4FHsUfZjDEmXFlybycU+BCn6v1TrAc5Y4wJZ5bcw9xenH6AHwW+8zYUY4wxrcSSe5j6HiehPwPs8TgWY4wxrcuSe5j5FOdRtklYL3LGGNNeWXIPA0U4o7I9Ciz3OBZjjDHes+Tehq3DeYztKZwR2owxxhiw5N4mTcGpep8IVHobijHGmBBkyb2NKAFewknqiz2OxRhjTGiz5B7iNgCP4Yx3m+9xLMEWoUqulnNo5Q6iqKCcSMrERzk+ysVHGRGUi49yItz3EZQhlBNBOUIZ/O9nA8MIG2NMOLPkHqJm4pTS38YZdjXcZGgVA6sKGFi+lgElCxlYNJlDCz8grqrlWg9UEENZRALlEQmUSxxlEfHkR/ViZtLZTIs7mulROWwQ+xcwxoQfUW1bfZUlJCTovn3h2WlqGfAqTlKf73EsLSVGlUO0mIEVmxlQupyBxV8ysPADOpUt8To0ANbEHcP05IuYFn8c06N78q1Eex2SMaaNmwCc2cLbFJEiVU0IeHlL7t7bAowFHge2ehxLc3TTCgZW5jOgbBUDS+YxcN/n9Nn3CZGUeh1awLZGH8r0lEuZnnAi06L7sFjiqLIqfmNMI0zAknujhVNyn4vT1/vrQLnHsTRGkioDqvYyoGI9A0uWMLB4GgMK3yelYr3XobW43ZFdmZlyCdMTT2Fa9KHMi0iizJK9MaYeE7Dk3mhtPbmXA2/iVL3P9jiWhvhUydUyBlZuY2DZNwws/ooB+z6iR/FMpJ0OPVMckcZXyRczPelnLInuwypfOt9LHHss4RtjXBOw5N5obTW5b8epdh8LbPI4ltpkahUDq3YxsHwtA0u+ZsC+yRy67wNiq3Z7HVqbsD26H6vijmFV7FC+j+nHqsjurIrMYJUkkC8RXodnjGlFE7Dk3mhtLbkvxKl6fwVC4s5zjCr9tZiBFZvcBm4zGVj4AR3LlnkdWtjaHdmVVXHHsiruCL6P6c/3kT3YFxHjdVjGmKAQbiyOY2hyZstu1ZK79yqBd3Cq3qd7GEd3rWBA5Q4Gln3PwJK5DNz3GXn7PmtTDdyMMabtiIL3joQ+N8KZLVt2b2xyt4d8W9BO4AmcTmd+aMX9JqtyWNVeBlasZ2DJYgYUTw/bBm7GGBOSJBke7Q1jZsKEG72OxpJ7S1iKU0p/ESgO4n58quRpGQMrtzKg7FsGFs9mYOFH9CiZGcS9to4qyWRvWQ+27OzC2o3pRERAUkIpCXGlJMQWExdTQkx0EdGRRUT5ioiUfURQCFqI2OC2xhgvSSe4IQne/9rrSPaz5N5EVcB7OEn9iyBsP0urGFDdwK34awYWTaZ/G2/gphJPSWVPtu/JYd2mLJZ+14Gvvo5n9oJ4NCqBDp2VmIxCKhK2USXlCNGIpiIagRABGoFoBBCBVLmfERITKkhNLiMlsYKUxHKSE8pJiisnKb6cxLgyEmIrSIgpIz66jIy4IrJjviWiaqfXp8MYEw40F84vhIXfeR3JAYKW3EXkaeB0YJuqHlbL/F8BN7kfC4Hfq+qiYMXTUnbjDLE6BljdAtvzb+A2sHQZA9we3NpqAzfFRwVd2VXUjY3bOrFyTQbzlyTy5dxYNuUnk5ntIz6zGE3Zwd6YtWxJXcaW476Fuh6tC+QJs0qcMW8D7LnWJ8L5h/fjogEdOTKrgBSWI22qpwFjTEgoHQynfg+b9ngdyUGC1qBOREbhJO3n60juRwMrVHWXiJwC3K2qwxvarlcN6r7BKaU/BzR17921goGVOxiwv4Hbp/TZ9ym+NphYqiSDvWU92bKzC9//kMmiFcnMnBvPslWJJKfHkNypnIi0AkriN7A9YiUbKhdTriVeh12rjokJ/OHI/vw8L45+yT8QXbXW65CMMaEufySc9BUU1zL6x4QJnjeoC2preRHpAbxfW3KvsVwasFRVsxvaZmsmdwU+xEnqn1Bn2fIgyQf04LaYgcXTOKzwgzbXwK26Gn3HnhzWbu7Ism9T+WphIrMXxKGRCaR1UWLS91KeuIVdUavYWLWIPVXbvQ672UZ2z+aKoT05oWsFXaJWEKFt91aIMSYIvjkOfj617vkhkNxD5Z77b3HyaK1E5HLgcoDo6OAP7LEXeBZ4FKjvLop/A7eBZd8woPirNtfArboavaCoKxu2d+ab1enMX5LEzLmxbN6ZTEa2j/iMYjRlu1ONnrKMLaO+46CvOmE0dN3MdRuZuW4jAFG+CH49+FAuODSDoRk7SWI5QqXHERpjvOGDz46Gq+pJ7CHC85K7iJyA8/TYMara4JDlwSy5f4+T0J8Bat5Byaruwa1sjTtE6Rf03zepzTRwO6AafX0Gi1akMmteHEu+TSQ5I4bkjuX40gooilvPDt9K1lcspsKehz9I15Rk/ji8Hz/rHUPvhE1E6+p22xWvMe2KJMCTh8D98xpetr2X3EVkIPAkcEogiT1YPsWpep8ERLsN3Aa4DdwGFs1kwL5JbaKBW23V6HMWJjBrQTxVkQmkd1Gi91ejL2Jjj0Xs6VajGl0Jq1J4S1u/ew83fzKHm93PwzMGc37/XI7OgdwO60mNXYJoaHeyZIxpJMmE29LhrQASe4jwLLmLSDfgbeAiVf3WqzjeL1vL7IoNXFgyj3+1gQZuSgQVdDugGn3B0iRmzKmjNXrKMraMqqU1epgl8ChfFBlJGaQnppOWlEZqYirJickkJSYRnxhPfEI8MQkxRMZEUlVZRVVFFZXllc6ropKKigoqyiv2/yyvKKe83H1VlFNWVkZZRRmlZaWUVZRRVl7G5l2bmZu/iK+mOc+2pvmyGcAFHJvdgWHddjO4z1qyOywlUjd4fHaMMU3XEy4ph9krvQ6kUYLZWv4V4HggA2eY8ruAKABVHSciTwJnA+vcVSpUdVhD223xavntd0L+X1tuey3EqUbvwdad2aw6oBo9gaQOsaR0rsCXuovi+I1ONXrlopBtjd4UIkJafBrpSel0SOxAWlLa/mSdmJBIXGIccYlxRMdHE5UQRWScN99Tq8qrKN5ZzJ7te8jfns/m7Zv5YesPrNu2nh6Vo0jbcSQpZT6OG7KN44/cRJ+u35AQucwevTOmLSgfCD/7AdYVNG69EKiWt77lPUzuTjV6D3bs6cq6zVks/S6NOV8nMGtBHFW+RDp0cTp1KU/Ywu7o79lQtZjdVVs8iTWYuqR24ZCuh9Ajuwcdu3QktXMqUYlRiK/tDqOqqpTtLmNf/j52bt9J0aZoilZ2Ze3cKFYv38mww8s4vH8p/XoX0TNnD50zd5GevIPEmC1EstGSvzFeKxgBJy2AwrLGrxsCyT1UWsuHLacavSsFRd3ZsL0T36xJZ8GSJGbMiWPj9kQycyJJyCpBk3dQGOt06rJ51DeEazV6j4we9M3pS/ec7nTs3JHkLslEJUR5HVaLExFiUmOISY2hQ+8O+6ePoILK3R34/KZ4/vHID0ACcODoUT6fMuCQKoYOrODQPsXk9iikW+fdZKXlk5KwlVjfZiLUetgzJmhWHwenTw38+ecQZMm9hfyvGr0L36/PZNGKFL6cF8/ibxJITo8luVMFkWkFFMdvJN+3kPWDF1GmRQcOMBNGT1hFSAS9O/amb05fumV3I6tLFkmdk/DF+rwOzXO+lAp+Mm4PuSNyGXfzGsrKDvzFV1YKC5f6WLjUB8QAqUDOActkpitHDq7k8ENL6dd7Hz1z9rql/+0kRG8hkk1W+jem0SJg5jFwWeg/6tYQq5ZvRLW8SpzTGn1vDus2dWTZd6nMWZjIzHnV1egQm1FIReIWCqJXs1EXUVC5ueViDVFRvij6dO5DXk4eXbO7ktk5k8ROiURER3gdWsgrXJjM+EsL+GFdy3Zf6fMphx+qDBlQXqP0v4OU+G3E+DYRoQH212tMeyCx8OLhcO9Xzd+WVcuHnprV6N+u6cD8pcnMnBPH+m2JZGVHkpBZAqn5FMauY2vKMjYet4JwrUavKTYqlkOyDyE3J5ec7BzSO6cTnxVPRKQl8qZIHLSHqyfH8v6VSUyetLHFtltZKSxYLCxYHENdpf+OmcoRgyo4/NAy+vbyL/1vIyFmC5G62Ur/pn2QDnBvF3ixBRJ7iGj3JfdvFjzP6hXTWLwihZnz4li0IpHk9FhSOlUQmbab0oSN7PB9w/rKRZS2s+eXk2KTOCT7EHrn9CY7O5sOnTsQlxHXphu6hSpVZeV/M3jyL6uprAyN/8nq0v/QgeUc2reYvO6F5HQqcO79x28jxreRCC3wOkxjmqkbXCEwbV3DiwbKSu7em7Q0kbemxrE7egUbeyxmV9capad20qlLh4QOHJJzCL1yetGlSxfSuqQR0yEGEUvkrUFEOOSqfO4Y1p2xv93O1i3ef5EMpPTfuaMybFAFhx9SRr/cffTI3kPnjF10SN5OYsxmfLoZaQ//QKZtquwPv9gG3+7wOpIW1+6T+66UhcyM+G+7SODVOiZ35JCuh9Azpyedu3QmpXMKMakxXodlgNSjCrhhSiJvXZ7E7Gmh/9jj5q3Cex9H8d7HUTgt/7MOmO/zKYMPq2LIwAoO61tM7+576dqpgMzUfL97/wVehG7au8Ij4adLYGex15EERbtP7uGuW4du9O3alx45PZxHzzonE50U/MF3TNNFZZVy7lvQ95+9eP6B1bSxO2cHqKwU5i3yMW+Rf8v/rgcsk91ZOWJQJQMOKaFvryJ65OyhS8ZOOiTtIMFK/yYYNh4Lp8yE8iqvIwkaS+5hQkTondWbPjl96J7dncwumaR0TsEXZ4+etUXig8G37qTr8J6MuXwzu3aFT++DNW3cLGzcHMmEDxOBRGqW/qOilEGHVbkt/0v2l/6z0naSHLeFWN8mxIblNQERmDcKLmr7j7o1xJJ7GxQZEUle5zz65PSha5euZGZnkpSVRESstVgPNxkn7uKWKR146TflLJq/veEVwlB5uTD3ax9zv/YBsdRV+h92eAUD3V7/nHv/O+mQvJ2E6M34dIuV/tu9aHhrKNwe/okdLLmHvJjIGPpl9yM323n0LKNLBglZCUREWSJvL6K7FnHJBxHMuqsnrz++xutwQpJT+o/i3Y+iqKv0P3hAFYMPc0r/udX3/tPySY7baqX/cCcp8EAPeGKW15G0GkvuISQhJuGAR8/Su6QTnxFvj54ZJLqKo/+xix7De/PYn9ZT2JT+rtux8nJhzgIfcxbUXfrv2sVp+T+wf6lz7z97N50zdpKW5PT659z7D6NuJNsLyYY/xcIni7yOpFVZcvdIanwq/XP60yunF527dKZDlw7EdohFIiyRm7p1+Xk+tx2WyXOXFrNymfUv35LWbxLWb4rinUl1l/6HDlSGDizjkLxicrvvJafj/0r/MRGbEG3ZngbDiRIFEo0ShRKDEkWVRqMaSaVGU1UVRaVGUlkVSWVlFJVVPioqI6mojKS8IoqKSh/lFc6rpDSa0jIflVURlJVFUFLmo7TM576PoLQ0gpLSCDJiI/nVCy8S++33Xh9+q7Pk3goykzLp37U/PXN60qlLJ9I6pxGTZo+emaaJy93H5R/7mHxjd957pQU73jD1Ki8XZs8XZs+PxSn9pwHdDljGv/Tfr9c+euTsoVP6TjokbSM+eot777/lSv9KJEgMSnXSjEI1miqNokqdZFlVFUllVTQVVZFUusmyojKSyiof5RWR7stHebmPsopIN0n6KC2PcN6XO8myuMRHiZs0S8uE4pIIikuEfUURFBULRcVCSYmwrxj2FQn7imD3HqGkFIqLW7/QMrBPMp/l/4fY/E2tvu9QYMm9heWk5dCva7/9w5emdE4hOtkePTMtKyK+khPH7Kb3UbmMvXENpaVWXRwKDi79dzxgfkyMMvgwZcjAMnp