analisys_efficient_frontier3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "#%autoreload 2\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "#import riskfolio as rp\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.optimize import minimize, NonlinearConstraint\n",
    "from scipy.stats import norm\n",
    "\n",
    "#from efficientfrontier import optimize\n",
    "\n",
    "pd.options.display.float_format = '{:.2%}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MSCI ACWI', 'MWITR', 'TBSP', 'EURPLN', 'Bloomberg Barclays Euro Aggrega', 'EDO', 'Bloomberg Barclays Global Aggre', 'Gold spot price', 'Global aggregate euro hdg', 'EURO STOXX 50 Daily Leverage', 'Bloomberg Barclays Global Corpo']\n"
     ]
    }
   ],
   "source": [
    "xl = pd.ExcelFile('wartosci_walorow_12_22.xlsx')\n",
    "sheets = xl.sheet_names\n",
    "\n",
    "merged = xl.parse(sheets.pop(0))\n",
    "print(sheets)\n",
    "\n",
    "for sheet in sheets:\n",
    "    data = xl.parse(sheet)    \n",
    "    merged = merged.merge(data, on='Date', how='left') \n",
    "\n",
    "xl.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 456 entries, 0 to 455\n",
      "Data columns (total 13 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   Date                 456 non-null    datetime64[ns]\n",
      " 1   GovBondG7            456 non-null    float64       \n",
      " 2   ACWI                 421 non-null    float64       \n",
      " 3   Mwig40tr             156 non-null    float64       \n",
      " 4   TBSP                 192 non-null    float64       \n",
      " 5   EURPLN               255 non-null    float64       \n",
      " 6   Euro_Corporate_Bond  164 non-null    float64       \n",
      " 7   EDO                  226 non-null    float64       \n",
      " 8   GlobalCorporate      124 non-null    float64       \n",
      " 9   Gold                 456 non-null    float64       \n",
      " 10  GABHDG               62 non-null     float64       \n",
      " 11  EURO_STOXX_Leverage  253 non-null    float64       \n",
      " 12  Fallen_Angels        79 non-null     float64       \n",
      "dtypes: datetime64[ns](1), float64(12)\n",
      "memory usage: 49.9 KB\n"
     ]
    }
   ],
   "source": [
    "merged.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "assets = merged.columns[1:]\n",
    "price_changes = merged[assets].pct_change()\n",
    "price_changes.set_index(merged['Date'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_changes.to_excel('oryginal_price_changes.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "moving_corr = price_changes.iloc[-150:][['ACWI']].rolling(50).corr(price_changes['Mwig40tr'])\n",
    "moving_corr60 = price_changes.iloc[-150:][['EURPLN']].rolling(50).corr(price_changes['ACWI'])\n",
    "fig, ax = plt.subplots(figsize=(16,8))\n",
    "sns.lineplot(data=moving_corr, ax=ax, palette='mako')\n",
    "sns.lineplot(data=moving_corr60, ax=ax, palette='Wistia_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "moving_corr = price_changes.iloc[-240:][['ACWI']].rolling(60).std()\n",
    "moving_corr60 = price_changes.iloc[-240:][['EURPLN']].rolling(60).std()\n",
    "fig, ax = plt.subplots(figsize=(16,8))\n",
    "sns.lineplot(data=moving_corr, ax=ax, palette='mako')\n",
    "sns.lineplot(data=moving_corr60, ax=ax, palette='Wistia_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "euro_assets = ['ACWI', 'GovBondG7', 'GlobalCorporate', 'Gold', 'GABHDG', 'EURO_STOXX_Leverage', 'Fallen_Angels']\n",
    "pln_assets = ['Mwig40tr', 'TBSP', 'EDO']\n",
    "\n",
    "pln = merged[euro_assets].apply(lambda x: x * merged['EURPLN'])\n",
    "pln_chng = pln.pct_change()\n",
    "pln_chng.set_index(merged['Date'], inplace=True)\n",
    "pln_chng = pd.merge(price_changes[pln_assets], pln_chng, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pln_chng.to_excel('pln_price_changes2.xlsx')\n",
    "pln_chng.to_csv('pln_price_changes2.csv', float_format='%.3f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 456 entries, 1985-01-01 to 2022-12-01\n",
      "Data columns (total 10 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Mwig40tr             155 non-null    float64\n",
      " 1   TBSP                 191 non-null    float64\n",
      " 2   EDO                  225 non-null    float64\n",
      " 3   ACWI                 254 non-null    float64\n",
      " 4   GovBondG7            254 non-null    float64\n",
      " 5   GlobalCorporate      123 non-null    float64\n",
      " 6   Gold                 254 non-null    float64\n",
      " 7   GABHDG               61 non-null     float64\n",
      " 8   EURO_STOXX_Leverage  252 non-null    float64\n",
      " 9   Fallen_Angels        78 non-null     float64\n",
      "dtypes: float64(10)\n",
      "memory usage: 55.4 KB\n"
     ]
    }
   ],
   "source": [
    "assets = pln_chng\n",
    "assets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def annullised_mean(series):\n",
    "    return np.mean(series)*12\n",
    "\n",
    "def annullised_std(series):\n",
    "    return np.std(series) * np.sqrt(12)\n",
    "\n",
    "def pstd(weights, cov_matrix):\n",
    "    #variance = np.transpose(weights)@cov_matrix@weights\n",
    "    variance = np.dot(weights.T, np.dot(cov_matrix, weights))\n",
    "    return np.sqrt(variance)\n",
    "\n",
    "def pmean(weightsDf, means):  \n",
    "    weights = weightsDf\n",
    "    return np.sum(weights * means)\n",
    "\n",
    "def portfolioMeasures(weights,means,cov_table):\n",
    "    mu = np.apply_along_axis(pmean,1,weights, means=means)\n",
    "    std = np.apply_along_axis(pstd,1,weights, cov_matrix=cov_table) \n",
    "    return np.array([mu,std]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>GovBondG7</th>\n",
       "      <th>ACWI</th>\n",
       "      <th>Mwig40tr</th>\n",
       "      <th>TBSP</th>\n",
       "      <th>EURPLN</th>\n",
       "      <th>Euro_Corporate_Bond</th>\n",
       "      <th>EDO</th>\n",
       "      <th>GlobalCorporate</th>\n",
       "      <th>Gold</th>\n",
       "      <th>GABHDG</th>\n",
       "      <th>EURO_STOXX_Leverage</th>\n",
       "      <th>Fallen_Angels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.41%</td>\n",
       "      <td>10.94%</td>\n",
       "      <td>8.38%</td>\n",
       "      <td>3.07%</td>\n",
       "      <td>1.29%</td>\n",
       "      <td>3.19%</td>\n",
       "      <td>3.53%</td>\n",
       "      <td>3.13%</td>\n",
       "      <td>5.32%</td>\n",
       "      <td>-1.38%</td>\n",
       "      <td>14.84%</td>\n",
       "      <td>6.32%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.62%</td>\n",
       "      <td>12.60%</td>\n",
       "      <td>17.63%</td>\n",
       "      <td>4.40%</td>\n",
       "      <td>6.14%</td>\n",
       "      <td>3.69%</td>\n",
       "      <td>0.55%</td>\n",
       "      <td>6.75%</td>\n",
       "      <td>15.42%</td>\n",
       "      <td>6.29%</td>\n",
       "      <td>34.41%</td>\n",
       "      <td>8.50%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      GovBondG7   ACWI  Mwig40tr  TBSP  EURPLN  Euro_Corporate_Bond   EDO  \\\n",
       "mean      1.41% 10.94%     8.38% 3.07%   1.29%                3.19% 3.53%   \n",
       "std       7.62% 12.60%    17.63% 4.40%   6.14%                3.69% 0.55%   \n",
       "\n",
       "      GlobalCorporate   Gold  GABHDG  EURO_STOXX_Leverage  Fallen_Angels  \n",
       "mean            3.13%  5.32%  -1.38%               14.84%          6.32%  \n",
       "std             6.75% 15.42%   6.29%               34.41%          8.50%  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "native = price_changes.iloc[-150:].agg(['mean', np.std])\n",
    "native.iloc[0,:] = native.iloc[0,:]*12\n",
    "native.iloc[1,:] = native.iloc[1,:] * np.sqrt(12)\n",
    "native\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>GovBondG7</th>\n",
       "      <th>ACWI</th>\n",
       "      <th>Mwig40tr</th>\n",
       "      <th>TBSP</th>\n",
       "      <th>EURPLN</th>\n",
       "      <th>Euro_Corporate_Bond</th>\n",
       "      <th>EDO</th>\n",
       "      <th>GlobalCorporate</th>\n",
       "      <th>Gold</th>\n",
       "      <th>GABHDG</th>\n",
       "      <th>EURO_STOXX_Leverage</th>\n",
       "      <th>Fallen_Angels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GovBondG7</th>\n",
       "      <td>100.00%</td>\n",
       "      <td>0.59%</td>\n",
       "      <td>-9.45%</td>\n",
       "      <td>-8.15%</td>\n",
       "      <td>3.55%</td>\n",
       "      <td>14.67%</td>\n",
       "      <td>-16.39%</td>\n",
       "      <td>79.19%</td>\n",
       "      <td>43.55%</td>\n",
       "      <td>24.68%</td>\n",
       "      <td>-28.91%</td>\n",
       "      <td>31.68%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACWI</th>\n",
       "      <td>0.59%</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>-6.74%</td>\n",
       "      <td>-7.66%</td>\n",
       "      <td>16.14%</td>\n",
       "      <td>49.71%</td>\n",
       "      <td>-3.54%</td>\n",
       "      <td>54.87%</td>\n",
       "      <td>-1.83%</td>\n",
       "      <td>31.69%</td>\n",
       "      <td>79.54%</td>\n",
       "      <td>77.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mwig40tr</th>\n",
       "      <td>-9.45%</td>\n",
       "      <td>-6.74%</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>6.49%</td>\n",
       "      <td>-56.05%</td>\n",
       "      <td>-4.70%</td>\n",
       "      <td>-2.44%</td>\n",
       "      <td>-6.76%</td>\n",
       "      <td>-6.70%</td>\n",
       "      <td>4.95%</td>\n",
       "      <td>-4.24%</td>\n",
       "      <td>-2.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TBSP</th>\n",
       "      <td>-8.15%</td>\n",
       "      <td>-7.66%</td>\n",
       "      <td>6.49%</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>-16.81%</td>\n",
       "      <td>5.18%</td>\n",
       "      <td>-5.99%</td>\n",
       "      <td>-8.09%</td>\n",
       "      <td>-2.93%</td>\n",
       "      <td>6.34%</td>\n",
       "      <td>-8.04%</td>\n",