1KyNCqqjSCEpKhdLSCIpKhJISJ1lWJ859RVBU7PwsLnES595C2FcEqlbTV5vB/VL4dOv9pO9quS6d2xpL7k0kIvTM6Ln/0bOO2U4i98Xbo2em9fS4cAd3DurCE5fsYe1qaxAW6kpLa5b+TUsbfmgKH62/j9Q9W70OxVOW3APgi/CR2zGXPjl96JbdjcwumSR1suFLTWhIOGwvV30RzYdX5fDZexu8DscYz4wckMKHa/5KUqG1R7HkXkN0ZDR9O/clLyePnOwcMrtkktDRHj0zoc2XXM7pz5WTNzaXJ+5aTUVF+Pa8ZUxtThiUwnvf3kNCUYHXoYSEdp/cu2R04ZxjzyE7O5uMzhnEZ8YjkXYfy7RNfX+/gzuHdWPspTvYvKnQ63CMaRUnDUnh3eV3Eley1+tQQka7T+6HDzmc0v6lXodhTItJPqKA66bEM2F0EjO/2Ox1OMYE1WlHpPDWotuIKSvyOpSQ0mBds4i8EMg0Y0zoiMoo45evF/Obm3tjo/aacHXW8GTeXniLJfZaBHIj+VD/DyLiA4YGJxxjTEuRCBj453xuf7Mn6elxXodjTIs656gkXp93E9Hl4TuoUnPUmdxF5BYR2QsMFJE97msvsA14t6ENi8jTIrJNRJbWMV9E5BERWSUii0VkSJOPwhhTp/QTdnHTlBSGDs9qeGFj2oCLRiby8lc3EVlZ7nUoIavO5K6q/1DVJODfqprsvpJUNV1Vbwlg288CJ9cz/xQgz31dDoxtRNzGmEaIzi7hwonlnP/HXl6HYkyz/PaYeJ798s/4qmyUv/o0WC2vqreISLaIHC0io6pfAaw3DajvYcMzgefVMRtIFZHOgYdujGkMiVKG/3UnN7/Qi+Rk6/7YtD1/GBXLEzNuIkLtUc+GNNhaXkTuA84DlsP+TpEVmNbMfWcD6/0+b3CnHdS8V0QuxyndEx1tXbka0xydTtvJrf0zeP6SEpYvyfc6HGMCcs2oaP4z7Wavw2gzAnkU7iygr6q29PNitbXh1doWVNXxwHiAhISEWpcxxgQutuc+LvvIx7RbejDh+bVeh2NMvW46zsd9U2/1Oow2JZDW8quBqCDsewMHDqicA7TP4XuM8UBEXCXHP1TA9WN7ExfX7ru8MCHqruPgvql3eB1Gm1Pnf7SIPIpTki4CForI58D+0ruq/qmZ+54IXCkirwLDgd2qaj1uGNPKup6bzx0DO/PUJXv5/rsCr8MxZr+/H1fBLVPv9TqMNqm+r+vz3J/zcRJxo4jIK8DxQIaIbADuwq0BUNVxwCTgVGAVzheISxu7D2NMy4g/ZC9/+DSST6/rykdvr294BWOC7IHjSrhu6n1eh9Fm1ZncVfW55mxYVc9vYL4Cf2zOPowxLceXXMHJT+6l9/DejL99DeXl1iLZtD4ReOTYQq6cer/XobRpgXQ/u8TtZMb/NV1E/iMi6a0RpDGm9eT9Lp87P+xKTtckr0Mx7YwIPH7MLq6cZom9uQJpUPch8AHwK/f1HjAd2ILTUY0xJswkDdnNNVPiGPWTLl6HYtqJiAjhmaO387vpD3sdSlgIpInsSFUd6fd5iYjMVNWRInJhsAIzxngrMq2Ms14uo89DvXn676upqrKnUE1wREZG8PwRGzh/5nivQwkbgZTcE0VkePUHETkSSHQ/Wv9/xoQxiYDDrsvn9nd6kJEZ73U4JgxFRUXw6rA1nD/LEntLCiS5XwY8KSJrRGQt8CTwOxFJAP4RzOCMMaGhw7HO4DNHHN3R61BMGImJ8fHWoG85e/YzXocSdhqsllfVucAAEUkBRFUL/Ga/HqzAjDGhJapzMRdMEPr+rRcvPrza63BMGxcbG8k7hy7h5LmveR1KWKqvE5sLVfVFEbmuxnQAVPXBIMdmjAkxEqkMu2sn3Y7sxZg/bGT37pbuldq0B/HxUbzXZz4/mv+216GErfqq5RPcn0l1vIwx7VTWKTu5dUo6AwZleB2KaWOSkqL5KHc2P1poiT2Y6uvE5nH35z2tF44xpq2I6V7EpZMimHl7D956eq3X4Zg2ICU5mo+6TmXE4o+9DiXsBdKJTR8R+VxElrqfB4rI7cEPzRgT6iJiqzj2/gJuGJ9LfHwwxpcy4aJDWiyfd/mMEcsssbeGQFrLPwHcApQDqOpinPHdjTEGgJxf7uD2zzqS1zfN61BMCMrMiOOLzA8YuvILr0NpNwJJ7vGqOqfGNHu+3RhzgPh+hYz+1Mep/9fN61BMCOmUFc+UlHc4/NvpXofSrgSS3HeISG+c4V8RkV8CNjSrMeYgvsQKfvL4Hq56oDfR0T6vwzEey+4Uz9T41+j//WyvQ2l3AknufwQeB/qJyEbgGmB0MIMyxrRtvS/N546PsunWPdnrUIxHumcnMC3qRfqsne91KO1SncldRB4Skf8DSlX1x0Am0E9Vj1HVda0WoTGmTUoatIerJ8dy/CnZXodiWlmvbolM5Rl6rV/sdSjtVn0l91XAWcBMt9vZJ4CLRWSwiARS4jfGtHO+1DLOfLGQy+/ujc8nXodjWkGfHklMK3uc7huXex1Ku1ZnklbV/6rqBaraAzgKeBvoDbwJFLRKdMaYNk9E6P+nfO6Y2J2sjjb4TDjr3zuZqfv+S/aW77wOpd2rtwQujoHAme7rOOA74IFWiM0YE0ZSjyrgxinJDD+2k9ehmCAY2CeZKQX/odP2NV6HYqi/b/lPgWRgITAb+LuqrmiluIwxYSiqYwnnvQ39/tmL5x9YjdoQ8WFh6CEpfLL5X3QosAepQkV9JffVOI+/5bmvXBGxjqSNMc0iPhh8605ue60XaWmxXodjmmnEYSl8vvHvlthDTH333K9Q1RHAz4EpwFDgRRGZLyLPtU54xphwlfHjndwypQOHD830OhTTRMcOTOGTtX8lZc92r0MxNQTS6r0UKAKK3fc5wJBgBmWMaR+iuxbx6/crOefynl6HYhrpR4NT+Oi7u0gq3Ol1KKYW9T3n/h8R+QqnN7q/4Azz+jjQV1UHBLJxETlZRL4RkVUicnMt81NE5D0RWSQiy0Tk0iYehzGmjYqIqeLo+3bx56d7k5Bgg8+0BT8dmsL7K+4kvniP16GYOtTZoA5YA7wEfK2qlY3dsIj4gDHAScAGYK6ITFRV/4cf/wgsV9WfiUgm8I2IvKSqZY3dnzGmbevy83xuP7Qjz15axDfLrTQYqn52ZApvLLyNmLIir0Mx9ajvnvsjqjqvKYnddSSwSlVXu8n6VZzH6Q7YDZAkIgIkAjuxQWmMabfi8gq54uMIfnZed69DMbU4e0Qyby242RJ7GxDMnuaygfV+nze40/z9FzgE2AQsAa5W1aogxmSMCXERCRWc+Nhurnm4NzExNvhMqDjvqCRenXsTURWlXodiAhDM5F5bX5M1n2r9Kc5z9F2AQcB/ReSgkSZE5HIRmSci8yoqrGBvTHvQ46J87vwkm+49bfAZr108MpGXZt9IZGW516GYAAWU3EXkcBG50n0dHuC2NwBd/T7n4JTQ/V0KvK2OVTj3+fvV3JCqjlfVYao6LDKyvmYCxphwkjBgD3+aHMOPT8/xOpR263fHxvPszBuJsErVNqXB5C4iV+M0rMtyXy+KyFUBbHsukCciPUUkGjgPmFhjmR+AE939dAT64nSeY4wxAPiSyzntub2M/msukZE2ZlVrunJULI9Pvwk5qNLVhLpA/lN+CwxX1TtV9U5gBPC7hlZS1QrgSuBjYAXwuqouE5HRIlI9HvxfgaNFZAnwOXCTqu5oyoEYY8KXiNDvjzu44/2udOqc4HU47cJ1x0Xx6LSbLbG3UYHUcQvg32K+ktrvpx9EVScBk2pMG+f3fhPwk0C2ZYwxKUfu5oYpibw9OpkvJ1t3p8Fyy3ER/H3qbV6HYZohkJL7M8BXInK3iNyNM4jMU0GNyhhj6hCZWcr/vV7MpX/ujdgQ8S3u7uOUv0+90+swTDM1mNxV9UGchm87gV3Apar6UJDjMsaYOokPDr85n9ve6El6epzX4YSNfxxXzl1T7/E6DNMC6ut+Ntn92QFYC7wIvACsc6cZY4ynMn60i5umpDLkyCyvQ2nzHjyumJun/s3rMEwLqa/k/rL7cz4wz+9V/dkYYzwXnV3MhRPLOe/3NvhMU4jAmFF7uXbqP70OxbSgOhvUqerp7k/7jzHGhLSIaGXE33bRY0Rvxly5nr17bXiKQERECI8fnc9l0x7xOhTTwupM7iJS77Cuqrqg5cMxxpim6/SzfG47NJPnLy1h+ZJ8r8MJaT6f8PSIrVw84zGvQzFBUN+jcA/UM0+BH7VwLMYY02yxvfZx2Uc+pt7UnXdfXOd1OCEpMjKCF4/4gXNnPul1KCZI6quWP6E1AzHGmJYSEVfJCY/spvdRuTx2/VpKSmxMimrR0T5eHbyKs2Y953UoJogC6X42SkT+JCJvuq8rRSSqNYIzxpjm6Hb+Du74rDO9clO8DiUkxMT4eOvwlZz1lSX2cBdIJzZjgaHAY+5rqDvNGGNCXkL/vfzxsyh+elbXhhcOY3FxkUw8dDGnz33J61BMKwik+9kjVNV/JLgvRGRRsAIyxpiW5kuu4JSn9pI7vDfj71hDeXn7GuEsISGK9/LmccKCd7wOxbSSQErulSLSu/qDiPTiwL7mjTGmTci7PJ87PuhKdk6i16G0muTkaD7uNZMTFlpib08CSe43ApNFZIqITAW+AK4PbljGGBMcycN2c92UBI49qYvXoQRdakoMn+ZMZuSSSQ0vbMJKfd3P/p/7djWQB/zJffVV1cmtEJsxxgSFr0Mpv3iliMtu601ERHiOPpPeIZbPO3/Ckcs/9ToU44H6Su63uD/fUtVSVV2sqotUtbQ1AjPGmGCSCDjs+nxuf7sHGRnhNfhMZkYcX6S/z5CVVg5rr+prUJcvIpOBniIyseZMVT0jeGEZY0zr6DBqF3+eksLrv0tm3qytXofTbJ2y4vk86W36fzfb61CMh+pL7qcBQ3BGgquvtzpjjGnToruU8KsJQt97e/HSo6u9DqfJcjon8EX0K+R9b72Dt3f19VBXBswWkdtUdar/PL/78cYYExYkSjninp10G96Lx/64kd2729YdyB45iXzBc/Rct8TrUEwICKS1/M21TLullmnGGNPmdTx1J7dMTufQgelehxKw3O5JTK16ip4bLLEbR32jwp0CnApki4j/eIDJgHXUbIwJW7E9ivjtRxHMuLUHbz+71utw6tW3ZxJfFI2ly9ZVXodiQkh9JfdNwDygBJjv95oI/DT4oRljjHciYqsY9WABNzzem/j40BxO49DcZKbufdQSuzmIqGrdM0V8wPOq+qvWC6l+CQkJum/fvhbb3qziWcwpmdNi2zPGhJ+ilYk8fUkhq74t8DqU/Qb1TeHT7Q+QsXODJ/svT0tjw913U5KbCxGB3OFtRzIzIT6+SavGxsaSk5NDVNSBXyhFpEhVEwLdTr19y6tqpYiki0i028CuUUTkZOBhwAc8qar31bLM8cBDQBSwQ1WPa+x+jDEmmOL7FfKHTyP59PpufPjmD16Hw7BDUvhk0z9J273Fsxg23H03SUceSY/ISMKzG6Bm6N0bUlMbvZqqkp+fz4YNG+jZs2ezQghk4Jh1wEz3Wff9RWZVfbC+ldxS/xjgJGADMFdEJqrqcr9lUnFGmjtZVX8QkazGH4IxxgRfRFIFPx2/h9wRuTx+6xrKyrwZYuOow1L4aN3fSN67w5P9VyvJzbXE3sJEhPT0dLZv397sbQVSl7IJeN9dNsnv1ZAjgVWqutot9b8KnFljmQuAt1X1BwBV3RZo4MYY44Xev9nBHR/m0LVbIJfBljXq8BQ+WfMXzxM7ABERltiDQKRlzmqDJXdVvcfdYZLzUQsD3HY2sN7v8wZgeI1l+gBRIjIF5wvDw6r6fIDbN8YYTyQN3s01U+J47w/JTPloY6vs88TBKUxceRfxxXtaZX9twd+efpqXP/oIn89HhAiP33orRx56KFf8/e/MWLQIX0QE42+9laMGDty/To8zziApPp6IiAg6dujA83ffTaeMDA+PIjgaTO4ichhOL3Ud3M87gItVdVlDq9YyrWbrvUhgKHAiEAfMEpHZqvptjRguBy4HiI6ObihkY4wJOl9qGWe+VErew7k8de/3VFXV3Ti5uU4elsI7S24ntrTlGhO3uCOOaNntzZ1b7+xZixfz/owZLHjxRWKio9lRUEBZeTkzFi7ku/XrWfbaaxSXlrK3lgbYk8eNIyM1lVvHjOHvzz7LIzfc0LKxh4BAquXHA9epandV7Y4z3OsTAay3Aejq9zkHp4q/5jIfqeo+Vd0BTAMOr7khVR2vqsNUdVhkZCDNBIwxJvhEhEOv2cEd73Ynq2PTWkc35IwjU3h30S2hndg9sHnHDjJSUohxC3wZqal0ycwkOiqKrTt3Ul5RQXxsLB3T6+6MaNTgwaxav77O+W1ZIMk9wX+IV1WdAgTSHH8ukCciPUUkGjgP5xl5f+8Cx4pIpIjE41TbrwgocmOMCRFpIwu4cUoyw4/p1KLb/eWIZN5ccDPR5SUtut1w8JMRI1i/dSt9zj6bP9x3H1PnzwegY4cO7Nm3j0vuuYf6HvUGeH/GDAbk5rZGuK0ukOS+WkTuEJEe7ut2YE1DK6lqBXAl8DFOwn5dVZeJyGgRGe0uswL4CFgMzMF5XG5pUw/GGGO8EtWxhPPeLuXi63rREm2iLjg6iVfn/JmoirbVx31rSYyPZ/4LLzD+1lvJTEvj3Ftv5dn33uOXN9/M52PGEB8by7UPOg91/eG++/hgxoz9654wejSDLriAPfv2ccsll3h0BMFVbyc2ACKSBtwDHONOmgbco6q7ghxbrawTG2NMqNv+aQfGXLGRgoKmJeZLjkngqZk3EaFVLRxZy1nx4Ycc4t8QrZXvudf05uef89S777Ji7VrWTpxIZWUlZ990E4P79uWDGTOYPG4cCXFx9DjjDOY9/zwZTXgOPWC5uU16zr3aihUrOOSQQw6Y1thObOosuYtIrIhcA/wVWAYMV9UhqnqNV4ndGGPagsyTdnLLlHQGDsls9LqXHxvP0zP+HNKJPRR8s3Yt3/3wvw6FFn77Lb2ys1FVJs+bh8/nY/ytt/Lwq68ypF8/EuLiPIy29dXXOu05oByYDpwCHAJc0woxGWNMmxfTrYhLPohg1p09eeOJBu9kAnDVqBgemfbnIEcWHgqLi7nq3/+moLCQSJ+P3Jwcxt92G5f+7Gf86f77KSopIT42lv/eeCP/ev553vz8c3554oleh91q6qyWF5ElqjrAfR8JzFHVIa0ZXG2sWt4Y09ZsfCedx/70A/v2lde5zPXHRXH/1NtaMarmOaha3vxPKFfL45Tagf2N44wxxjRB9ln53P55R/ocklbr/NuOkzaV2E3oqy+5Hy4ie9zXXmBg9XsRsS6SjDGmEeL6FDL6Ex+nndtt/zSfT/j7CZXcO/UuDyMz4ajOe+6q6mvNQIwxJtxFJFRw0tg99B7Rm48f38mjhy7npLce9zosE4asuzdjjGklCRpLZkEER/bfxG0DPyXr9c+8DsmEKUvuxhgTBElVcWTlQ9Y3O8ic9Q1Z780kYen3Xodl2glL7sYY00ypFfFkba8ka8U2MqcvJ2vidGLX1BxKw5jWY8ndGGMCFEEEaWVxZG0pI3P5FrKmLCFz4nSit+R7HVq7k19QwIl/+AMAW/Lz8fl8ZLqPn8157jmio6K45O67mfr116QkJFBSVsb5P/0pd/3udwBcdu+9XHfBBfTv1euAXusSR42icNo0rw6rxVhyN8aYWvjwkV4SR9bGYjKXbCTri4VkvDeDyN2FXocWkh7uPbNFt3f19yPrnZ+emsrCl18G4O7x40mMi+OGiy46aLl//+lP/PLEEykpLaX/Oedw8amn0jM7mydvv71F4w01ltyNMe1eFFFkFMWQ9UMhmQt/IOvTBaRP+pKI0jKvQzMtpKTM+V1Wd0N7/BVXcP/VVzOsf/9al9+8Ywfn3norewoLqaisZOzNN3Ps4MGtFm9zWXI3xrQrMUSTuTeKrDV7yJq3hsxP5pL2+TykstLr0EwQ3PjII9z71FOs2rCBP517LlkdOgS03ssffcRPR4zgtt/8hsrKSopK2tawu5bcjTFhK9599CxrdQGZc1aR9cFsUr5c7HVYphVVV8sXFhVx4h/+wJeLFnH04Yc3uN4R/fvzm7/+lfKKCn5+3HEM6tu3FaJtOZbcjTFhIbEqjqydkPVdPplffkPWe1+SuPg7r8MyreidyZO554knAA66p54YH8/xQ4cyI8DkPmrIEKaNH88HM2Zw0V13ceNFF3HxaacFJe5gsORujGlzUirjydpe5Tx6NmM5We/PJO679V6HZTx21gkncNYJJ9Q6r6Kigq+WLuWqc88NaFvrNm8mOzOT3511FvuKi1mwcqUld2OMaQmC0KE8nswt5WQt20zWtKVkTJxBzKbtXodm2ojqe+5lFRWceMQR/KKO5F/TlPnz+fcLLxAVGUlifDzP3313cANtYXUO+RqqbMhXY8KTjwjSS+PI3FhC1pJNzqNn788gctder0MztbAhX+sRAkO+WsndGNPqIokksyiWzPX7yFr4A1mffU2HSTPxFZd6HZoxYcGSuzEmqKKJImtvNJnr9pI1fy2Zn8ylwydz7NEzY4LIkrsxpsXEaQxZu31kri4ga873ZH44h5QZC5E2dvvPmLbOkrsxpkkSq+LI3CVkfZdP1uxvyfxgFknzV3odljGGICd3ETkZeBjwAU+q6n11LHcEMBs4V1XfDGZMxpjGS6mMJ3NHFVkrt5M1cwWZE2cS/+06r8MyxtQhaMldRHzAGOAkYAMwV0QmquryWpb7J/BxsGIxxgRGENLK48naWu6MejZtGZkTpxOzYZvXoRljGiGYJfcjgVWquhpARF4FzgSW11juKuAt4IggxmKMqSGi+tGzzWVkLdlA1uTFZEycQdTO3V6HZkyDAhnydcr8+ZwwejQTH3iAn40aBcDp117LDRdeyPFDh9a57YdefpnLf/EL4mNjg34cwRLM5J4N+HcZtQEY7r+AiGQDZwE/wpK7MUETiY+M4lgyNxQ7j559/jXpH3yJb1+x16GZMCGPt+wlXK+YW+/8QId8zcnK4m/PPLM/uQfioVdf5cJTT7XkXgepZVrNJrMPATepaqVIbYu7GxK5HLgcIDo6uqXiMyYsRRNFZmGM8+jZgjVkfTKftE++IqK8wuvQjGl1h/fpQ3lFBZ9+9RUnDT+gfMnnc+Zww8MPU1FZyRH9+zP25pt5/O232bR9OyeMHk1GaiqTx43jk9mzuWv8eErLyuidk8Mzd95JYnw8Nz/6KBOnTyfS5+Mnw4dz/zXXeHOQtQhmct8AdPX7nANsqrHMMOBVN7FnAKeKSIWqTvBfSFXHA+PB6aEuWAEb09bEaQyZeyKdR8/mrSbzozmkTllgj54Z4+f23/yG28eOPSC5l5SWcsk99/D5Y4/Rp3t3Lr7rLsa++SbXXHABD778MpPHjSMjNZUdBQXc+/TTfDZmDAlxcfzzued48KWXuPKcc3hnyhRWvvkmIkLB3tDqSTGYyX0ukCciPYGNwHnABf4LqGrP6vci8izwfs3EboxxJFTFklUQQeaqnWTN/o7MSbNJnrPM67CMCXnHDh4MwPSvv94/7Zt16+iZnU2f7t0B+PVppzHmjTe45oID0hSzlyxh+erVjPztbwEoq6jgqAEDSE5IIDYmhsvuvZfTRo7k9GOPbaWjCUzQkruqVojIlTit4H3A06q6TERGu/PHBWvfxrR1yZXxZOUrmd9sJ+vLlWS+N5OE5Wu8DsuYkFbfkK+3/eY3/O3pp4mMdNJeoOOqqConDR/OK3/720Hz5jz7LJ/Pncurn3zCf994gy/Gjm3mEbScoD7nrqqTgEk1ptWa1FX1kmDGYkwoEoTUiniytlaQtWIrmdOXkTVhOjHrt3gdmjFtTs0hX6fMn7///U9GjOCOcePYtN0ZUbBfjx6s3bSJVevXk9u1Ky9MmsRxQ4YAkBQfz959+8hITWXEgAH88V//2r9cUUkJG7ZupUtmJkUlJZw6ciQjDjuM3F/8onUPtgHWQ50xrSgSHz23RtFl3nqypiwm893pRO0o8DosY9qF2y69lDNvuAGA2JgYnrnzTv7v5pv3N6gbffbZAFx+1lmccvXVdM7IYPK4cTx7112cf9ttlJaXA3Dv6NEkJSRw5vXXU1JWhqryn2uv9ey4amNDvtqQrybIIoig285Y+k5cQq/7XiJ6206vQzKm2WzI13rYkK/GhCdByC6Mp88n35H3txeJXVPzQRFjjAkeS+7GtKCOJYn0mfYDff7xKomLvvU6HGNMO2XJ3Zhm6lCeQN852+jzwNukTpnf8ArGGBNkltyNaYKUynjyFu+mz6PvkzlhqtfhGGPMASy5GxOgrNIEes3bRu8nPiJj4nSvwzHGmDpZcjemDhFE0GVPHL2nr6X3w++QNK/mgIbGGBOaLLkb4yeKSLpti6L3x8vo+eAbxK7b7HVIxpg6+IYPZ0Dv3ijgi4jgvzfeyNGHH97s7ZZXVHDH2LG8NXkyMVFRxMfGcs/ll3PKyJHND7qVWHI37V6cxtBrXSW93p5Ht0ffJHJ3odchGdPmyBEftOj2dO5pDS4TFxOzf9jXj2fN4pYxY5g6fnyz933H2LFszs9n6auvEhMdzdb8fKYuWBDw+pWVlfiaHUXzWHI37VJKZTy9l++l14tT6fL0B0hlpdchGWOaYc++faQlJwNQWFTEmddfz669eymvqODe3/+eM487DoCf33AD67dupaS0lKvPO4/La3QbW1RSwhPvvsuaCROIcYcY75iezjknnQTAKx9/zN+feQZV5bRjjuGfV10FQOKoUVx3wQV8PHs2D/z731z4+99z7rnnMnnyZABefvllcnNzeeONN7jnnnvw+XykpKQwbdq0oJwPS+6m3cgqTaDX/O30Hv+hNYgzJgwUl5Yy6IILKCkrY/OOHfsHbomNjuadf/+b5MREdhQUMOLSSzlj1ChEhKfvuIMOKSkUl5RwxK9/zdk/+hHpfr3JrVq/nm4dO5KcmHjQ/jZt385Njz7K/BdeIC0piZ9cdRUTpkzh58cfz77iYg7r3Zu/jB7t9FAHJCcnM2fOHJ5//nmuueYa3n//ff7yl7/w8ccfk52dTUFBQdDOjSV3E7aqe4nrPX0dvR5+x4ZHNSbM+FfLz1q8mIvvuoulr72GArc+9hjTvv6aCBE2bt/O1vx8OmVk8Mhrr/HOlCkArN+6le/Wrz8guddn7vLlHD90KJlpaQD86uSTmfb11/z8+OPx+Xyc/aMfHbD8+eefv//ntW7f8yNHjuSSSy7hnHPO4RdBHGzGkrsJKz58dM+PofcnK+n54OvEfb/B65CMMa3gqIED2bF7N9t37WLSzJls37WL+S+8QFRkJD3OOIOSsjKmzJ/PZ3PmMOvpp4mPjeX4K66gpKzsgO3kdu3KD1u3snffPpISDuzKvb6xWGKjo/H5DrzTLiIHvR83bhxfffUVH3zwAYMGDWLhwoWkp6c39/APEtHiWzSmlcUQTb8N0Zz62HKu6HMHP8u7nP5/fNASuzHtyMq1a6msrCQ9JYXdhYVkdehAVGQkk+fNY91m56mX3YWFpCUlER8by8q1a5m9dOlB24mPjeW3Z5zBn+6/nzJ3FLjNO3bw4qRJDD/sMKYuWMCOggIqKyt55eOP9w8TW5vXXntt/8+jjjoKgO+//57hw4fzl7/8hYyMDNavX9/SpwKwkrtpoxI0ll6ryuj92ixyxr6Nr7jU65CMMa2s+p47gALP3X03Pp+PX51yCj+77jqGXXwxg/r0oV+PHgCcfNRRjHvrLQaefz59u3dnxGGH1brde3//e24fO5b+55xDbHQ0CXFx/OWKK+ickcE//vhHThg9GlXl1JEj9zfUq01paSnDhw+nqqqKV155BYAbb7yR7777DlXlxBNP5PAWeHSvNjbkqw352makViTQe+lucp/9go4vfIi0sb9dY8KJDflaj9xcegwaxLx588howjmyIV9N2MsqSaT33C3kjv2ADh/N8jocY4xpEyy5m5AiCNl748mdupZeD79F0vyVXodkjDGNtnbtWk/3b8ndeK76kbXcL1aT+69XSVi+xuuQjDGmTbPkbjwhCF0K48mbvJrcf79OwtLvvQ7JGGPChiV302oEofO+ePpMXuMk9CWrvA7JGGPCUlCTu4icDDwM+IAnVfW+GvN/BdzkfiwEfq+qi4IZk2l9XfYlkDd1Lbn/foPERd96HY4xxoS9oCV3EfEBY4CTgA3AXBGZqKr+g2KvAY5T1V0icgowHhgerJhM6+lclEje1LXk3f8GiV9/43U4xpgwtDU/n2v/8x9mL1lCWnIy0VFR/PmiizjrhBNqXX7K/Pnc/+KLvP+f/xw0r8cZZzDv+efJCLAr2lAXzJL7kcAqVV0NICKvAmcC+5O7qn7pt/xsICeI8Zgg61yUQN60H8h94A1r5W5Me5N4RMtur3BuvbNVlZ/feCO/Pu00Xr73XgDWbd7MxCCNstbWBDO5ZwP+/eptoP5S+W+BD4MYjwmCTsUJ5E1fT979b5I0b3nDKxhjTAv4Yu5coiMjGX322funde/cmavOPZeS0lJ+f999zFuxgkifjwevvZYThg07YP38ggLOv/12tu/axZGHHlpvv/FtUTCTu9QyrdazJyIn4CT3Y+qYfzlwOUC0O76u8U6n4kTyZqwn94E3baQ1Y4wnlq1ezZB+/WqdN+aNNwBY8uqrrFy7lp9ceSXfvvXWAcvc8+STHHP44dz5u9/xwYwZjH/nnaDH3JqCmdw3AF39PucAm2ouJCIDgSeBU1Q1v7YNqep4nPvxJCQkhNfXqzaiY0kCeTM2kvfgmyTPPniwBWOM8dIf//lPZixaRHRkJDkdO3LVOecA0K9HD7p37sy3P/xwwPLTFizg7X/9C4DTjjmGtOTkVo85mIKZ3OcCeSLSE9gInAdc4L+AiHQD3gYuUlVrRh1iskoSyftyI30eeIvkWYu9DscYY/Y7tFcv3vrii/2fx9x0EzsKChh28cVkZ2UFtA3/IVnDTdCGfFXVCuBK4GNgBfC6qi4TkdEiMtpd7E4gHXhMRBaKyLxgxWMCk1WayMjJe7jkZ89wfpfLGPbLuyyxG2NCzo+OOIKSsjLGvvnm/mlFJSUAjBo8mJc++giAb9et44ctW+jbvfsB648aMmT/Mh/OnMmuPXtaKfLWEdTn3FV1EjCpxrRxfu8vAy4LZgymYVmlieTO2kSf/7xNyvSFXodjjDENEhEm3H8/1z74IP964QUyU1NJiIvjn1deyZnHHcfof/yDAeedR6TPx7N33UVMjfZad112GefffjtDLryQ44YMoVunTh4dSXDYkK/tdMjXzNIE8mZvIe8/b5M67WuvwzHGtDE25Gs9cnOhGc/L25CvplEyyhLI+2oreQ9PIO0LuwNijDHhypJ7mMsoSyBvzjYnoX9ef6cQxhhjwoMl9zCUXpZA3txt9HlkImmffuV1OMYYY1qZJfcwkV6eQN7c7eQ9OpEOH8/2OhxjjDEesuTehnUoTyBv/g7y/vse6ZO+bHgFY4wx7YIl9zamQ0UCufPz6fPf90j/YKbX4RhjjAlBltzbgLSKBPIW5JM35gMy3pvudTjGGBOSHnr5ZS7/xS+Ij41t0vqX3H03px97LL888cQWjqz1WXIPUakVCfT5eid5j31Axrs2hKExJrRJjVHXmkvnNf5x3YdefZULTz21yck9nFhyDyGplfHkfV1A3thJZL4zxetwjDEmZO0rLuacW25hw7ZtVFZW8n8//jGbtm/nhNGjyUhNZfK4cbzy8cf8/ZlnUFVOO+YY/nnVVQAkjhrF1eedx/szZhAXE8O7999Px/T0A7Z/x9ixrN+6lafvvJNbx4xh4vTpRPp8/GT4cO6/5hoPjrhxLLl7LKUynrxFBfR57EMy357sdTjGGNMmfDRrFl0yMvjgoYcA2F1YyDPvvcfkcePISE1l0/bt3PToo8x/4QXSkpL4yVVXMWHKFH5+/PHsKy5mxGGH8bc//IE/P/IIT0yYwO2//e3+bf/5kUec7d11F7v27OGdKVNY+eabiAgFe/d6dMSNE7SBY0zdUirjGbagnPOveI9LMi9n5I//bIndGGMaYUDv3nw2dy43Pfoo07/+mpTExAPmz12+nOOHDiUzLY3IyEh+dfLJTPva6Wo7OiqK0489FoCh/fqxdvPm/ev99amnKNi7l8dvvRURITkhgdiYGC67917e/uKLNlPlbyX3VpJSGU/u4t30efxjsl7/zOtwjDGmTevTvTvzn3+eSTNncsuYMfxk+PAD5tc3bkpUZOT+4V59Ph8VFRX75x3Rvz/zV65k5+7ddEhJITIykjnPPsvnc+fy6ief8N833uCLsWODc1AtyJJ7ECVXxpO3dA954z+m4yufeh2OMcaEjU3bt9MhOZkLTz2VxPh4nn3/fZLi49m7bx8ZqakMP+wwrn7gAXYUFJCWlMQrH3/MVeee2+B2Tz7qKH46YgSnXXstnzz6KCJCUUkJp44cyYjDDiP3F79ohaNrPkvuLSypKo68pYXkjf+ETi9/7HU4xhgTlpasWsWNjzxChAhRkZGMvflmZi1ZwilXX03njAwmjxvHP/74R04YPRpV5dSRIznzuOMC2vb//fjH7C0q4ozrr+fle+/lzOuvp6SsDFXlP9deG+Qjaxk25GsLDPmaVBVH3rJC8p74jE4vfthCkRljTOiyIV/rYUO+tl2JVXHkLd9H3lOf0en5D5E29iXJGGNM+LLk3giJVXHkrShyEvpzkyyhG2OMCUmW3BuQWBVH7soi8p7+nM7PfGAJ3RhjTMiz5F6LBI0lb2UJec98Qeen3rOEbowxNVVVoYB4HUeYaal2cJbcXQkaS+43JeQ9O4UuT7xrCd0YY+oRu2oV+R06kB4ZaQm+hagq+fn5xLZARzntvrX8hrnvIU89Q5cnJyKVlS22XWOMCWflaWlsuPtuSnJzIcI6Oz1AZibExzdp1djYWHJycoiKijpgemNbywc1uYvIycDDgA94UlXvqzFf3PmnAkXAJaq6oL5ttnRy58474a9/bbntGWOMad8mTIAzz2zRTTY2uQft65aI+IAxwClAf+B8EelfY7FTgDz3dTkQ+n36GWOMMSEumHUpRwKrVHW1qpYBrwI1v8qcCTyvjtlAqoh0DmJMxhhjTNgLZoO6bGC93+cNwPAAlskGNtNaunSBIUNabXfGGGPCXDN6p2spwUzutTWgrHmDP5BlEJHLcartAVREit33kUBFzeXNAewcNczOUcPsHDXMzlHD2sc5Ov745qxd1zmKa+xGgmUD0NXvcw6wqQnLoKrjgfE1p4vIPFUd1vxQw5edo4bZOWqYnaOG2TlqmJ2jhrXUOQrmPfe5QJ6I9BSRaOA8YGKNZSYCF4tjBLBbVVuvSt4YY4wJQ0EruatqhYhcCXyM8yjc06q6TERGu/PHAZNwHoNbhfMo3KXBiscYY4xpL4LaQ52qTsJJ4P7Txvm9V+CPzdjFQVX15iB2jhpm56hhdo4aZueoYXaOGtYi56jN9VBnjDHGmPpZn4HGGGNMmAnJ5C4iJ4vINyKySkRurmW+iMgj7vzFIjIk0HXDRVPPkYh0FZHJIrJCRJaJyNWtH33raM7fkTvfJyJfi8j7rRd162rm/1qqiLwpIivdv6ejWjf61tPM83St+7+2VEReEZHmjwoSggI4R/1EZJaIlIrIDY1ZN1w09Rw16bqtqiH1wml89z3QC4gGFgH9ayxzKvAhznPyI4CvAl03HF7NPEedgSHu+yTgWztHB54jv/nXAS8D73t9PKF4joDngMvc99FAqtfHFGrnCadTrjVAnPv5dZwxNDw/Lg/OURZwBPA34IbGrBsOr2aeo0Zft0Ox5N6cbmsDWTccNPkcqepmdQfnUdW9wAqcC1C4aVb3xyKSA5wGPNmaQbeyJp8jEUkGRgFPAahqmaoWtGLsram5XWlHAnEiEgnEU0tfHmGgwXOkqttUdS5Q3th1w0STz1FTrtuhmNzr6pI2kGUCWTccNOcc7SciPYDBwFctH6LnmnuOHgL+DFQFKb5Q0Jxz1AvYDjzj3rp4UkQCHrGqjWnyeVLVjcD9wA843WrvVtVPghirV5pz7bXrdiMEet0OxeTenG5rA+rONgw0u2tfEUkE3gKuUdU9LRhbqGjyORKR04Ftqjq/5cMKKc35O4oEhgBjVXUwsA8I13ulzflbSsMpnfUEugAJInJhC8cXCppz7bXrdqAbaMR1OxSTe3O6rQ2oO9sw0KyufUUkCucP5CVVfTuIcXqpOedoJHCGiKzFqTr7kYi8GLxQPdPc/7UNqlpdengTJ9mHo+acpx8Da1R1u6qWA28DRwcxVq8059pr1+0ANPa6HYrJvTnd1gaybjho8jkSEcG5T7pCVR9s3bBbVZPPkareoqo5qtrDXe8LVQ3H0lZzztEWYL2I9HWXOxFY3mqRt67mXJN+AEaISLz7v3cizv3ScNOca69dtxvQpOu21y0Ia3vhtDz9Fqdl4W3utNHAaPe9AGPc+UuAYfWtG46vpp4j4BicqqDFwEL3darXxxNK56jGNo4nTFvLN/ccAYOAee7f0gQgzevjCdHzdA+wElgKvADEeH08Hp2jTjil1z1Agfs+ua51w/HV1HPUlOu29VBnjDHGhJlQrJY3xhhjTDNYcjfGGGPCjCV3Y4wxJsxYcjfGGGPCjCV3Y4wxJsxYcjcmyESkUkQWuqOCvSEi8Y1Yd5CInOr3OUZEPnO3d249600RkWHu+0kiktqIfa4VkSXijG42VUS6B7puje0cL7WMqOdO3+12W7tSRO4PYFvXNOa8GdPeWXI3JviKVXWQqh4GlOE819ogd6CRQTjPxlYbDES523stkO2o6qna+EFdTlDVgcAU4PZGrhuI6ep0WzsYOF1ERjaw/DU4g64EzD1/xrRLltyNaV3TgVwR6SAiE9zS8WwRGQggIneLyHgR+QR4HvgLcK5fSf1FYJD7ubeInOiWgJeIyNMiElNzh25JPMN9f51bg7BURK4JIN5ZuINbiEimiLwlInPd10h3+pEi8qUbx5d+vdY1SFWLcTrkqN7HT8QZz3qBW8uRKCJ/wumXfbKITHaXK/Q7vl+KyLPu+2dF5EF3uX+6nx9x41otIr8MNDZj2jJL7sa0ErckeQpOD2b3AF+7peNbcRJ5taHAmap6AXAn8JpfSf0ynFLvIGAj8CxwrqoOwBnM5ff17H8ocCkwHGfM8d+JyOAGwj4Zp/c5gIeB/6jqEcDZ/G843JXAKLckfifw9wa26R9TGpAHTHO/gNwO/FhVh+D0fnedqj6C0wf3Cap6QgCb7eNu43r3c2ecHr5OB+4LNDZj2jKrtjIm+OJEZKH7fjpOH9Ff4SRIVPULEUkXkRR3mYluibYhfXEGJfnW/fwc8Eec4WprcwzwjqruAxCRt4Fjga9rWXayiHQEtvG/avkfA/2dbq4BSBaRJCAFeE5E8nC6yIwKIPZjRWSxewz3qeoWcUbj6w/MdPcRjVNz0FhvqGql3+cJqloFLHePyZiwZ8ndmOArdkva+4lfhvRT3Rf0vgC3W9s2Wmr5E9w4nsW5NXAdTk3fUTW/eIjIo8BkVT1LnLGmpwSw/emqerqI9AFmiMg7bnyfqur5Aazv3292bI15Nc9fqX+4AWzbmDbPquWN8cY04FfgtB4Hdmjt4zPvBZLq2MZKoIeI5LqfLwKmNrDPn7sjlCUAZ+HUJNTKTeLX4Ix21gH4BLiyer6IDHLfpuDcIgC4pJ7917aPb4F/ADcBs4GR1cfjxtnHXbTmedgqIoeISIR7HMYYP5bcjfHG3cAwt2r6PuDXdSw3Gacq/KBH31S1BOce+hsisgSoAsbVtUNVXYBTEp+Dc1vgSVWtrUref53NwCs41f1/qo5ZRJbzv1b//wL+ISIzAV9926vDOGAUkIjz5eAV97zMBvq5y4wHPqxuUAfcDLwPfAFsbsI+jQlrNiqcMcYYE2as5G6MMcaEGUvuxhhjTJix5G6MMcaEGUvuxhhjTJix5G6MMcaEGUvuxhhjTJix5G6MMcaEGUvuxhhjTJj5f7YuP0PyzGZ+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x324 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "transition_map(ret_df, labels=df.columns, startyear=df.index[0], endyear=df.index[-1])\n",