       "      <td>-0.57%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EURPLN</th>\n",
       "      <td>3.55%</td>\n",
       "      <td>16.14%</td>\n",
       "      <td>-56.05%</td>\n",
       "      <td>-16.81%</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>0.18%</td>\n",
       "      <td>0.91%</td>\n",
       "      <td>14.41%</td>\n",
       "      <td>-5.63%</td>\n",
       "      <td>3.36%</td>\n",
       "      <td>11.31%</td>\n",
       "      <td>25.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Euro_Corporate_Bond</th>\n",
       "      <td>14.67%</td>\n",
       "      <td>49.71%</td>\n",
       "      <td>-4.70%</td>\n",
       "      <td>5.18%</td>\n",
       "      <td>0.18%</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>0.96%</td>\n",
       "      <td>61.97%</td>\n",
       "      <td>16.92%</td>\n",
       "      <td>33.41%</td>\n",
       "      <td>41.82%</td>\n",
       "      <td>74.31%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDO</th>\n",
       "      <td>-16.39%</td>\n",
       "      <td>-3.54%</td>\n",
       "      <td>-2.44%</td>\n",
       "      <td>-5.99%</td>\n",
       "      <td>0.91%</td>\n",
       "      <td>0.96%</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>-17.61%</td>\n",
       "      <td>-4.14%</td>\n",
       "      <td>-17.46%</td>\n",
       "      <td>4.64%</td>\n",
       "      <td>-10.04%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GlobalCorporate</th>\n",
       "      <td>79.19%</td>\n",
       "      <td>54.87%</td>\n",
       "      <td>-6.76%</td>\n",
       "      <td>-8.09%</td>\n",
       "      <td>14.41%</td>\n",
       "      <td>61.97%</td>\n",
       "      <td>-17.61%</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>35.42%</td>\n",
       "      <td>37.72%</td>\n",
       "      <td>32.77%</td>\n",
       "      <td>83.58%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gold</th>\n",
       "      <td>43.55%</td>\n",
       "      <td>-1.83%</td>\n",
       "      <td>-6.70%</td>\n",
       "      <td>-2.93%</td>\n",
       "      <td>-5.63%</td>\n",
       "      <td>16.92%</td>\n",
       "      <td>-4.14%</td>\n",
       "      <td>35.42%</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>21.00%</td>\n",
       "      <td>-21.57%</td>\n",
       "      <td>5.96%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GABHDG</th>\n",
       "      <td>24.68%</td>\n",
       "      <td>31.69%</td>\n",
       "      <td>4.95%</td>\n",
       "      <td>6.34%</td>\n",
       "      <td>3.36%</td>\n",
       "      <td>33.41%</td>\n",
       "      <td>-17.46%</td>\n",
       "      <td>37.72%</td>\n",
       "      <td>21.00%</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>36.77%</td>\n",
       "      <td>29.09%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EURO_STOXX_Leverage</th>\n",
       "      <td>-28.91%</td>\n",
       "      <td>79.54%</td>\n",
       "      <td>-4.24%</td>\n",
       "      <td>-8.04%</td>\n",
       "      <td>11.31%</td>\n",
       "      <td>41.82%</td>\n",
       "      <td>4.64%</td>\n",
       "      <td>32.77%</td>\n",
       "      <td>-21.57%</td>\n",
       "      <td>36.77%</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>57.55%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fallen_Angels</th>\n",
       "      <td>31.68%</td>\n",
       "      <td>77.30%</td>\n",
       "      <td>-2.60%</td>\n",
       "      <td>-0.57%</td>\n",
       "      <td>25.49%</td>\n",
       "      <td>74.31%</td>\n",
       "      <td>-10.04%</td>\n",
       "      <td>83.58%</td>\n",
       "      <td>5.96%</td>\n",
       "      <td>29.09%</td>\n",
       "      <td>57.55%</td>\n",
       "      <td>100.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     GovBondG7    ACWI  Mwig40tr    TBSP  EURPLN  \\\n",
       "GovBondG7              100.00%   0.59%    -9.45%  -8.15%   3.55%   \n",
       "ACWI                     0.59% 100.00%    -6.74%  -7.66%  16.14%   \n",
       "Mwig40tr                -9.45%  -6.74%   100.00%   6.49% -56.05%   \n",
       "TBSP                    -8.15%  -7.66%     6.49% 100.00% -16.81%   \n",
       "EURPLN                   3.55%  16.14%   -56.05% -16.81% 100.00%   \n",
       "Euro_Corporate_Bond     14.67%  49.71%    -4.70%   5.18%   0.18%   \n",
       "EDO                    -16.39%  -3.54%    -2.44%  -5.99%   0.91%   \n",
       "GlobalCorporate         79.19%  54.87%    -6.76%  -8.09%  14.41%   \n",
       "Gold                    43.55%  -1.83%    -6.70%  -2.93%  -5.63%   \n",
       "GABHDG                  24.68%  31.69%     4.95%   6.34%   3.36%   \n",
       "EURO_STOXX_Leverage    -28.91%  79.54%    -4.24%  -8.04%  11.31%   \n",
       "Fallen_Angels           31.68%  77.30%    -2.60%  -0.57%  25.49%   \n",
       "\n",
       "                     Euro_Corporate_Bond     EDO  GlobalCorporate    Gold  \\\n",
       "GovBondG7                         14.67% -16.39%           79.19%  43.55%   \n",
       "ACWI                              49.71%  -3.54%           54.87%  -1.83%   \n",
       "Mwig40tr                          -4.70%  -2.44%           -6.76%  -6.70%   \n",
       "TBSP                               5.18%  -5.99%           -8.09%  -2.93%   \n",
       "EURPLN                             0.18%   0.91%           14.41%  -5.63%   \n",
       "Euro_Corporate_Bond              100.00%   0.96%           61.97%  16.92%   \n",
       "EDO                                0.96% 100.00%          -17.61%  -4.14%   \n",
       "GlobalCorporate                   61.97% -17.61%          100.00%  35.42%   \n",
       "Gold                              16.92%  -4.14%           35.42% 100.00%   \n",
       "GABHDG                            33.41% -17.46%           37.72%  21.00%   \n",
       "EURO_STOXX_Leverage               41.82%   4.64%           32.77% -21.57%   \n",
       "Fallen_Angels                     74.31% -10.04%           83.58%   5.96%   \n",
       "\n",
       "                     GABHDG  EURO_STOXX_Leverage  Fallen_Angels  \n",
       "GovBondG7            24.68%              -28.91%         31.68%  \n",
       "ACWI                 31.69%               79.54%         77.30%  \n",
       "Mwig40tr              4.95%               -4.24%         -2.60%  \n",
       "TBSP                  6.34%               -8.04%         -0.57%  \n",
       "EURPLN                3.36%               11.31%         25.49%  \n",
       "Euro_Corporate_Bond  33.41%               41.82%         74.31%  \n",
       "EDO                 -17.46%                4.64%        -10.04%  \n",
       "GlobalCorporate      37.72%               32.77%         83.58%  \n",
       "Gold                 21.00%              -21.57%          5.96%  \n",
       "GABHDG              100.00%               36.77%         29.09%  \n",
       "EURO_STOXX_Leverage  36.77%              100.00%         57.55%  \n",
       "Fallen_Angels        29.09%               57.55%        100.00%  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_changes.iloc[-150:].corr(method='pearson')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mwig40tr</th>\n",
       "      <th>TBSP</th>\n",
       "      <th>EDO</th>\n",
       "      <th>ACWI</th>\n",
       "      <th>GovBondG7</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.32%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>4.43%</td>\n",
       "      <td>11.67%</td>\n",
       "      <td>2.38%</td>\n",
       "      <td>4.06%</td>\n",
       "      <td>11.94%</td>\n",
       "      <td>0.80%</td>\n",
       "      <td>15.18%</td>\n",
       "      <td>8.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21.69%</td>\n",
       "      <td>5.83%</td>\n",
       "      <td>0.58%</td>\n",
       "      <td>18.02%</td>\n",
       "      <td>8.12%</td>\n",
       "      <td>9.34%</td>\n",
       "      <td>12.30%</td>\n",
       "      <td>8.55%</td>\n",
       "      <td>40.21%</td>\n",
       "      <td>12.23%</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "      Mwig40tr  TBSP   EDO   ACWI  GovBondG7  GlobalCorporate   Gold  GABHDG  \\\n",
       "mean     2.32% 0.00% 4.43% 11.67%      2.38%            4.06% 11.94%   0.80%   \n",
       "std     21.69% 5.83% 0.58% 18.02%      8.12%            9.34% 12.30%   8.55%   \n",
       "\n",
       "      EURO_STOXX_Leverage  Fallen_Angels  \n",
       "mean               15.18%          8.49%  \n",
       "std                40.21%         12.23%  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf60 = assets.iloc[-60:].agg(['mean', np.std])\n",
    "perf60.iloc[0,:] = perf60.iloc[0,:]*12\n",
    "perf60.iloc[1,:] = perf60.iloc[1,:] * np.sqrt(12)\n",
    "perf60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mwig40tr</th>\n",
       "      <th>TBSP</th>\n",
       "      <th>EDO</th>\n",
       "      <th>ACWI</th>\n",
       "      <th>GovBondG7</th>\n",
       "      <th>GlobalCorporate</th>\n",
       "      <th>Gold</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.25%</td>\n",
       "      <td>4.85%</td>\n",
       "      <td>2.89%</td>\n",
       "      <td>11.50%</td>\n",
       "      <td>2.83%</td>\n",
       "      <td>4.25%</td>\n",
       "      <td>9.31%</td>\n",
       "      <td>6.88%</td>\n",
       "      <td>14.45%</td>\n",
       "      <td>8.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>20.76%</td>\n",
       "      <td>1.76%</td>\n",
       "      <td>0.27%</td>\n",
       "      <td>16.40%</td>\n",
       "      <td>7.83%</td>\n",
       "      <td>9.16%</td>\n",
       "      <td>11.93%</td>\n",
       "      <td>6.85%</td>\n",
       "      <td>37.74%</td>\n",
       "      <td>12.19%</td>\n",
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      ],
      "text/plain": [
       "      Mwig40tr  TBSP   EDO   ACWI  GovBondG7  GlobalCorporate   Gold  GABHDG  \\\n",
       "mean     3.25% 4.85% 2.89% 11.50%      2.83%            4.25%  9.31%   6.88%   \n",
       "std     20.76% 1.76% 0.27% 16.40%      7.83%            9.16% 11.93%   6.85%   \n",
       "\n",
       "      EURO_STOXX_Leverage  Fallen_Angels  \n",
       "mean               14.45%          8.28%  \n",
       "std                37.74%         12.19%  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf60 = assets.loc['01-11-2017':'01-12-2021'].agg(['mean', np.std])\n",
    "perf60.iloc[0,:] = perf60.iloc[0,:]*12\n",
    "perf60.iloc[1,:] = perf60.iloc[1,:] * np.sqrt(12)\n",
    "perf60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "#T_fcaef_row1_col2 {\n",
       "  background-color: #f7fcba;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row1_col3 {\n",
       "  background-color: #e6f5ac;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row1_col4, #T_fcaef_row1_col5, #T_fcaef_row3_col6 {\n",
       "  background-color: #d6efa2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row1_col6 {\n",
       "  background-color: #ddf1a6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row1_col7 {\n",
       "  background-color: #bae394;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row1_col8, #T_fcaef_row9_col0 {\n",
       "  background-color: #fcfed4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row1_col9 {\n",
       "  background-color: #ccea9d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row2_col0 {\n",
       "  background-color: #c7e89a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row2_col3 {\n",
       "  background-color: #e8f6ae;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row2_col4, #T_fcaef_row7_col2 {\n",
       "  background-color: #f9fdc2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row2_col5 {\n",
       "  background-color: #f1fab5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row2_col6 {\n",
       "  background-color: #fbfdce;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row2_col7 {\n",
       "  background-color: #ecf7b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row2_col8 {\n",
       "  background-color: #e2f4aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row2_col9, #T_fcaef_row4_col8 {\n",
       "  background-color: #e9f6af;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row3_col0 {\n",
       "  background-color: #fafdcc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row3_col1 {\n",
       "  background-color: #fbfed2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row3_col2, #T_fcaef_row6_col1 {\n",
       "  background-color: #edf8b2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row3_col4 {\n",
       "  background-color: #5fba6c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row3_col5 {\n",
       "  background-color: #187b3f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row3_col7 {\n",
       "  background-color: #31974f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row3_col8 {\n",
       "  background-color: #036b38;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row3_col9 {\n",
       "  background-color: #006636;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row4_col1 {\n",
       "  background-color: #fbfed0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row4_col3 {\n",
       "  background-color: #6fc174;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row4_col5, #T_fcaef_row5_col4 {\n",
       "  background-color: #006335;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row4_col6 {\n",
       "  background-color: #5ab76a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row4_col7 {\n",
       "  background-color: #2e924c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row4_col9, #T_fcaef_row9_col7 {\n",
       "  background-color: #2b8e4a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row5_col0, #T_fcaef_row7_col0 {\n",
       "  background-color: #feffde;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row5_col1 {\n",
       "  background-color: #fafdc8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row5_col2 {\n",
       "  background-color: #fafdcb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row5_col3 {\n",
       "  background-color: #1c7e40;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row5_col6 {\n",
       "  background-color: #79c679;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row5_col7, #T_fcaef_row7_col5 {\n",
       "  background-color: #218242;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row5_col8, #T_fcaef_row7_col6 {\n",
       "  background-color: #77c679;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row5_col9 {\n",
       "  background-color: #005730;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row6_col0 {\n",
       "  background-color: #f5fbb8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row6_col2 {\n",
       "  background-color: #f8fdc1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row6_col3 {\n",
       "  background-color: #c4e799;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row6_col4 {\n",
       "  background-color: #42ab5d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row6_col5 {\n",
       "  background-color: #61bb6d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row6_col7 {\n",
       "  background-color: #5db96b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row6_col9 {\n",
       "  background-color: #81ca7d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row7_col1 {\n",
       "  background-color: #eaf7af;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row7_col3 {\n",
       "  background-color: #359c53;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row7_col4 {\n",
       "  background-color: #2c8f4b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row7_col8 {\n",
       "  background-color: #6bc072;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row7_col9 {\n",
       "  background-color: #2d914b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row8_col0 {\n",
       "  background-color: #edf8b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row8_col2 {\n",
       "  background-color: #d2eda0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row8_col3 {\n",
       "  background-color: #006837;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row8_col5 {\n",
       "  background-color: #56b568;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row8_col6 {\n",
       "  background-color: #fcfed7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row8_col7 {\n",
       "  background-color: #4cb063;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row8_col9 {\n",
       "  background-color: #30954f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row9_col1, #T_fcaef_row9_col2 {\n",
       "  background-color: #f4fbb7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row9_col3 {\n",
       "  background-color: #006737;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row9_col4 {\n",
       "  background-color: #278946;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row9_col5 {\n",
       "  background-color: #005530;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fcaef_row9_col6 {\n",
       "  background-color: #97d385;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fcaef_row9_col8 {\n",
       "  background-color: #3ba358;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_fcaef\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_fcaef_level0_col0\" class=\"col_heading level0 col0\" >Mwig40tr</th>\n",
       "      <th id=\"T_fcaef_level0_col1\" class=\"col_heading level0 col1\" >TBSP</th>\n",
       "      <th id=\"T_fcaef_level0_col2\" class=\"col_heading level0 col2\" >EDO</th>\n",
       "      <th id=\"T_fcaef_level0_col3\" class=\"col_heading level0 col3\" >ACWI</th>\n",
       "      <th id=\"T_fcaef_level0_col4\" class=\"col_heading level0 col4\" >GovBondG7</th>\n",
       "      <th id=\"T_fcaef_level0_col5\" class=\"col_heading level0 col5\" >GlobalCorporate</th>\n",
       "      <th id=\"T_fcaef_level0_col6\" class=\"col_heading level0 col6\" >Gold</th>\n",
       "      <th id=\"T_fcaef_level0_col7\" class=\"col_heading level0 col7\" >GABHDG</th>\n",
       "      <th id=\"T_fcaef_level0_col8\" class=\"col_heading level0 col8\" >EURO_STOXX_Leverage</th>\n",
       "      <th id=\"T_fcaef_level0_col9\" class=\"col_heading level0 col9\" >Fallen_Angels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_fcaef_level0_row0\" class=\"row_heading level0 row0\" >Mwig40tr</th>\n",
       "      <td id=\"T_fcaef_row0_col0\" class=\"data row0 col0\" >100.00%</td>\n",
       "      <td id=\"T_fcaef_row0_col1\" class=\"data row0 col1\" >4.29%</td>\n",
       "      <td id=\"T_fcaef_row0_col2\" class=\"data row0 col2\" >1.81%</td>\n",
       "      <td id=\"T_fcaef_row0_col3\" class=\"data row0 col3\" >-30.80%</td>\n",
       "      <td id=\"T_fcaef_row0_col4\" class=\"data row0 col4\" >-41.21%</td>\n",
       "      <td id=\"T_fcaef_row0_col5\" class=\"data row0 col5\" >-38.10%</td>\n",
       "      <td id=\"T_fcaef_row0_col6\" class=\"data row0 col6\" >-22.17%</td>\n",
       "      <td id=\"T_fcaef_row0_col7\" class=\"data row0 col7\" >-37.96%</td>\n",
       "      <td id=\"T_fcaef_row0_col8\" class=\"data row0 col8\" >-17.20%</td>\n",
       "      <td id=\"T_fcaef_row0_col9\" class=\"data row0 col9\" >-34.14%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fcaef_level0_row1\" class=\"row_heading level0 row1\" >TBSP</th>\n",
       "      <td id=\"T_fcaef_row1_col0\" class=\"data row1 col0\" >4.29%</td>\n",
       "      <td id=\"T_fcaef_row1_col1\" class=\"data row1 col1\" >100.00%</td>\n",
       "      <td id=\"T_fcaef_row1_col2\" class=\"data row1 col2\" >-11.19%</td>\n",
       "      <td id=\"T_fcaef_row1_col3\" class=\"data row1 col3\" >-5.04%</td>\n",
       "      <td id=\"T_fcaef_row1_col4\" class=\"data row1 col4\" >-4.58%</td>\n",
       "      <td id=\"T_fcaef_row1_col5\" class=\"data row1 col5\" >-2.29%</td>\n",
       "      <td id=\"T_fcaef_row1_col6\" class=\"data row1 col6\" >6.80%</td>\n",
       "      <td id=\"T_fcaef_row1_col7\" class=\"data row1 col7\" >8.64%</td>\n",
       "      <td id=\"T_fcaef_row1_col8\" class=\"data row1 col8\" >-11.49%</td>\n",