    "# these are absolute values, there are shorts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z0KfmAJ8CjlfVR1atG3508Z/zyz/LtG3ksRB9tp1LzUleCdwN3FBV3x5avt55Mu+az+/OCZJczuB3+5k+286r5q7W3wD+gKFzfI7vcx+Ldj6/0GaPwm72C/hN4MvAd7qfL++W/zZwpJt+NYNR6WPA48D7xm2/ADW/icF/6R4BHu5e+7t1/wI82q07DFwww1r3M/iEzXfPvG/AjcCN3XQYfEHKd7ua9q637SadE+Nq/iTw46H3dXncebIANd/U1XSMwUDuGxb9fe7m/xK4a9V283yf7wROAf/LoDf+9kU/n4df3vovSY3YDpdcJGlbMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSI/4fbx9jjO/u0koAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# number of stocks\n",
    "n = 1000 \n",
    "# historical mean returns for each stock\n",
    "mu = np.random.normal(0.1, 0.2, n)\n",
    "\n",
    "# number of factors\n",
    "m = 10\n",
    "\n",
    "# factor covariance matrix - random symmetrical matrix\n",
    "SigmaFactor = np.random.randn(m, m)/4\n",
    "SigmaFactor = SigmaFactor.T @ SigmaFactor\n",
    "\n",
    "# factor loadings, determine volatility and covariances between stocks\n",
    "F = np.random.randn(n, m)\n",
    "# idiosyncratic risk of each stock\n",
    "D = np.diag(np.random.uniform(0, 0.9, size=n))\n",
    "\n",
    "count, bins, ignored = plt.hist(mu, 100, density=True, align='mid')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ECOS 2.0.7 - (C) embotech GmbH, Zurich Switzerland, 2012-15. Web: www.embotech.com/ECOS\n",
      "\n",
      "It     pcost       dcost      gap   pres   dres    k/t    mu     step   sigma     IR    |   BT\n",
      " 0  -9.828e-02  -3.087e+00  +4e+01  2e-01  6e-01  1e+00  2e-02    ---    ---    1  1  - |  -  - \n",
      " 1  -2.140e-01  -1.045e+00  +3e+01  7e-02  3e-01  8e-01  1e-02  0.2887  3e-01   0  0  0 |  0  0\n",
      " 2  -1.064e+00  -1.920e+00  +3e+01  3e-02  4e-01  1e+00  1e-02  0.0179  1e+00   0  0  0 |  0  0\n",
      " 3  -1.452e+00  -1.581e+00  +1e+01  5e-03  1e-01  5e-01  6e-03  0.6227  6e-02   0  0  0 |  0  0\n",
      " 4  -1.487e+00  -1.618e+00  +8e+00  4e-03  9e-02  3e-01  5e-03  0.3177  3e-01   1  1  1 |  0  0\n",
      " 5  -1.510e+00  -1.642e+00  +8e+00  4e-03  9e-02  3e-01  5e-03  0.0197  9e-01   1  1  0 |  0  0\n",
      " 6  -1.779e+00  -1.903e+00  +3e+00  1e-03  3e-02  4e-02  2e-03  0.9890  4e-01   1  1  1 |  0  0\n",
      " 7  -1.922e+00  -1.926e+00  +1e-01  4e-05  1e-03  9e-04  6e-05  0.9722  7e-03   1  1  1 |  0  0\n",
      " 8  -1.929e+00  -1.929e+00  +1e-03  4e-07  1e-05  9e-06  7e-07  0.9890  1e-04   1  0  1 |  0  0\n",
      " 9  -1.929e+00  -1.929e+00  +1e-05  5e-09  1e-07  1e-07  7e-09  0.9890  1e-04   1  1  1 |  0  0\n",
      "10  -1.929e+00  -1.929e+00  +2e-07  5e-11  2e-09  1e-09  8e-11  0.9890  1e-04   1  0  0 |  0  0\n",
      "11  -1.929e+00  -1.929e+00  +2e-09  6e-13  2e-11  1e-11  9e-13  0.9890  1e-04   1  0  0 |  0  0\n",
      "\n",
      "OPTIMAL (within feastol=1.8e-11, reltol=8.9e-10, abstol=1.7e-09).\n",
      "Runtime: 0.007954 seconds.\n",
      "\n",
      "Max return portfolio (return=1.9291, vol=25.0975)\n"
     ]
    }
   ],
   "source": [
    "w = cp.Variable(n)         # what we solve for: weight for each stock\n",
    "ret = mu.T @ w             # solve for weights that maximize portfolio return\n",
    "f = F.T @ w                # portfolio factor loading\n",
    "Lmax = cp.Parameter()      # leverage constraint\n",
    "# portfolio volatility: factor risk + idiosyncratic risk\n",
    "risk = cp.quad_form(f, SigmaFactor) + cp.quad_form(w, D)\n",
    "prob = cp.Problem(cp.Maximize(ret), \n",
    "                  [cp.sum(w) == 1, \n",
    "                   cp.norm(w, 1) <= Lmax])\n",
    "\n",
    "# Solve the factor model problem.\n",
    "Lmax.value = 2\n",
    "prob.solve(verbose=True)\n",
    "\n",
    "maxretvol = risk.value\n",
    "maxret = ret.value\n",
    "print(\"Max return portfolio (return=%.4f, vol=%.4f)\" % (maxret, maxretvol))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min vol portfolio (return=0.1376, risk=0.0002)\n"
     ]
    }
   ],
   "source": [
    "# solve min vol portfolio (other corner solution)\n",
    "\n",
    "prob = cp.Problem(cp.Minimize(risk),\n",
    "                  [cp.sum(w) == 1, \n",
    "                   cp.norm(w, 1) <= Lmax])\n",
    "prob.solve(solver=cp.OSQP)\n",
    "\n",
    "minvol = risk.value\n",
    "minvolret = ret.value\n",
    "print(\"Min vol portfolio (return=%.4f, risk=%.4f)\" % (minvolret, minvol))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 6s, sys: 1.46 s, total: 1min 8s\n",
      "Wall time: 8.74 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# solve points in between\n",
    "# maximize return subject to volatility constraints between minimum volatility and max return volatility\n",
    "# might have to run a couple of times to get a solution\n",
    "\n",
    "# specify a Parameter variable instead of creating new Problem at each iteration\n",
    "# this allows the solver to reuse previous work\n",
    "vol_limit = cp.Parameter(nonneg=True)\n",
    "\n",
    "prob = cp.Problem(cp.Maximize(ret),\n",