       "      <td id=\"T_fcaef_row1_col9\" class=\"data row1 col9\" >4.13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fcaef_level0_row2\" class=\"row_heading level0 row2\" >EDO</th>\n",
       "      <td id=\"T_fcaef_row2_col0\" class=\"data row2 col0\" >1.81%</td>\n",
       "      <td id=\"T_fcaef_row2_col1\" class=\"data row2 col1\" >-11.19%</td>\n",
       "      <td id=\"T_fcaef_row2_col2\" class=\"data row2 col2\" >100.00%</td>\n",
       "      <td id=\"T_fcaef_row2_col3\" class=\"data row2 col3\" >-5.86%</td>\n",
       "      <td id=\"T_fcaef_row2_col4\" class=\"data row2 col4\" >-26.96%</td>\n",
       "      <td id=\"T_fcaef_row2_col5\" class=\"data row2 col5\" >-17.48%</td>\n",
       "      <td id=\"T_fcaef_row2_col6\" class=\"data row2 col6\" >-13.98%</td>\n",
       "      <td id=\"T_fcaef_row2_col7\" class=\"data row2 col7\" >-14.08%</td>\n",
       "      <td id=\"T_fcaef_row2_col8\" class=\"data row2 col8\" >7.61%</td>\n",
       "      <td id=\"T_fcaef_row2_col9\" class=\"data row2 col9\" >-9.33%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fcaef_level0_row3\" class=\"row_heading level0 row3\" >ACWI</th>\n",
       "      <td id=\"T_fcaef_row3_col0\" class=\"data row3 col0\" >-30.80%</td>\n",
       "      <td id=\"T_fcaef_row3_col1\" class=\"data row3 col1\" >-5.04%</td>\n",
       "      <td id=\"T_fcaef_row3_col2\" class=\"data row3 col2\" >-5.86%</td>\n",
       "      <td id=\"T_fcaef_row3_col3\" class=\"data row3 col3\" >100.00%</td>\n",
       "      <td id=\"T_fcaef_row3_col4\" class=\"data row3 col4\" >37.32%</td>\n",
       "      <td id=\"T_fcaef_row3_col5\" class=\"data row3 col5\" >70.60%</td>\n",
       "      <td id=\"T_fcaef_row3_col6\" class=\"data row3 col6\" >9.68%</td>\n",
       "      <td id=\"T_fcaef_row3_col7\" class=\"data row3 col7\" >57.11%</td>\n",
       "      <td id=\"T_fcaef_row3_col8\" class=\"data row3 col8\" >83.57%</td>\n",
       "      <td id=\"T_fcaef_row3_col9\" class=\"data row3 col9\" >83.90%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fcaef_level0_row4\" class=\"row_heading level0 row4\" >GovBondG7</th>\n",
       "      <td id=\"T_fcaef_row4_col0\" class=\"data row4 col0\" >-41.21%</td>\n",
       "      <td id=\"T_fcaef_row4_col1\" class=\"data row4 col1\" >-4.58%</td>\n",
       "      <td id=\"T_fcaef_row4_col2\" class=\"data row4 col2\" >-26.96%</td>\n",
       "      <td id=\"T_fcaef_row4_col3\" class=\"data row4 col3\" >37.32%</td>\n",
       "      <td id=\"T_fcaef_row4_col4\" class=\"data row4 col4\" >100.00%</td>\n",
       "      <td id=\"T_fcaef_row4_col5\" class=\"data row4 col5\" >85.02%</td>\n",
       "      <td id=\"T_fcaef_row4_col6\" class=\"data row4 col6\" >47.04%</td>\n",
       "      <td id=\"T_fcaef_row4_col7\" class=\"data row4 col7\" >59.25%</td>\n",
       "      <td id=\"T_fcaef_row4_col8\" class=\"data row4 col8\" >4.40%</td>\n",
       "      <td id=\"T_fcaef_row4_col9\" class=\"data row4 col9\" >62.18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fcaef_level0_row5\" class=\"row_heading level0 row5\" >GlobalCorporate</th>\n",
       "      <td id=\"T_fcaef_row5_col0\" class=\"data row5 col0\" >-38.10%</td>\n",
       "      <td id=\"T_fcaef_row5_col1\" class=\"data row5 col1\" >-2.29%</td>\n",
       "      <td id=\"T_fcaef_row5_col2\" class=\"data row5 col2\" >-17.48%</td>\n",
       "      <td id=\"T_fcaef_row5_col3\" class=\"data row5 col3\" >70.60%</td>\n",
       "      <td id=\"T_fcaef_row5_col4\" class=\"data row5 col4\" >85.02%</td>\n",
       "      <td id=\"T_fcaef_row5_col5\" class=\"data row5 col5\" >100.00%</td>\n",
       "      <td id=\"T_fcaef_row5_col6\" class=\"data row5 col6\" >38.44%</td>\n",
       "      <td id=\"T_fcaef_row5_col7\" class=\"data row5 col7\" >66.15%</td>\n",
       "      <td id=\"T_fcaef_row5_col8\" class=\"data row5 col8\" >41.59%</td>\n",
       "      <td id=\"T_fcaef_row5_col9\" class=\"data row5 col9\" >91.47%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fcaef_level0_row6\" class=\"row_heading level0 row6\" >Gold</th>\n",
       "      <td id=\"T_fcaef_row6_col0\" class=\"data row6 col0\" >-22.17%</td>\n",
       "      <td id=\"T_fcaef_row6_col1\" class=\"data row6 col1\" >6.80%</td>\n",
       "      <td id=\"T_fcaef_row6_col2\" class=\"data row6 col2\" >-13.98%</td>\n",
       "      <td id=\"T_fcaef_row6_col3\" class=\"data row6 col3\" >9.68%</td>\n",
       "      <td id=\"T_fcaef_row6_col4\" class=\"data row6 col4\" >47.04%</td>\n",
       "      <td id=\"T_fcaef_row6_col5\" class=\"data row6 col5\" >38.44%</td>\n",
       "      <td id=\"T_fcaef_row6_col6\" class=\"data row6 col6\" >100.00%</td>\n",
       "      <td id=\"T_fcaef_row6_col7\" class=\"data row6 col7\" >39.38%</td>\n",
       "      <td id=\"T_fcaef_row6_col8\" class=\"data row6 col8\" >-17.38%</td>\n",
       "      <td id=\"T_fcaef_row6_col9\" class=\"data row6 col9\" >30.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fcaef_level0_row7\" class=\"row_heading level0 row7\" >GABHDG</th>\n",
       "      <td id=\"T_fcaef_row7_col0\" class=\"data row7 col0\" >-37.96%</td>\n",
       "      <td id=\"T_fcaef_row7_col1\" class=\"data row7 col1\" >8.64%</td>\n",
       "      <td id=\"T_fcaef_row7_col2\" class=\"data row7 col2\" >-14.08%</td>\n",
       "      <td id=\"T_fcaef_row7_col3\" class=\"data row7 col3\" >57.11%</td>\n",
       "      <td id=\"T_fcaef_row7_col4\" class=\"data row7 col4\" >59.25%</td>\n",
       "      <td id=\"T_fcaef_row7_col5\" class=\"data row7 col5\" >66.15%</td>\n",
       "      <td id=\"T_fcaef_row7_col6\" class=\"data row7 col6\" >39.38%</td>\n",
       "      <td id=\"T_fcaef_row7_col7\" class=\"data row7 col7\" >100.00%</td>\n",
       "      <td id=\"T_fcaef_row7_col8\" class=\"data row7 col8\" >44.68%</td>\n",
       "      <td id=\"T_fcaef_row7_col9\" class=\"data row7 col9\" >61.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fcaef_level0_row8\" class=\"row_heading level0 row8\" >EURO_STOXX_Leverage</th>\n",
       "      <td id=\"T_fcaef_row8_col0\" class=\"data row8 col0\" >-17.20%</td>\n",
       "      <td id=\"T_fcaef_row8_col1\" class=\"data row8 col1\" >-11.49%</td>\n",
       "      <td id=\"T_fcaef_row8_col2\" class=\"data row8 col2\" >7.61%</td>\n",
       "      <td id=\"T_fcaef_row8_col3\" class=\"data row8 col3\" >83.57%</td>\n",
       "      <td id=\"T_fcaef_row8_col4\" class=\"data row8 col4\" >4.40%</td>\n",
       "      <td id=\"T_fcaef_row8_col5\" class=\"data row8 col5\" >41.59%</td>\n",
       "      <td id=\"T_fcaef_row8_col6\" class=\"data row8 col6\" >-17.38%</td>\n",
       "      <td id=\"T_fcaef_row8_col7\" class=\"data row8 col7\" >44.68%</td>\n",
       "      <td id=\"T_fcaef_row8_col8\" class=\"data row8 col8\" >100.00%</td>\n",
       "      <td id=\"T_fcaef_row8_col9\" class=\"data row8 col9\" >58.81%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fcaef_level0_row9\" class=\"row_heading level0 row9\" >Fallen_Angels</th>\n",
       "      <td id=\"T_fcaef_row9_col0\" class=\"data row9 col0\" >-34.14%</td>\n",
       "      <td id=\"T_fcaef_row9_col1\" class=\"data row9 col1\" >4.13%</td>\n",
       "      <td id=\"T_fcaef_row9_col2\" class=\"data row9 col2\" >-9.33%</td>\n",
       "      <td id=\"T_fcaef_row9_col3\" class=\"data row9 col3\" >83.90%</td>\n",
       "      <td id=\"T_fcaef_row9_col4\" class=\"data row9 col4\" >62.18%</td>\n",
       "      <td id=\"T_fcaef_row9_col5\" class=\"data row9 col5\" >91.47%</td>\n",
       "      <td id=\"T_fcaef_row9_col6\" class=\"data row9 col6\" >30.07%</td>\n",
       "      <td id=\"T_fcaef_row9_col7\" class=\"data row9 col7\" >61.17%</td>\n",
       "      <td id=\"T_fcaef_row9_col8\" class=\"data row9 col8\" >58.81%</td>\n",
       "      <td id=\"T_fcaef_row9_col9\" class=\"data row9 col9\" >100.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f4fcdc85e0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr60 = assets.iloc[-60:].corr(method='pearson')\n",
    "#corr60 = assets.iloc[-60:].cov()\n",
    "corr60.style.format(\"{:.2%}\").background_gradient(cmap='YlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Mwig40tr</th>\n",
       "      <th>TBSP</th>\n",
       "      <th>EDO</th>\n",
       "      <th>ACWI</th>\n",
       "      <th>GovBondG7</th>\n",
       "      <th>GlobalCorporate</th>\n",
       "      <th>Gold</th>\n",
       "      <th>GABHDG</th>\n",
       "      <th>EURO_STOXX_Leverage</th>\n",
       "      <th>Fallen_Angels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.26%</td>\n",
       "      <td>1.90%</td>\n",
       "      <td>3.26%</td>\n",
       "      <td>12.81%</td>\n",
       "      <td>2.56%</td>\n",
       "      <td>4.72%</td>\n",
       "      <td>5.43%</td>\n",
       "      <td>0.69%</td>\n",
       "      <td>17.54%</td>\n",
       "      <td>7.54%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.89%</td>\n",
       "      <td>4.73%</td>\n",
       "      <td>0.59%</td>\n",
       "      <td>15.21%</td>\n",
       "      <td>8.94%</td>\n",
       "      <td>9.33%</td>\n",
       "      <td>15.28%</td>\n",
       "      <td>8.48%</td>\n",
       "      <td>35.24%</td>\n",
       "      <td>11.28%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Mwig40tr  TBSP   EDO   ACWI  GovBondG7  GlobalCorporate   Gold  GABHDG  \\\n",
       "mean     9.26% 1.90% 3.26% 12.81%      2.56%            4.72%  5.43%   0.69%   \n",
       "std     17.89% 4.73% 0.59% 15.21%      8.94%            9.33% 15.28%   8.48%   \n",
       "\n",
       "      EURO_STOXX_Leverage  Fallen_Angels  \n",
       "mean               17.54%          7.54%  \n",
       "std                35.24%         11.28%  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf120 = assets.iloc[-120:].agg(['mean', np.std])\n",
    "perf120.iloc[0,:] = perf120.iloc[0,:]*12\n",
    "perf120.iloc[1,:] = perf120.iloc[1,:] * np.sqrt(12)\n",
    "perf120"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  background-color: #83cb7d;\n",
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       "<table id=\"T_4c90b\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_4c90b_level0_col0\" class=\"col_heading level0 col0\" >Mwig40tr</th>\n",