    "                  [cp.sum(w) == 1, \n",
    "                   cp.norm(w, 1) <= Lmax,\n",
    "                   risk <= vol_limit]\n",
    "                 )\n",
    "\n",
    "# define function so we can solve many in parallel\n",
    "def solve_vl(vl_val):\n",
    "    vol_limit.value = vl_val\n",
    "    result = prob.solve(verbose=False)\n",
    "    return (ret.value, np.sqrt(risk.value), w.value)\n",
    "\n",
    "# number of points on the frontier\n",
    "NPOINTS = 200\n",
    "vl_vals = np.linspace(minvol, maxretvol, NPOINTS)\n",
    "\n",
    "# iterate in-process\n",
    "results_dict = {}\n",
    "for vl_val in vl_vals:\n",
    "    # print(datetime.strftime(datetime.now(), \"%H:%M:%S\"), vl_val)\n",
    "    results_dict[vl_val] = solve_vl(vl_val)\n",
    "    \n",
    "# parallel implementation\n",
    "# NPROCESSES = 8\n",
    "# pool = Pool(processes = NPROCESSES)\n",
    "# result_values = pool.map(solve_vl, vl_vals)\n",
    "# results_dict = dict(zip(vl_vals, result_values))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>i</th>\n",
       "      <th>var</th>\n",
       "      <th>return</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>0.165322</td>\n",
       "      <td>0.012779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.126278</td>\n",
       "      <td>1.301645</td>\n",
       "      <td>0.355356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.252395</td>\n",
       "      <td>1.378043</td>\n",
       "      <td>0.502389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.378512</td>\n",
       "      <td>1.425796</td>\n",
       "      <td>0.615233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.504629</td>\n",
       "      <td>1.458763</td>\n",
       "      <td>0.710372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>195</td>\n",
       "      <td>24.592997</td>\n",
       "      <td>1.927820</td>\n",
       "      <td>4.959133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>196</td>\n",
       "      <td>24.719114</td>\n",
       "      <td>1.928131</td>\n",
       "      <td>4.971832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>197</td>\n",
       "      <td>24.845231</td>\n",
       "      <td>1.928441</td>\n",
       "      <td>4.984499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>198</td>\n",
       "      <td>24.971348</td>\n",
       "      <td>1.928748</td>\n",
       "      <td>4.997134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>199</td>\n",
       "      <td>25.097465</td>\n",
       "      <td>1.929053</td>\n",
       "      <td>5.009737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       i        var    return       std\n",
       "0      0   0.000161  0.165322  0.012779\n",
       "1      1   0.126278  1.301645  0.355356\n",
       "2      2   0.252395  1.378043  0.502389\n",
       "3      3   0.378512  1.425796  0.615233\n",
       "4      4   0.504629  1.458763  0.710372\n",
       "..   ...        ...       ...       ...\n",
       "195  195  24.592997  1.927820  4.959133\n",
       "196  196  24.719114  1.928131  4.971832\n",
       "197  197  24.845231  1.928441  4.984499\n",
       "198  198  24.971348  1.928748  4.997134\n",
       "199  199  25.097465  1.929053  5.009737\n",
       "\n",
       "[200 rows x 4 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_df = pd.DataFrame(enumerate(results_dict.keys()))\n",
    "ret_df.columns=['i', 'var']\n",
    "ret_df['return'] = [results_dict[v][0] for v in ret_df['var']]\n",
    "ret_df['std'] = [results_dict[v][1] for v in ret_df['var']]\n",
    "ret_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oe5NHr5cvmDmDdfMrueGsoGW+bkGlpjUVkbQo4EWmyb7kEV5obmdz82FeaGlnc3M7B7r6AMgzWF5bzgXLZo+0ytfOr2RmaVHMVYtItlLAi0SgrbOXzc3tQaC3HOaF5vaR+8vzDE6bU8FbVtdx5sIqTl9Qxeq5lRq6VUSmlAJeZJIOdfWxuaWdzS3tvNB8mM3N7STC29IsbJlfvKKGMxZWcebCKtbOq1KYi0jkFPAiE9A7MMiWRJJnXjvMs68d4vnmw+w+2DOyfmlNGQ31szlzYRVnLKhi3YIqyov1v5mITD/9yyMyjuFOcM++dohnXj3Ms7sP0dSSHLk1bX5VCWcvnskt5y/hzDDMq2ZoYhURyQwKeJFQT98gm1vaefa1Qzz72mGeee3QyHXz4oI8zlxYxXsvqufcxTM5e9Es5laVxFyxiMj4FPBySnJ3mg/18PSrh3gmDPSte5IMDAU3my+pLuVNy6s5Z/Eszl08i9XzKjTBiohkFQW8nBIGBod4cW8HT+06yKZdh3hq18GR1nlZUT5nLZrJH1y6jHMWzeKcxTM1yYqIZD0FvOSkrt4Bntt9eCTQn33tEF19g0AweMyFy6tpqJ9Nw5JZnDanQqPAiUjOUcBLTtifPMKmV4OW+dOvHqIpkWRwyDGD1XMr+d03LBwJ9PkzZ8RdrohI5BTwknXcne2tnTy16xCbdh1i06sHefVANwAlhXmcvWgmf7h+OQ31szln8UwqS9SzXUROPQp4yXhDQ84r+zt5YscBnthxgCd3HuRgOMRrdVkRDfWzePcFS2ion83aeZWa11xEBAW8ZKDjBfrCWTO4bFUd5y8LTrcvrSnTxCsiImNQwEvsjhfoC2bO4C2r67hgWTXnL53NotmlMVcrIpIdFPASi5bDPTz6ciuPbmvj8e0Hjgn0y1bVccGy2VywrFqBLiJykhTwMi06ewd4YvsBHtvWxiOvtLKjtQuAuZUlrF9Vy4XLqhXoIiJTSAEvkRgacja3tPPoK6088kobz7x6iIEhp6QwjwuWVXPL+Uu4ZGUNK+rKdQ1dRCQCkQW8md0BXAfsd/fTx1i/HrgX2Bku+oG7/2W47mrgn4B84Ovu/qWo6pSp0903wGOvtPGfW/fx8xdbaesMRoo7fUElH7xkGW9eWcMblsyiuEBTpYqIRC3KFvydwG3AN46zzaPufl3qAjPLB74CXAE0A0+Z2X3uviWqQuXktff080DTXh5o3Mtj29roHRiioqSA9avqeOvqOt68skbDvoqIxCCygHf3R8ys/iR2PQ/Y5u47AMzsbuAGQAGfIXr6BvnZi/u477kED7/USt/gEAtnzeCd5y/mijVzeOPS2ZqYRUQkZnFfg7/QzJ4HEsAn3L0JWADsTtmmGTg/juLkqKEh5/EdB/je08082LSXrr5B6iqKedcFS/jts+dz1sIqXUsXEckgcQb8M8ASd+80s2uBHwIrgbFSwsd7EzPbAGwAWLx4cQRlntr2tPfwvU3N/MfTu9l9sIfKkgJ+++z5XH/WfM5fWq1JWkREMlRsAe/uyZTn95vZV82shqDFvihl04UELfzx3mcjsBGgoaFh3D8EJH0Dg0P859b9/Mem3Tz80n6GHN60vJpPXLmKq9bNpaRQneRERDJdbAFvZnOBfe7uZnYekAccAA4DK81sKdAC3AS8M646TyXJI/189ze7ufPXu2g53MOcymL+cP0K3t6wkCXVZXGXJyIiExDlbXJ3AeuBGjNrBj4PFAK4++3AjcCHzWwA6AFucncHBszsVuABgtvk7givzUtEdh/s5n//ahfffeo1uvoGOX/pbD53/VreurqOAnWWExHJShZkam5oaGjwTZs2xV1G1njmtUN8/dEd/LRxL3lmXHfmPN5/8TLOWFgVd2kiIpIGM3va3RvGWhd3L3qJwZZEkv/5wIs8/FIrlSUFbLhkOb//piXMq5oRd2kiIjJFFPCnkN0Hu/n7h17mh8+1UFFcwKevWc27L1hCWbH+MxARyTX6l/0UcLCrj9t+vo1vPfEqZvAHlyznw5cup6q0MO7SREQkIgr4HNbdN8Adj+3kX3+5g66+Ad7+hkV8/IqVOhUvInIKUMDnqJ9t3cdn72lkb/IIV66dw6euXsWKuoq4yxIRkWmigM8xh7r6+OL/a+KHzyVYPbeC2955