       "      <th id=\"T_4c90b_level0_col1\" class=\"col_heading level0 col1\" >TBSP</th>\n",
       "      <th id=\"T_4c90b_level0_col2\" class=\"col_heading level0 col2\" >EDO</th>\n",
       "      <th id=\"T_4c90b_level0_col3\" class=\"col_heading level0 col3\" >ACWI</th>\n",
       "      <th id=\"T_4c90b_level0_col4\" class=\"col_heading level0 col4\" >GovBondG7</th>\n",
       "      <th id=\"T_4c90b_level0_col5\" class=\"col_heading level0 col5\" >GlobalCorporate</th>\n",
       "      <th id=\"T_4c90b_level0_col6\" class=\"col_heading level0 col6\" >Gold</th>\n",
       "      <th id=\"T_4c90b_level0_col7\" class=\"col_heading level0 col7\" >GABHDG</th>\n",
       "      <th id=\"T_4c90b_level0_col8\" class=\"col_heading level0 col8\" >EURO_STOXX_Leverage</th>\n",
       "      <th id=\"T_4c90b_level0_col9\" class=\"col_heading level0 col9\" >Fallen_Angels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4c90b_level0_row0\" class=\"row_heading level0 row0\" >Mwig40tr</th>\n",
       "      <td id=\"T_4c90b_row0_col0\" class=\"data row0 col0\" >100.00%</td>\n",
       "      <td id=\"T_4c90b_row0_col1\" class=\"data row0 col1\" >4.09%</td>\n",
       "      <td id=\"T_4c90b_row0_col2\" class=\"data row0 col2\" >-2.53%</td>\n",
       "      <td id=\"T_4c90b_row0_col3\" class=\"data row0 col3\" >-26.05%</td>\n",
       "      <td id=\"T_4c90b_row0_col4\" class=\"data row0 col4\" >-35.52%</td>\n",
       "      <td id=\"T_4c90b_row0_col5\" class=\"data row0 col5\" >-34.43%</td>\n",
       "      <td id=\"T_4c90b_row0_col6\" class=\"data row0 col6\" >-29.45%</td>\n",
       "      <td id=\"T_4c90b_row0_col7\" class=\"data row0 col7\" >-37.71%</td>\n",
       "      <td id=\"T_4c90b_row0_col8\" class=\"data row0 col8\" >-13.60%</td>\n",
       "      <td id=\"T_4c90b_row0_col9\" class=\"data row0 col9\" >-29.83%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4c90b_level0_row1\" class=\"row_heading level0 row1\" >TBSP</th>\n",
       "      <td id=\"T_4c90b_row1_col0\" class=\"data row1 col0\" >4.09%</td>\n",
       "      <td id=\"T_4c90b_row1_col1\" class=\"data row1 col1\" >100.00%</td>\n",
       "      <td id=\"T_4c90b_row1_col2\" class=\"data row1 col2\" >-12.85%</td>\n",
       "      <td id=\"T_4c90b_row1_col3\" class=\"data row1 col3\" >-9.22%</td>\n",
       "      <td id=\"T_4c90b_row1_col4\" class=\"data row1 col4\" >-15.34%</td>\n",
       "      <td id=\"T_4c90b_row1_col5\" class=\"data row1 col5\" >-12.34%</td>\n",
       "      <td id=\"T_4c90b_row1_col6\" class=\"data row1 col6\" >-7.32%</td>\n",
       "      <td id=\"T_4c90b_row1_col7\" class=\"data row1 col7\" >8.52%</td>\n",
       "      <td id=\"T_4c90b_row1_col8\" class=\"data row1 col8\" >-9.96%</td>\n",
       "      <td id=\"T_4c90b_row1_col9\" class=\"data row1 col9\" >-0.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4c90b_level0_row2\" class=\"row_heading level0 row2\" >EDO</th>\n",
       "      <td id=\"T_4c90b_row2_col0\" class=\"data row2 col0\" >-2.53%</td>\n",
       "      <td id=\"T_4c90b_row2_col1\" class=\"data row2 col1\" >-12.85%</td>\n",
       "      <td id=\"T_4c90b_row2_col2\" class=\"data row2 col2\" >100.00%</td>\n",
       "      <td id=\"T_4c90b_row2_col3\" class=\"data row2 col3\" >-2.09%</td>\n",
       "      <td id=\"T_4c90b_row2_col4\" class=\"data row2 col4\" >-14.90%</td>\n",
       "      <td id=\"T_4c90b_row2_col5\" class=\"data row2 col5\" >-10.80%</td>\n",
       "      <td id=\"T_4c90b_row2_col6\" class=\"data row2 col6\" >-5.09%</td>\n",
       "      <td id=\"T_4c90b_row2_col7\" class=\"data row2 col7\" >-13.41%</td>\n",
       "      <td id=\"T_4c90b_row2_col8\" class=\"data row2 col8\" >5.30%</td>\n",
       "      <td id=\"T_4c90b_row2_col9\" class=\"data row2 col9\" >-4.69%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4c90b_level0_row3\" class=\"row_heading level0 row3\" >ACWI</th>\n",
       "      <td id=\"T_4c90b_row3_col0\" class=\"data row3 col0\" >-26.05%</td>\n",
       "      <td id=\"T_4c90b_row3_col1\" class=\"data row3 col1\" >-9.22%</td>\n",
       "      <td id=\"T_4c90b_row3_col2\" class=\"data row3 col2\" >-2.09%</td>\n",
       "      <td id=\"T_4c90b_row3_col3\" class=\"data row3 col3\" >100.00%</td>\n",
       "      <td id=\"T_4c90b_row3_col4\" class=\"data row3 col4\" >40.48%</td>\n",
       "      <td id=\"T_4c90b_row3_col5\" class=\"data row3 col5\" >69.42%</td>\n",
       "      <td id=\"T_4c90b_row3_col6\" class=\"data row3 col6\" >16.35%</td>\n",
       "      <td id=\"T_4c90b_row3_col7\" class=\"data row3 col7\" >57.15%</td>\n",
       "      <td id=\"T_4c90b_row3_col8\" class=\"data row3 col8\" >81.87%</td>\n",
       "      <td id=\"T_4c90b_row3_col9\" class=\"data row3 col9\" >84.21%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4c90b_level0_row4\" class=\"row_heading level0 row4\" >GovBondG7</th>\n",
       "      <td id=\"T_4c90b_row4_col0\" class=\"data row4 col0\" >-35.52%</td>\n",
       "      <td id=\"T_4c90b_row4_col1\" class=\"data row4 col1\" >-15.34%</td>\n",
       "      <td id=\"T_4c90b_row4_col2\" class=\"data row4 col2\" >-14.90%</td>\n",
       "      <td id=\"T_4c90b_row4_col3\" class=\"data row4 col3\" >40.48%</td>\n",
       "      <td id=\"T_4c90b_row4_col4\" class=\"data row4 col4\" >100.00%</td>\n",
       "      <td id=\"T_4c90b_row4_col5\" class=\"data row4 col5\" >89.02%</td>\n",
       "      <td id=\"T_4c90b_row4_col6\" class=\"data row4 col6\" >55.72%</td>\n",
       "      <td id=\"T_4c90b_row4_col7\" class=\"data row4 col7\" >59.24%</td>\n",
       "      <td id=\"T_4c90b_row4_col8\" class=\"data row4 col8\" >8.83%</td>\n",
       "      <td id=\"T_4c90b_row4_col9\" class=\"data row4 col9\" >64.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4c90b_level0_row5\" class=\"row_heading level0 row5\" >GlobalCorporate</th>\n",
       "      <td id=\"T_4c90b_row5_col0\" class=\"data row5 col0\" >-34.43%</td>\n",
       "      <td id=\"T_4c90b_row5_col1\" class=\"data row5 col1\" >-12.34%</td>\n",
       "      <td id=\"T_4c90b_row5_col2\" class=\"data row5 col2\" >-10.80%</td>\n",
       "      <td id=\"T_4c90b_row5_col3\" class=\"data row5 col3\" >69.42%</td>\n",
       "      <td id=\"T_4c90b_row5_col4\" class=\"data row5 col4\" >89.02%</td>\n",
       "      <td id=\"T_4c90b_row5_col5\" class=\"data row5 col5\" >100.00%</td>\n",
       "      <td id=\"T_4c90b_row5_col6\" class=\"data row5 col6\" >47.14%</td>\n",
       "      <td id=\"T_4c90b_row5_col7\" class=\"data row5 col7\" >66.16%</td>\n",
       "      <td id=\"T_4c90b_row5_col8\" class=\"data row5 col8\" >40.62%</td>\n",
       "      <td id=\"T_4c90b_row5_col9\" class=\"data row5 col9\" >91.53%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4c90b_level0_row6\" class=\"row_heading level0 row6\" >Gold</th>\n",
       "      <td id=\"T_4c90b_row6_col0\" class=\"data row6 col0\" >-29.45%</td>\n",
       "      <td id=\"T_4c90b_row6_col1\" class=\"data row6 col1\" >-7.32%</td>\n",
       "      <td id=\"T_4c90b_row6_col2\" class=\"data row6 col2\" >-5.09%</td>\n",
       "      <td id=\"T_4c90b_row6_col3\" class=\"data row6 col3\" >16.35%</td>\n",
       "      <td id=\"T_4c90b_row6_col4\" class=\"data row6 col4\" >55.72%</td>\n",
       "      <td id=\"T_4c90b_row6_col5\" class=\"data row6 col5\" >47.14%</td>\n",
       "      <td id=\"T_4c90b_row6_col6\" class=\"data row6 col6\" >100.00%</td>\n",
       "      <td id=\"T_4c90b_row6_col7\" class=\"data row6 col7\" >39.48%</td>\n",
       "      <td id=\"T_4c90b_row6_col8\" class=\"data row6 col8\" >-4.66%</td>\n",
       "      <td id=\"T_4c90b_row6_col9\" class=\"data row6 col9\" >32.20%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4c90b_level0_row7\" class=\"row_heading level0 row7\" >GABHDG</th>\n",
       "      <td id=\"T_4c90b_row7_col0\" class=\"data row7 col0\" >-37.71%</td>\n",
       "      <td id=\"T_4c90b_row7_col1\" class=\"data row7 col1\" >8.52%</td>\n",
       "      <td id=\"T_4c90b_row7_col2\" class=\"data row7 col2\" >-13.41%</td>\n",
       "      <td id=\"T_4c90b_row7_col3\" class=\"data row7 col3\" >57.15%</td>\n",
       "      <td id=\"T_4c90b_row7_col4\" class=\"data row7 col4\" >59.24%</td>\n",
       "      <td id=\"T_4c90b_row7_col5\" class=\"data row7 col5\" >66.16%</td>\n",
       "      <td id=\"T_4c90b_row7_col6\" class=\"data row7 col6\" >39.48%</td>\n",
       "      <td id=\"T_4c90b_row7_col7\" class=\"data row7 col7\" >100.00%</td>\n",
       "      <td id=\"T_4c90b_row7_col8\" class=\"data row7 col8\" >44.77%</td>\n",
       "      <td id=\"T_4c90b_row7_col9\" class=\"data row7 col9\" >61.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4c90b_level0_row8\" class=\"row_heading level0 row8\" >EURO_STOXX_Leverage</th>\n",
       "      <td id=\"T_4c90b_row8_col0\" class=\"data row8 col0\" >-13.60%</td>\n",
       "      <td id=\"T_4c90b_row8_col1\" class=\"data row8 col1\" >-9.96%</td>\n",
       "      <td id=\"T_4c90b_row8_col2\" class=\"data row8 col2\" >5.30%</td>\n",
       "      <td id=\"T_4c90b_row8_col3\" class=\"data row8 col3\" >81.87%</td>\n",
       "      <td id=\"T_4c90b_row8_col4\" class=\"data row8 col4\" >8.83%</td>\n",
       "      <td id=\"T_4c90b_row8_col5\" class=\"data row8 col5\" >40.62%</td>\n",
       "      <td id=\"T_4c90b_row8_col6\" class=\"data row8 col6\" >-4.66%</td>\n",
       "      <td id=\"T_4c90b_row8_col7\" class=\"data row8 col7\" >44.77%</td>\n",
       "      <td id=\"T_4c90b_row8_col8\" class=\"data row8 col8\" >100.00%</td>\n",
       "      <td id=\"T_4c90b_row8_col9\" class=\"data row8 col9\" >59.90%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4c90b_level0_row9\" class=\"row_heading level0 row9\" >Fallen_Angels</th>\n",
       "      <td id=\"T_4c90b_row9_col0\" class=\"data row9 col0\" >-29.83%</td>\n",
       "      <td id=\"T_4c90b_row9_col1\" class=\"data row9 col1\" >-0.25%</td>\n",
       "      <td id=\"T_4c90b_row9_col2\" class=\"data row9 col2\" >-4.69%</td>\n",
       "      <td id=\"T_4c90b_row9_col3\" class=\"data row9 col3\" >84.21%</td>\n",
       "      <td id=\"T_4c90b_row9_col4\" class=\"data row9 col4\" >64.17%</td>\n",
       "      <td id=\"T_4c90b_row9_col5\" class=\"data row9 col5\" >91.53%</td>\n",
       "      <td id=\"T_4c90b_row9_col6\" class=\"data row9 col6\" >32.20%</td>\n",
       "      <td id=\"T_4c90b_row9_col7\" class=\"data row9 col7\" >61.19%</td>\n",
       "      <td id=\"T_4c90b_row9_col8\" class=\"data row9 col8\" >59.90%</td>\n",
       "      <td id=\"T_4c90b_row9_col9\" class=\"data row9 col9\" >100.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f4fd018520>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr120 = assets.iloc[-120:].corr(method='pearson')\n",
    "#corr120 = assets.iloc[-120:].cov()\n",
    "corr120.style.format(\"{:.2%}\").background_gradient(cmap='YlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Mwig40tr</th>\n",
       "      <th>TBSP</th>\n",
       "      <th>EDO</th>\n",
       "      <th>ACWI</th>\n",
       "      <th>GovBondG7</th>\n",
       "      <th>GlobalCorporate</th>\n",
       "      <th>Gold</th>\n",
       "      <th>GABHDG</th>\n",
       "      <th>EURO_STOXX_Leverage</th>\n",
       "      <th>Fallen_Angels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.38%</td>\n",
       "      <td>3.07%</td>\n",
       "      <td>3.53%</td>\n",
       "      <td>12.37%</td>\n",
       "      <td>2.72%</td>\n",
       "      <td>4.44%</td>\n",
       "      <td>6.56%</td>\n",
       "      <td>0.69%</td>\n",
       "      <td>16.38%</td>\n",
       "      <td>7.54%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.63%</td>\n",
       "      <td>4.40%</td>\n",
       "      <td>0.55%</td>\n",
       "      <td>15.03%</td>\n",
       "      <td>9.98%</td>\n",
       "      <td>9.25%</td>\n",
       "      <td>16.38%</td>\n",
       "      <td>8.48%</td>\n",
       "      <td>35.90%</td>\n",
       "      <td>11.28%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Mwig40tr  TBSP   EDO   ACWI  GovBondG7  GlobalCorporate   Gold  GABHDG  \\\n",
       "mean     8.38% 3.07% 3.53% 12.37%      2.72%            4.44%  6.56%   0.69%   \n",
       "std     17.63% 4.40% 0.55% 15.03%      9.98%            9.25% 16.38%   8.48%   \n",
       "\n",
       "      EURO_STOXX_Leverage  Fallen_Angels  \n",
       "mean               16.38%          7.54%  \n",
       "std                35.90%         11.28%  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf180 = assets.iloc[-150:].agg(['mean', np.std])\n",
    "perf180.iloc[0,:] = perf180.iloc[0,:]*12\n",
    "perf180.iloc[1,:] = perf180.iloc[1,:] * np.sqrt(12)\n",
    "perf180"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "#T_a4a21_row3_col4 {\n",
       "  background-color: #64bc6f;\n",
       "  color: #f1f1f1;\n",
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       "#T_a4a21_row3_col5 {\n",
       "  background-color: #1d7f41;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a4a21_row3_col6 {\n",
       "  background-color: #b3e091;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a4a21_row3_col7, #T_a4a21_row8_col9 {\n",
       "  background-color: #31974f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a4a21_row3_col8 {\n",
       "  background-color: #10743c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a4a21_row3_col9, #T_a4a21_row9_col3 {\n",
       "  background-color: #006737;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a4a21_row4_col2 {\n",
       "  background-color: #feffe2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a4a21_row4_col3 {\n",
       "  background-color: #77c679;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a4a21_row4_col5 {\n",
       "  background-color: #005c32;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a4a21_row4_col6 {\n",
       "  background-color: #40aa5c;\n",
       "  color: #f1f1f1;\n",
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       "#T_a4a21_row4_col7 {\n",
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       "#T_a4a21_row4_col9 {\n",
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       "  background-color: #b1df90;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a4a21_row6_col4 {\n",
       "  background-color: #389f55;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a4a21_row6_col5 {\n",
       "  background-color: #47ae60;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a4a21_row6_col7 {\n",
       "  background-color: #5db96b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a4a21_row6_col8 {\n",
       "  background-color: #fcfed6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a4a21_row7_col1 {\n",
       "  background-color: #e2f4aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a4a21_row7_col3 {\n",
       "  background-color: #369d54;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a4a21_row7_col4 {\n",
       "  background-color: #2c8f4b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a4a21_row7_col5, #T_a4a21_row9_col4 {\n",
       "  background-color: #238443;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a4a21_row7_col6 {\n",
       "  background-color: #6dc073;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a4a21_row7_col8 {\n",
       "  background-color: #70c275;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a4a21_row7_col9 {\n",
       "  background-color: #2f934d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a4a21_row8_col3 {\n",
       "  background-color: #0a703a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a4a21_row8_col4 {\n",
       "  background-color: #d2eda0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a4a21_row8_col5 {\n",
       "  background-color: #5fba6c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a4a21_row8_col7 {\n",
       "  background-color: #4cb063;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a4a21_row9_col1 {\n",
       "  background-color: #f4fbb7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a4a21_row9_col2 {\n",
       "  background-color: #fafdcb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a4a21_row9_col6 {\n",
       "  background-color: #84cb7e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a4a21_row9_col7 {\n",
       "  background-color: #2b8e4a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a4a21_row9_col8 {\n",
       "  background-color: #3ba358;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_a4a21\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_a4a21_level0_col0\" class=\"col_heading level0 col0\" >Mwig40tr</th>\n",
       "      <th id=\"T_a4a21_level0_col1\" class=\"col_heading level0 col1\" >TBSP</th>\n",
       "      <th id=\"T_a4a21_level0_col2\" class=\"col_heading level0 col2\" >EDO</th>\n",
       "      <th id=\"T_a4a21_level0_col3\" class=\"col_heading level0 col3\" >ACWI</th>\n",
       "      <th id=\"T_a4a21_level0_col4\" class=\"col_heading level0 col4\" >GovBondG7</th>\n",
       "      <th id=\"T_a4a21_level0_col5\" class=\"col_heading level0 col5\" >GlobalCorporate</th>\n",
       "      <th id=\"T_a4a21_level0_col6\" class=\"col_heading level0 col6\" >Gold</th>\n",
       "      <th id=\"T_a4a21_level0_col7\" class=\"col_heading level0 col7\" >GABHDG</th>\n",
       "      <th id=\"T_a4a21_level0_col8\" class=\"col_heading level0 col8\" >EURO_STOXX_Leverage</th>\n",
       "      <th id=\"T_a4a21_level0_col9\" class=\"col_heading level0 col9\" >Fallen_Angels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a4a21_level0_row0\" class=\"row_heading level0 row0\" >Mwig40tr</th>\n",
       "      <td id=\"T_a4a21_row0_col0\" class=\"data row0 col0\" >100.00%</td>\n",
       "      <td id=\"T_a4a21_row0_col1\" class=\"data row0 col1\" >6.49%</td>\n",
       "      <td id=\"T_a4a21_row0_col2\" class=\"data row0 col2\" >-2.44%</td>\n",
       "      <td id=\"T_a4a21_row0_col3\" class=\"data row0 col3\" >-29.47%</td>\n",
       "      <td id=\"T_a4a21_row0_col4\" class=\"data row0 col4\" >-41.78%</td>\n",
       "      <td id=\"T_a4a21_row0_col5\" class=\"data row0 col5\" >-34.82%</td>\n",
       "      <td id=\"T_a4a21_row0_col6\" class=\"data row0 col6\" >-27.75%</td>\n",
       "      <td id=\"T_a4a21_row0_col7\" class=\"data row0 col7\" >-37.71%</td>\n",
       "      <td id=\"T_a4a21_row0_col8\" class=\"data row0 col8\" >-14.29%</td>\n",
       "      <td id=\"T_a4a21_row0_col9\" class=\"data row0 col9\" >-29.83%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a4a21_level0_row1\" class=\"row_heading level0 row1\" >TBSP</th>\n",
       "      <td id=\"T_a4a21_row1_col0\" class=\"data row1 col0\" >6.49%</td>\n",
       "      <td id=\"T_a4a21_row1_col1\" class=\"data row1 col1\" >100.00%</td>\n",
       "      <td id=\"T_a4a21_row1_col2\" class=\"data row1 col2\" >-5.99%</td>\n",
       "      <td id=\"T_a4a21_row1_col3\" class=\"data row1 col3\" >-13.44%</td>\n",
       "      <td id=\"T_a4a21_row1_col4\" class=\"data row1 col4\" >-16.44%</td>\n",
       "      <td id=\"T_a4a21_row1_col5\" class=\"data row1 col5\" >-13.38%</td>\n",
       "      <td id=\"T_a4a21_row1_col6\" class=\"data row1 col6\" >-8.90%</td>\n",
       "      <td id=\"T_a4a21_row1_col7\" class=\"data row1 col7\" >8.52%</td>\n",
       "      <td id=\"T_a4a21_row1_col8\" class=\"data row1 col8\" >-10.80%</td>\n",
       "      <td id=\"T_a4a21_row1_col9\" class=\"data row1 col9\" >-0.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a4a21_level0_row2\" class=\"row_heading level0 row2\" >EDO</th>\n",
       "      <td id=\"T_a4a21_row2_col0\" class=\"data row2 col0\" >-2.44%</td>\n",
       "      <td id=\"T_a4a21_row2_col1\" class=\"data row2 col1\" >-5.99%</td>\n",
       "      <td id=\"T_a4a21_row2_col2\" class=\"data row2 col2\" >100.00%</td>\n",
       "      <td id=\"T_a4a21_row2_col3\" class=\"data row2 col3\" >-2.38%</td>\n",
       "      <td id=\"T_a4a21_row2_col4\" class=\"data row2 col4\" >-12.09%</td>\n",
       "      <td id=\"T_a4a21_row2_col5\" class=\"data row2 col5\" >-11.51%</td>\n",
       "      <td id=\"T_a4a21_row2_col6\" class=\"data row2 col6\" >-3.81%</td>\n",