Dg31s+MuS0REppkCPof8ZPMe/uLeRg539/Pxy1fyh+tXUFSg+9hFRE5FCvgc0NrRy+fva+T+zXs5Y0EV33z/+ayZVxl3WSIiEiMFfBZzd+59LsEX/l8T3X2DfOrqVWx48zKNPiciIgr4bLW3/QifvWczP3txP+cunsn/vPFMdaITEZERCvgs9PMX9/Gxu5+jf3CIv7huLe99U72mbRURkWMo4LOIu/PVh7fzdw++xNp5lXzlnedSX6NZ3kRE5PUU8Fmiq3eAT37vee7fvJcbzp7Pl37nTGYUaV52EREZmwI+C7x2oJsN39zEy/s6+Oy1a/jAm5diplPyIiIyPgV8hnvslTZuvesZ3OHO953HJafVxl2SiIhkAQV8hnJ3/v2xnfz3+7eysq6Cje95A0uqdb1dRETSo4DPQEf6B/nMDzZzz7MtXL1uLv/r987SlK4iIjIhSo0Mkzjcwx9882kaE+386RWn8ZHLVpCnW+BERGSCFPAZ5Dc7D/Lhbz1N78AQ//buBi5fOyfukkREJEsp4DPEd558jc/d28ji2aVsfE8DK+rK4y5JRESymAI+A+zvOMJnf7iZi1fUcNs7z6VqRmHcJYmISJbTrCQZoKkliTv80VtWKtxFRGRKKOAzQGNLOwBr52uKVxERmRqRBbyZ3WFm+82scZz1t5jZC+Hj12Z2Vsq6XWa22cyeM7NNUdWYKRoT7SyrKaNct8KJiMgUibIFfydw9XHW7wQudfczgb8CNo5af5m7n+3uDRHVlzGaEkm13kVEZEpFFvDu/ghw8Djrf+3uh8KXTwALo6olkx3u7qP5UA+nL6iKuxQREckhmXIN/v3AT1JeO/CgmT1tZhtiqmlaNCWSAJw+XwEvIiJTJ/aLvmZ2GUHAX5yy+CJ3T5hZHfCQmb0YnhEYa/8NwAaAxYsXR17vVGtKBB3s1ukUvYiITKFYW/BmdibwdeAGdz8wvNzdE+HP/cA9wHnjvYe7b3T3BndvqK3NvpnWGluSLJg5g1llRXGXIiIiOSS2gDezxcAPgHe7+8spy8vMrGL4OXAlMGZP/FzQmGhX611ERKZcZKfozewuYD1QY2bNwOeBQgB3vx34HFANfNXMAAbCHvNzgHvCZQXAd9z9p1HVGaeu3gF2tnXxtrMXxF2KiIjkmMgC3t1vPsH6DwAfGGP5DuCs1++Re7buCUawUwteRESmWqb0oj8lDY9gp1vkRERkqingY9SYSFJTXkxdRXHcpYiISI5RwMeoKZHk9AWVhP0NREREpowCPiZH+gd5ZV+Hrr+LiEgkFPAxeXlfBwNDrhHsREQkEgr4mDS2hEPUqoOdiIhEIO3b5MxsAbAkdZ/xho+VE2tKtFNZUsDCWTPiLkVERHJQWgFvZn8LvAPYAgyGix1QwJ+kxkSSdfOr1MFOREQikW4L/m3AKnfvjbCWU8bA4BAv7knynguXxF2KiIjkqHSvwe8gHGZWJm97axe9A0OsUwc7ERGJSLot+G7gOTP7GTDSinf3j0ZSVY47OoKdbpETEZFopBvw94UPmQKNiXZmFOaztKY87lJERCRHnTDgzSyfYErXy6ehnlNCU0uStfMryc9TBzsREYnGCa/Bu/sg0G1mumA8BYaGnC17khrBTkREIpXuKfojwGYzewjoGl6oa/AT9+rBbjp7BzSCnYiIRCrdgP9x+JBJGu5gt04d7EREJEJpBby7/5+oCzlVNCWSFOYbK+sq4i5FRERyWLoj2e0kGLnuGO6+bMorynFNiXZWza2gqEDTAIiISHTSPUXfkPK8BHg7MHvqy8lt7k5jSztXrZsbdykiIpLj0mpGuvuBlEeLu/8j8JZoS8s9ifYjHOruZ51mkBMRkYile4r+3JSXeQQtel1EnqCm4Q52ukVOREQilu4p+v+V8nwA2An83tSXk9saE0nyDNbMVcCLiEi00u3p9X53vyx8XOHuG4C+4+1gZneY2X4zaxxnvZnZP5vZNjN7IfUsgZldbWYvhes+nf7HyWxNLe2sqCtnRlF+3KWIiEiOSzfgv5fmslR3AlcfZ/01wMrwsQH4GowMjfuVcP1a4GYzW5tmnRmtKZwDXkREJGrHPUVvZquBdUCVmf1OyqpKgt7043L3R8ys/jib3AB8w90deMLMZprZPKAe2ObuO8Ia7g633XKCz5LRWjt62Zs8ouvvIiIyLU50DX4VcB0wE7g+ZXkH8MFJHnsBsDvldXO4bKzl50/yWLFrSgxPEasWvIiIRO+4Ae/u9wL3mtmF7v74FB97rKnU/DjLx34Tsw0Ep/hZvHjx1FQWgaZEEoC1asGLiMg0SPca/AEz+9lwhzkzO9PM/nySx24GFqW8XggkjrN8TO6+0d0b3L2htrZ2kiVFpynRzpLqUipLCuMuRURETgHpBvy/AZ8B+gHc/QXgpkke+z7gPWFv+guAdnffAzwFrDSzpWZWFB7nvkkeK3aNLUnNICciItMm3fvgS939N2bHnD0fON4OZnYXsB6oMbNm4PNAIYC73w7cD1wLbAO6gfeF6wbM7FbgASAfuMPdm9L9QJmovaef1w52c9N5i068sYiIyBRIN+DbzGw54bVwM7sR2HO8Hdz95hOsd+Aj46y7n+APgJywJbz+rlvkRERkuqQb8B8BNgKrzayFYCS7WyKrKscM96DXLXIiIjJd0p0PfgdwuZmVEVy37wHeAbwaYW05o7GlnXlVJdSUF8ddioiInCKO28nOzCrN7DNmdpuZXUFwrfz3Ca6bayz6NAUj2Kn1LiIi0+dELfhvAoeAxwkGtvkUUAS8zd2fi7a03NDdN8D21k6uPWNe3KWIiMgp5EQBv8zdzwAws68DbcBid++IvLIcsXVPB0OuEexERGR6neg++P7hJ+4+COxUuE/M0SFqdYpeRESmz4la8GeZWTJ8bsCM8LUR3Omm1DqBppYks8uKmFt53Ll5REREptSJxqLXxOWT1JhoZ938SkYNEiQiIhKpdIeqlZPQNzDEy/s6dP1dRESmnQI+Qi/v66B/0HWLnIiITDsFfIRGOthpiFoREZlmCvgINbYkqSguYPHs0rhLERGRU4wCPkKNiXbWzq8kL08d7EREZHop4CMyOORs3ZPUDHIiIhILBXxEdrR2cqR/SAPciIhILBTwEWkcGcFOLXgREZl+CviINLUkKS7IY1lNWdyliIjIKUgBH5HGRDtr5lVSkK+vWEREpp/SJwJDQ05TS1LX30VEJDYK+AjsPtRNR++AetCLiEhsFPARaEoEE/BpBDsREYmLAj4CjS3tFOQZp80tj7sUERE5RSngI9CYSHLanAqKCzTbroiIxCPSgDezq83sJTPbZmafHmP9J83sufDRaGaDZjY7XLfLzDaH6zZFWedUcneaWto1g5yIiMSqIKo3NrN84CvAFUAz8JSZ3efuW4a3cfcvA18Ot78e+GN3P5jyNpe5e1tUNUZhX7KXA119GuBGRERiFWUL/jxgm7vvcPc+4G7ghuNsfzNwV4T1TIvGluER7NSCFxGR+EQZ8AuA3Smvm8Nlr2NmpcDVwPdTFjvwoJk9bWYbIqtyijUm2jGD1XMV8CIiEp/ITtEDY82R6uNsez3wq1Gn5y9y94SZ1QEPmdmL7v7I6w4ShP8GgMWLF0+25klrSiRZVlNGWXGUX62IiMjxRdmCbwYWpbxeCCTG2fYmRp2ed/dE+HM/cA/BKf/XcfeN7t7g7g21tbWTLnqymlradf1dRERiF2XAPwWsNLOlZlZEEOL3jd7IzKqAS4F7U5aVmVnF8HPgSqAxwlqnxMGuPhLtRzTAjYiIxC6y88juPmBmtwIPAPnAHe7eZGYfCtffHm76X4AH3b0rZfc5wD1mNlzjd9z9p1HVOlWawilidYuciIjELdILxe5+P3D/qGW3j3p9J3DnqGU7gLOirC0KjS3BELUag15EROKmkeymUGOinUWzZ1BVWhh3KSIicopTwE+hppZ21s1T611EROKngJ8iHUf62XWgWwPciIhIRlDAT5Et4RSx63SLnIiIZAAF/BRpHA549aAXEZEMoICfIk2JduoqiqmrKIm7FBEREQX8VGlqSWoEOxERyRgK+CnQ0zfIK/s7OF2n50VEJEMo4KfAi3uTDDms1QA3IiKSIRTwU6Ap7GCnW+RERCRTKOCnQFOinZmlhSyYOSPuUkRERAAF/JRobEmybn4l4eQ4IiIisVPAT1L/4BAv7e3QFLEiIpJRFPCT9Mq+TvoGhzSCnYiIZBQF/CQ1hnPA6xY5ERHJJAr4SWpqaaesKJ/66rK4SxERERmhgJ+kpkSStfMryctTBzsREckcCvhJGBxytuxJsk4d7EREJMMo4CdhZ1sX3X2DmkFOREQyjgJ+EpqGO9ipB72IiGQYBfwkNCWSFBXksaKuPO5SREREjqGAn4TGlnbWzK2gMF9fo4iIZBYl00ly97AHvU7Pi4hI5ok04M3sajN7ycy2mdmnx1i/3szazey58PG5dPeNW/OhHtp7+jWDnIiIZKSCqN7YzPKBrwBXAM3AU2Z2n7tvGbXpo+5+3UnuG5uRDnZqwYuISAaKsgV/HrDN3Xe4ex9wN3DDNOw7LRpbkuTnGavmVsRdioiIyOtEGfALgN0pr5vDZaNdaGbPm9lPzGzdBPfFzDaY2SYz29Ta2joVdaelKdHOyrpySgrzp+2YIiIi6Yoy4Mcau9VHvX4GWOLuZwH/AvxwAvsGC903unuDuzfU1taebK0T1pjQCHYiIpK5ogz4ZmBRyuuFQCJ1A3dPuntn+Px+oNDMatLZN077k0do7ejVCHYiIpKxogz4p4CVZrbUzIqAm4D7Ujcws7lmZuHz88J6DqSzb5yaEklAI9iJiEjmiqwXvbsPmNmtwANAPnCHuzeZ2YfC9bcDNwIfNrMBoAe4yd0dGHPfqGqdqMaWoAf9WrXgRUQkQ0UW8DBy2v3+UctuT3l+G3BbuvtmisZEO8tqyigvjvTrExEROWkaye4kNLYk1XoXEZGMpoCfoMPdfbQc7tH1dxERyWgK+Aka6WCnW+RERCSDKeAnaLiDnW6RExGRTKaAn6CmRJIFM2cwq6wo7lJERETGpYCfoMZEu1rvIiKS8RTwE9DZO8DOti51sBMRkYyngJ+ArXuSuOv6u4iIZD4F/AQ0hR3s1IIXEZFMp4CfgMZEkpryYuoqiuMuRURE5LgU8BPQ2BJ0sAvnxxEREclYCvg0HekfZNv+Tk5foOvvIiKS+RTwaXp5XwcDQ64R7EREJCso4NPU2BIMUbtOAS8iIllAAZ+mxkQ7FSUFLJo9I+5SRERETkgBn6amRJLT51epg52IiGQFBXwa+geH2LonqQ52IiKSNRTwadje2knfwJCuv4uISNZQwKehKexgpxa8iIhkCwV8GhoT7cwozGdpTXncpYiIiKRFAZ+GppYka+ZVkJ+nDnYiIpIdFPAnMDTkNCXaNcGMiIhklUgD3syuNrOXzGybmX16jPW3mNkL4ePXZnZWyrpdZrbZzJ4zs01R1nk8rx7spqtvUCPYiYhIVimI6o3NLB/4CnAF0Aw8ZWb3ufuWlM12Ape6+yEzuwbYCJyfsv4yd2+LqsZ0NIZTxK5TBzsREckiUbbgzwO2ufsOd+8D7gZuSN3A3X/t7ofCl08ACyOs56Q0JtopzDdW1lXEXYqIiEjaogz4BcDulNfN4bLxvB/4ScprBx40s6fNbEME9aVlSyLJqrkVFBWou4KIiGSPyE7RA2N1OfcxNzS7jCDgL05ZfJG7J8ysDnjIzF5090fG2HcDsAFg8eLFk686tVh3GlvauWrd3Cl9XxERkahF2SxtBhalvF4IJEZvZGZnAl8HbnD3A