       "      <td id=\"T_a4a21_row2_col7\" class=\"data row2 col7\" >-13.41%</td>\n",
       "      <td id=\"T_a4a21_row2_col8\" class=\"data row2 col8\" >4.69%</td>\n",
       "      <td id=\"T_a4a21_row2_col9\" class=\"data row2 col9\" >-4.69%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a4a21_level0_row3\" class=\"row_heading level0 row3\" >ACWI</th>\n",
       "      <td id=\"T_a4a21_row3_col0\" class=\"data row3 col0\" >-29.47%</td>\n",
       "      <td id=\"T_a4a21_row3_col1\" class=\"data row3 col1\" >-13.44%</td>\n",
       "      <td id=\"T_a4a21_row3_col2\" class=\"data row3 col2\" >-2.38%</td>\n",
       "      <td id=\"T_a4a21_row3_col3\" class=\"data row3 col3\" >100.00%</td>\n",
       "      <td id=\"T_a4a21_row3_col4\" class=\"data row3 col4\" >35.53%</td>\n",
       "      <td id=\"T_a4a21_row3_col5\" class=\"data row3 col5\" >69.42%</td>\n",
       "      <td id=\"T_a4a21_row3_col6\" class=\"data row3 col6\" >17.75%</td>\n",
       "      <td id=\"T_a4a21_row3_col7\" class=\"data row3 col7\" >57.15%</td>\n",
       "      <td id=\"T_a4a21_row3_col8\" class=\"data row3 col8\" >79.19%</td>\n",
       "      <td id=\"T_a4a21_row3_col9\" class=\"data row3 col9\" >84.21%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a4a21_level0_row4\" class=\"row_heading level0 row4\" >GovBondG7</th>\n",
       "      <td id=\"T_a4a21_row4_col0\" class=\"data row4 col0\" >-41.78%</td>\n",
       "      <td id=\"T_a4a21_row4_col1\" class=\"data row4 col1\" >-16.44%</td>\n",
       "      <td id=\"T_a4a21_row4_col2\" class=\"data row4 col2\" >-12.09%</td>\n",
       "      <td id=\"T_a4a21_row4_col3\" class=\"data row4 col3\" >35.53%</td>\n",
       "      <td id=\"T_a4a21_row4_col4\" class=\"data row4 col4\" >100.00%</td>\n",
       "      <td id=\"T_a4a21_row4_col5\" class=\"data row4 col5\" >88.76%</td>\n",
       "      <td id=\"T_a4a21_row4_col6\" class=\"data row4 col6\" >52.15%</td>\n",
       "      <td id=\"T_a4a21_row4_col7\" class=\"data row4 col7\" >59.24%</td>\n",
       "      <td id=\"T_a4a21_row4_col8\" class=\"data row4 col8\" >-3.26%</td>\n",
       "      <td id=\"T_a4a21_row4_col9\" class=\"data row4 col9\" >64.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a4a21_level0_row5\" class=\"row_heading level0 row5\" >GlobalCorporate</th>\n",
       "      <td id=\"T_a4a21_row5_col0\" class=\"data row5 col0\" >-34.82%</td>\n",
       "      <td id=\"T_a4a21_row5_col1\" class=\"data row5 col1\" >-13.38%</td>\n",
       "      <td id=\"T_a4a21_row5_col2\" class=\"data row5 col2\" >-11.51%</td>\n",
       "      <td id=\"T_a4a21_row5_col3\" class=\"data row5 col3\" >69.42%</td>\n",
       "      <td id=\"T_a4a21_row5_col4\" class=\"data row5 col4\" >88.76%</td>\n",
       "      <td id=\"T_a4a21_row5_col5\" class=\"data row5 col5\" >100.00%</td>\n",
       "      <td id=\"T_a4a21_row5_col6\" class=\"data row5 col6\" >47.66%</td>\n",
       "      <td id=\"T_a4a21_row5_col7\" class=\"data row5 col7\" >66.16%</td>\n",
       "      <td id=\"T_a4a21_row5_col8\" class=\"data row5 col8\" >40.27%</td>\n",
       "      <td id=\"T_a4a21_row5_col9\" class=\"data row5 col9\" >91.53%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a4a21_level0_row6\" class=\"row_heading level0 row6\" >Gold</th>\n",
       "      <td id=\"T_a4a21_row6_col0\" class=\"data row6 col0\" >-27.75%</td>\n",
       "      <td id=\"T_a4a21_row6_col1\" class=\"data row6 col1\" >-8.90%</td>\n",
       "      <td id=\"T_a4a21_row6_col2\" class=\"data row6 col2\" >-3.81%</td>\n",
       "      <td id=\"T_a4a21_row6_col3\" class=\"data row6 col3\" >17.75%</td>\n",
       "      <td id=\"T_a4a21_row6_col4\" class=\"data row6 col4\" >52.15%</td>\n",
       "      <td id=\"T_a4a21_row6_col5\" class=\"data row6 col5\" >47.66%</td>\n",
       "      <td id=\"T_a4a21_row6_col6\" class=\"data row6 col6\" >100.00%</td>\n",
       "      <td id=\"T_a4a21_row6_col7\" class=\"data row6 col7\" >39.48%</td>\n",
       "      <td id=\"T_a4a21_row6_col8\" class=\"data row6 col8\" >-9.23%</td>\n",
       "      <td id=\"T_a4a21_row6_col9\" class=\"data row6 col9\" >32.20%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a4a21_level0_row7\" class=\"row_heading level0 row7\" >GABHDG</th>\n",
       "      <td id=\"T_a4a21_row7_col0\" class=\"data row7 col0\" >-37.71%</td>\n",
       "      <td id=\"T_a4a21_row7_col1\" class=\"data row7 col1\" >8.52%</td>\n",
       "      <td id=\"T_a4a21_row7_col2\" class=\"data row7 col2\" >-13.41%</td>\n",
       "      <td id=\"T_a4a21_row7_col3\" class=\"data row7 col3\" >57.15%</td>\n",
       "      <td id=\"T_a4a21_row7_col4\" class=\"data row7 col4\" >59.24%</td>\n",
       "      <td id=\"T_a4a21_row7_col5\" class=\"data row7 col5\" >66.16%</td>\n",
       "      <td id=\"T_a4a21_row7_col6\" class=\"data row7 col6\" >39.48%</td>\n",
       "      <td id=\"T_a4a21_row7_col7\" class=\"data row7 col7\" >100.00%</td>\n",
       "      <td id=\"T_a4a21_row7_col8\" class=\"data row7 col8\" >44.77%</td>\n",
       "      <td id=\"T_a4a21_row7_col9\" class=\"data row7 col9\" >61.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a4a21_level0_row8\" class=\"row_heading level0 row8\" >EURO_STOXX_Leverage</th>\n",
       "      <td id=\"T_a4a21_row8_col0\" class=\"data row8 col0\" >-14.29%</td>\n",
       "      <td id=\"T_a4a21_row8_col1\" class=\"data row8 col1\" >-10.80%</td>\n",
       "      <td id=\"T_a4a21_row8_col2\" class=\"data row8 col2\" >4.69%</td>\n",
       "      <td id=\"T_a4a21_row8_col3\" class=\"data row8 col3\" >79.19%</td>\n",
       "      <td id=\"T_a4a21_row8_col4\" class=\"data row8 col4\" >-3.26%</td>\n",
       "      <td id=\"T_a4a21_row8_col5\" class=\"data row8 col5\" >40.27%</td>\n",
       "      <td id=\"T_a4a21_row8_col6\" class=\"data row8 col6\" >-9.23%</td>\n",
       "      <td id=\"T_a4a21_row8_col7\" class=\"data row8 col7\" >44.77%</td>\n",
       "      <td id=\"T_a4a21_row8_col8\" class=\"data row8 col8\" >100.00%</td>\n",
       "      <td id=\"T_a4a21_row8_col9\" class=\"data row8 col9\" >59.90%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a4a21_level0_row9\" class=\"row_heading level0 row9\" >Fallen_Angels</th>\n",
       "      <td id=\"T_a4a21_row9_col0\" class=\"data row9 col0\" >-29.83%</td>\n",
       "      <td id=\"T_a4a21_row9_col1\" class=\"data row9 col1\" >-0.25%</td>\n",
       "      <td id=\"T_a4a21_row9_col2\" class=\"data row9 col2\" >-4.69%</td>\n",
       "      <td id=\"T_a4a21_row9_col3\" class=\"data row9 col3\" >84.21%</td>\n",
       "      <td id=\"T_a4a21_row9_col4\" class=\"data row9 col4\" >64.17%</td>\n",
       "      <td id=\"T_a4a21_row9_col5\" class=\"data row9 col5\" >91.53%</td>\n",
       "      <td id=\"T_a4a21_row9_col6\" class=\"data row9 col6\" >32.20%</td>\n",
       "      <td id=\"T_a4a21_row9_col7\" class=\"data row9 col7\" >61.19%</td>\n",
       "      <td id=\"T_a4a21_row9_col8\" class=\"data row9 col8\" >59.90%</td>\n",
       "      <td id=\"T_a4a21_row9_col9\" class=\"data row9 col9\" >100.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f4fcc491c0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr180 = assets.iloc[-150:].corr(method='pearson')\n",
    "#corr120 = assets.iloc[-120:].cov()\n",
    "corr180.style.format(\"{:.2%}\").background_gradient(cmap='YlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "moving_corr = assets[['ACWI']].rolling(120).corr(assets['TBSP'])\n",
    "moving_corr60 = assets[['ACWI']].rolling(150).corr(assets['TBSP'])\n",
    "fig, ax = plt.subplots(figsize=(16,8))\n",
    "sns.lineplot(data=moving_corr, ax=ax, palette='mako')\n",
    "sns.lineplot(data=moving_corr60, ax=ax, palette='Wistia_r')"
   ]
  },
  {
   "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>Mwig40tr</th>\n",
       "      <th>TBSP</th>\n",
       "      <th>ACWI</th>\n",
       "      <th>Gold</th>\n",
       "      <th>EURO_STOXX_Leverage</th>\n",
       "      <th>EDO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-02-01</th>\n",
       "      <td>10.14%</td>\n",
       "      <td>-0.13%</td>\n",
       "      <td>2.40%</td>\n",
       "      <td>3.07%</td>\n",
       "      <td>3.85%</td>\n",
       "      <td>0.10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
       "      <td>4.24%</td>\n",
       "      <td>0.39%</td>\n",
       "      <td>0.03%</td>\n",
       "      <td>-2.09%</td>\n",
       "      <td>11.27%</td>\n",
       "      <td>0.10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01</th>\n",
       "      <td>-1.99%</td>\n",
       "      <td>1.33%</td>\n",
       "      <td>-2.35%</td>\n",
       "      <td>-2.23%</td>\n",
       "      <td>2.14%</td>\n",
       "      <td>0.10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-01</th>\n",
       "      <td>2.48%</td>\n",
       "      <td>0.32%</td>\n",
       "      <td>-0.48%</td>\n",
       "      <td>-2.73%</td>\n",
       "      <td>2.61%</td>\n",
       "      <td>0.10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01</th>\n",
       "      <td>-0.44%</td>\n",
       "      <td>0.96%</td>\n",
       "      <td>-2.20%</td>\n",
       "      <td>-4.52%</td>\n",
       "      <td>-6.82%</td>\n",
       "      <td>0.10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-01</th>\n",
       "      <td>2.74%</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>1.26%</td>\n",
       "      <td>0.48%</td>\n",
       "      <td>1.74%</td>\n",
       "      <td>0.12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01</th>\n",
       "      <td>1.03%</td>\n",
       "      <td>0.10%</td>\n",
       "      <td>0.14%</td>\n",
       "      <td>3.19%</td>\n",
       "      <td>-0.98%</td>\n",
       "      <td>0.12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-01</th>\n",
       "      <td>-0.05%</td>\n",
       "      <td>0.52%</td>\n",
       "      <td>1.97%</td>\n",
       "      <td>-2.18%</td>\n",
       "      <td>10.43%</td>\n",
       "      <td>0.12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-01</th>\n",
       "      <td>1.64%</td>\n",
       "      <td>-0.02%</td>\n",
       "      <td>5.16%</td>\n",