8PL3T0R/twP3ENwyv913H2juze4e0Ntbe0Ulg+J9iMc6u5n3XxdfxcRkewSZcA/Baw0s6VmVgTcBNyXuoGZLQZ+ALzb3V9OWV5mZhXDz4ErgcYIax3T0Q526kEvIiLZJbJT9O4+YGa3Ag8A+cAd7t5kZh8K198OfA6oBr4aztI24O4NwBzgnnBZAfAdd/9pVLWOpymRJM9gzVy14EVEJLtEeQ0ed78fuH/UsttTnn8A+MAY++0Azhq9fLo1tbSzvLacGUX5cZciIiIyIeoafhyNGsFORESylAJ+HK0dvexL9qqDnYiIZCUF/DiaEkEHO7XgRUQkGyngx9GUCOaAX6sWvIiIZCEF/DiaEu0sqS6lsqQw7lJEREQmTAE/jsaWpGaQExGRrBXpbXLZyt352FtXUldZHHcpIiIiJ0UBPwYz43ffkHET24mIiKRNp+hFRERykAJeREQkByngRUREcpACXkREJAcp4EVERHKQAl5ERCQHKeBFRERykAJeREQkByngRUREcpC5e9w1TBkzawVenaK3qwHapui9TlX6DidP3+Hk6TucGvoeJy+K73CJu9eOtSKnAn4qmdkmd2+Iu45spu9w8vQdTp6+w6mh73Hypvs71Cl6ERGRHKSAFxERyUEK+PFtjLuAHKDvcPL0HU6evsOpoe9x8qb1O9Q1eBERkRykFryIiEgOUsCPYmZXm9lLZrbNzD4ddz3ZyMzuMLP9ZtYYdy3ZyswWmdkvzGyrmTWZ2cfirinbmFmJmf3GzJ4Pv8Mvxl1TtjKzfDN71sx+FHct2crMdpnZZjN7zsw2TcsxdYr+KDPLB14GrgCagaeAm919S6yFZRkzuwToBL7h7qfHXU82MrN5wDx3f8bMKoCngbfpv8X0mZkBZe7eaWaFwGPAx9z9iZhLyzpm9idAA1Dp7tfFXU82MrNdQIO7T9tYAmrBH+s8YJu773D3PuBu4IaYa8o67v4IcDDuOrKZu+9x92fC5x3AVmBBvFVlFw90hi8Lw4daNBNkZguB3wK+HnctMjEK+GMtAHanvG5G/6hKzMysHjgHeDLmUrJOeGr5OWA/8JC76zucuH8EPgUMxVxHtnPgQTN72sw2TMcBFfDHsjGW6S9+iY2ZlQPfBz7u7sm468k27j7o7mcDC4HzzEyXjCbAzK4D9rv703HXkgMucvdzgWuAj4SXMiOlgD9WM7Ao5fVCIBFTLXKKC68bfx/4trv/IO56spm7HwYeBq6Ot5KscxHw2+H147uBt5jZt+ItKTu5eyL8uR+4h+CScKQU8Md6ClhpZkvNrAi4Cbgv5prkFBR2EPt3YKu7/33c9WQjM6s1s5nh8xnA5cCLsRaVZdz9M+6+0N3rCf49/Lm7vyvmsrKOmZWFnWUxszLgSiDyu4wU8CncfQC4FXiAoFPTf7h7U7xVZR8zuwt4HFhlZs1m9v64a8pCFwHvJmgxPRc+ro27qCwzD/iFmb1A8Mf7Q+6u27wkDnOAx8zseeA3wI/d/adRH1S3yYmIiOQgteBFRERykAJeREQkByngRUREcpACXkREJAcp4EVERHKQAl5kHGb22XAWshfC29TOD5d/3MxKp/A4u8ysZhL7v9fMbhtneWs4C9grZvaAmb1pEsf5SzO7PI1a5qe8/rqZrT3ZY6ZRU7GZ/Wf4+3nHqHV3mtnOcN3zZvbWE7xXvZm9M6paRaZbQdwFiGQiM7sQuA441917wwAuCld/HPgW0B1TbfnuPpjm5t9191vD/S4DfmBml7n71oke190/l8Zm7yUYwGN41K4PTPQ4E3QOUBgORzuWT7r798LPvhFYeZz3qgfeCXxnIgVM8PchMm3UghcZ2zygzd17Ady9zd0TZvZRYD7BACq/ADCzr5nZptFzjoct8y+a2TPhPNCrw+XVZvZg2LL+V1LmQDCzH4aTUTSlTkhhZp1hC/pJ4EIze5+ZvWxmvyQYFOeE3P0XBCG3IXzP5Wb20/B4j5rZajOrCuvOC7cpNbPdZlYYtohvDJd/zsyeMrNGM9togRsJphT9dthqnmFmD5tZQ7jPzeH30Ghmfzvqs/1N2Mp+wszmjK7dzGaH380L4TZnmlkdwR9aZ4fHW36cj/844cRRFkxA8+Ww/hfM7A/Cbb4EvDl8rz8efWbEzH5kZuvH+X2M+RnM7O3h533ezB5J5/ckMlUU8CJjexBYFIboV83sUgB3/2eC1ull7n5ZuO1n3b0BOBO41MzOTHmftnCCia8BnwiXfR54zN3PIRgKeXHK9v/V3d9AEJQfNbPqcHkZ0Oju5wPbgS8SBPsVwEROgT8DrA6fbwT+KDzeJ4Cvuns78DxwabjN9cAD7t4/6n1uc/c3uvvpwAzgOnf/HrAJuMXdz3b3nuGNw9P2fwu8BTgbeKOZvS3lsz3h7mcBjwAfHKPuLwLPuvuZwJ8B3wjH9P4A8Gh4vO3H+dxXAz8Mn78faHf3NwJvBD5oZkuBT6e81z8c572Ga2509/Pd/bHjfIbPAVeFy3/7BO8pMqUU8CJjCOcRfwNBa7cV+K6ZvXeczX/PzJ4BngXWcWzgDk8S8zTBKWCASwhanrj7j4FDKdt/1ILhLJ8gmPho+JTyIMHEMwDnAw+7e6u79wHfncBHMxiZpe5NwP+1YDrVfyU4a0H4fsPXs28a5/0vM7MnzWwzQWivO8Fx35hS8wDwbYLvAaAPGB5CNvV7SnUx8E0Ad/85UG1mVSc4JsCXzWwHwff938NlVwLvCT/3k0A1xz91P5bU3weM/xl+BdxpZh8E8id4DJFJ0TV4kXGE11UfBh4Og+z3gTtTtwlbfp8A3ujuh8zsTqAkZZPe8Ocgx/7/9roxosPTv5cDF7p7t5k9nPJeR0Zd5z3ZMabPIZhnIQ84PM616/uA/2Fmswn+yPn5qDpLgK8CDe6+28y+wLGfeSxjTcU8rN+Pjpk9+ns63v7pfAefJPgj66PA/yH4PEZw5uKBYw4Qnn5PMcCxjaDUzzj69zHmZ3D3D1nQOfO3gOfM7Gx3P5BG3SKTpha8yBjMbJWZpbbqzgZeDZ93ABXh80qgC2gPr7tek8bbPwLcEh7nGmBWuLwKOBSG+2rggnH2fxJYH17LLwTenuZnupTgjMS/hXPL7zSzt4frzMzOgpGzF78B/gn40RgdyIaDri08E3BjyrrU72Z0zZeaWY2Z5QM3A79Mp+5Q6ne2nuDSRzKdHd19iOCz5JnZVQSTSX04/O4ws9MsmOFrdO27CK7v55nZIk5iek8zW+7uT4YdFNs4djpqkUipBS8ytnLgXyyYbnQA2EbYOY3g2vVPzGyPu19mZs8CTcAOglOyJ/JF4K7wtP4vgdfC5T8FPmTB7GcvEZymfx133xO2mh8H9hBcVx/v9O87zOxioBTYCfxuSg/6W4CvmdmfA4UE830/H677LvB/gfVjHP+wmf0bsJkgBJ9KWX0ncLuZ9QAXjqr5M8AvCFrQ97v7vePUPJYvAP87/G66Cc6mpM3d3cz+GvgUQb+FeuAZMzOCSzBvA14ABsJLJHcC/0jwnW0muDPgmYkcM/Tl8A9FA37G0e9XJHKaTU5ERCQH6RS9iIhIDlLAi4iI5CAFvIiISA5SwIuIiOQgBbyIiEgOUsCLiIjkIAW8iIhIDlLAi4iI5KD/D8aJHGn+OnViAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot frontier\n",
    "plt.figure(figsize=(8, 4.5))\n",
    "#plt.scatter(asset_vols, mu)\n",
    "\n",
    "x = ret_df['std']\n",
    "y = ret_df['return']\n",
    "plt.xlabel(\"Standard Deviation of Returns\")\n",
    "plt.ylabel(\"Return\")\n",
    "plt.title(\"Risk vs. Return\")\n",
    "\n",
    "# plot the data\n",
    "plt.plot(x,y);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TRIALS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year 0 :  1025000\n",
      "year 1 :   975000\n",
      "year 2 :   925000\n",
      "year 3 :   875000\n",
      "year 4 :   825000\n",
      "year 5 :   775000\n",
      "year 6 :   725000\n",
      "year 7 :   675000\n",
      "year 8 :   625000\n",
      "year 9 :   575000\n",
      "year 10:   525000\n",
      "year 11:   475000\n",
      "year 12:   425000\n",
      "year 13:   375000\n",
      "year 14:   325000\n",
      "year 15:   275000\n",
      "year 16:   225000\n",
      "year 17:   175000\n",
      "year 18:   125000\n",
      "year 19:    75000\n",
      "year 20:    25000\n",
      "year 21:        0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def trial(vol=0,\n",
    "          startval=1025000,\n",
    "          spend=50000,\n",
    "          ret_expected=0.0,\n",
    "          vol_expected=0.0, \n",
    "          verbose=False):\n",
    "\n",
    "    year = 0\n",
    "    val = startval\n",
    "    while val > 0:\n",
    "        if verbose:\n",
    "            print(\"year %-2d: %8.0f\" % (year, val))\n",
    "        ret = np.random.normal(loc=ret_expected,\n",
    "                               scale=vol_expected)\n",
    "        val -= spend\n",
    "        # lognormal distribution\n",
    "        val = np.exp(np.log(val) + ret) if val > 0 else 0\n",
    "        val = max(0, val)\n",
    "        year += 1\n",
    "\n",
    "    if verbose:\n",
    "        print(\"year %-2d: %8.0f\" % (year, val))\n",
    "        \n",
    "    return(year)\n",
    "\n",
    "trial(vol_expected=0.0, verbose=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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