       "      <td>1.96%</td>\n",
       "      <td>6.21%</td>\n",
       "      <td>0.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01</th>\n",
       "      <td>-2.26%</td>\n",
       "      <td>0.21%</td>\n",
       "      <td>-1.56%</td>\n",
       "      <td>-2.70%</td>\n",
       "      <td>-7.08%</td>\n",
       "      <td>0.16%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01</th>\n",
       "      <td>-2.57%</td>\n",
       "      <td>0.48%</td>\n",
       "      <td>-0.44%</td>\n",
       "      <td>-0.80%</td>\n",
       "      <td>-4.30%</td>\n",
       "      <td>0.16%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>2.05%</td>\n",
       "      <td>0.36%</td>\n",
       "      <td>1.10%</td>\n",
       "      <td>-0.74%</td>\n",
       "      <td>5.61%</td>\n",
       "      <td>0.22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01</th>\n",
       "      <td>3.64%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>-2.82%</td>\n",
       "      <td>-0.65%</td>\n",
       "      <td>-9.70%</td>\n",
       "      <td>0.22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01</th>\n",
       "      <td>-6.02%</td>\n",
       "      <td>0.66%</td>\n",
       "      <td>-2.38%</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>-3.88%</td>\n",
       "      <td>0.22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01</th>\n",
       "      <td>-3.52%</td>\n",
       "      <td>0.96%</td>\n",
       "      <td>3.96%</td>\n",
       "      <td>2.09%</td>\n",
       "      <td>13.21%</td>\n",
       "      <td>0.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01</th>\n",
       "      <td>1.11%</td>\n",
       "      <td>0.30%</td>\n",
       "      <td>4.08%</td>\n",
       "      <td>3.24%</td>\n",
       "      <td>-3.67%</td>\n",
       "      <td>0.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-01</th>\n",
       "      <td>-0.85%</td>\n",
       "      <td>-0.20%</td>\n",
       "      <td>1.68%</td>\n",
       "      <td>-2.11%</td>\n",
       "      <td>1.36%</td>\n",
       "      <td>0.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>-5.38%</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>3.73%</td>\n",
       "      <td>-1.72%</td>\n",
       "      <td>9.44%</td>\n",
       "      <td>0.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>3.72%</td>\n",
       "      <td>0.35%</td>\n",
       "      <td>-0.69%</td>\n",
       "      <td>-3.00%</td>\n",
       "      <td>-9.42%</td>\n",
       "      <td>0.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>-2.57%</td>\n",
       "      <td>0.20%</td>\n",
       "      <td>1.76%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>1.17%</td>\n",
       "      <td>0.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>-1.47%</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>-5.86%</td>\n",
       "      <td>4.11%</td>\n",
       "      <td>-12.00%</td>\n",
       "      <td>0.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>-8.39%</td>\n",
       "      <td>0.34%</td>\n",
       "      <td>2.61%</td>\n",
       "      <td>1.30%</td>\n",
       "      <td>-0.03%</td>\n",
       "      <td>0.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>3.28%</td>\n",
       "      <td>0.71%</td>\n",
       "      <td>-8.81%</td>\n",
       "      <td>3.22%</td>\n",
       "      <td>-11.50%</td>\n",
       "      <td>0.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>-0.88%</td>\n",
       "      <td>0.97%</td>\n",
       "      <td>7.51%</td>\n",
       "      <td>2.85%</td>\n",
       "      <td>11.22%</td>\n",
       "      <td>0.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-01</th>\n",
       "      <td>4.28%</td>\n",
       "      <td>0.51%</td>\n",
       "      <td>2.69%</td>\n",
       "      <td>-0.34%</td>\n",
       "      <td>8.20%</td>\n",
       "      <td>0.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>1.42%</td>\n",
       "      <td>-0.50%</td>\n",
       "      <td>3.97%</td>\n",
       "      <td>0.76%</td>\n",
       "      <td>4.67%</td>\n",
       "      <td>0.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>0.36%</td>\n",
       "      <td>0.58%</td>\n",
       "      <td>3.61%</td>\n",
       "      <td>-0.84%</td>\n",
       "      <td>11.23%</td>\n",
       "      <td>0.23%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01</th>\n",
       "      <td>-0.09%</td>\n",
       "      <td>-0.37%</td>\n",
       "      <td>-5.70%</td>\n",
       "      <td>1.19%</td>\n",
       "      <td>-10.56%</td>\n",
       "      <td>0.23%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-01</th>\n",
       "      <td>-3.77%</td>\n",
       "      <td>1.17%</td>\n",
       "      <td>4.35%</td>\n",
       "      <td>6.47%</td>\n",
       "      <td>12.25%</td>\n",
       "      <td>0.23%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>3.75%</td>\n",
       "      <td>0.74%</td>\n",
       "      <td>1.52%</td>\n",
       "      <td>2.53%</td>\n",
       "      <td>-1.02%</td>\n",
       "      <td>0.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01</th>\n",
       "      <td>-2.48%</td>\n",
       "      <td>0.88%</td>\n",
       "      <td>-0.27%</td>\n",
       "      <td>9.32%</td>\n",
       "      <td>-1.44%</td>\n",
       "      <td>0.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-01</th>\n",
       "      <td>-4.01%</td>\n",
       "      <td>1.03%</td>\n",
       "      <td>5.57%</td>\n",
       "      <td>0.43%</td>\n",
       "      <td>10.85%</td>\n",
       "      <td>0.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01</th>\n",
       "      <td>-1.77%</td>\n",
       "      <td>-0.26%</td>\n",
       "      <td>0.24%</td>\n",
       "      <td>-0.77%</td>\n",
       "      <td>1.93%</td>\n",
       "      <td>0.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>0.34%</td>\n",
       "      <td>0.35%</td>\n",
       "      <td>1.51%</td>\n",
       "      <td>-4.28%</td>\n",
       "      <td>3.07%</td>\n",
       "      <td>0.27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>2.12%</td>\n",
       "      <td>0.01%</td>\n",
       "      <td>2.48%</td>\n",
       "      <td>3.21%</td>\n",
       "      <td>3.60%</td>\n",
       "      <td>0.27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>3.24%</td>\n",
       "      <td>-0.25%</td>\n",
       "      <td>-0.86%</td>\n",
       "      <td>4.25%</td>\n",
       "      <td>-6.66%</td>\n",
       "      <td>0.27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>2.57%</td>\n",
       "      <td>0.07%</td>\n",
       "      <td>-6.48%</td>\n",
       "      <td>3.34%</td>\n",
       "      <td>-15.78%</td>\n",
       "      <td>0.27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-01</th>\n",
       "      <td>-10.64%</td>\n",
       "      <td>1.29%</td>\n",
       "      <td>-12.62%</td>\n",
       "      <td>0.89%</td>\n",
       "      <td>-32.83%</td>\n",
       "      <td>0.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>-20.44%</td>\n",
       "      <td>1.27%</td>\n",
       "      <td>17.47%</td>\n",
       "      <td>12.24%</td>\n",
       "      <td>15.67%</td>\n",
       "      <td>0.31%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-01</th>\n",
       "      <td>15.09%</td>\n",
       "      <td>1.16%</td>\n",
       "      <td>1.68%</td>\n",
       "      <td>-1.13%</td>\n",
       "      <td>9.00%</td>\n",
       "      <td>0.31%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>2.58%</td>\n",
       "      <td>1.50%</td>\n",
       "      <td>0.49%</td>\n",
       "      <td>-0.44%</td>\n",
       "      <td>10.05%</td>\n",
       "      <td>0.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>3.75%</td>\n",
       "      <td>-0.31%</td>\n",
       "      <td>-0.49%</td>\n",
       "      <td>4.99%</td>\n",
       "      <td>-3.39%</td>\n",
       "      <td>0.35%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-01</th>\n",
       "      <td>2.90%</td>\n",
       "      <td>0.48%</td>\n",
       "      <td>4.52%</td>\n",
       "      <td>-1.92%</td>\n",
       "      <td>5.46%</td>\n",
       "      <td>0.35%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-01</th>\n",
       "      <td>4.03%</td>\n",
       "      <td>-0.38%</td>\n",
       "      <td>-1.61%</td>\n",
       "      <td>-2.03%</td>\n",
       "      <td>-5.23%</td>\n",
       "      <td>0.34%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>-2.99%</td>\n",
       "      <td>0.49%</td>\n",
       "      <td>0.62%</td>\n",
       "      <td>2.82%</td>\n",
       "      <td>-11.77%</td>\n",
       "      <td>0.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>-10.25%</td>\n",
       "      <td>0.71%</td>\n",
       "      <td>11.58%</td>\n",
       "      <td>-6.99%</td>\n",
       "      <td>41.00%</td>\n",
       "      <td>0.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-01</th>\n",
       "      <td>16.64%</td>\n",
       "      <td>-0.05%</td>\n",
       "      <td>-0.62%</td>\n",
       "      <td>1.86%</td>\n",
       "      <td>0.62%</td>\n",
       "      <td>0.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>6.67%</td>\n",
       "      <td>0.04%</td>\n",
       "      <td>2.55%</td>\n",
       "      <td>1.51%</td>\n",
       "      <td>-2.00%</td>\n",
       "      <td>0.38%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Mwig40tr   TBSP    ACWI   Gold  EURO_STOXX_Leverage   EDO\n",
       "Date                                                                 \n",
       "2017-02-01    10.14% -0.13%   2.40%  3.07%                3.85% 0.10%\n",
       "2017-03-01     4.24%  0.39%   0.03% -2.09%               11.27% 0.10%\n",
       "2017-04-01    -1.99%  1.33%  -2.35% -2.23%                2.14% 0.10%\n",
       "2017-05-01     2.48%  0.32%  -0.48% -2.73%                2.61% 0.10%\n",
       "2017-06-01    -0.44%  0.96%  -2.20% -4.52%               -6.82% 0.10%\n",
       "2017-07-01     2.74%  0.15%   1.26%  0.48%                1.74% 0.12%\n",
       "2017-08-01     1.03%  0.10%   0.14%  3.19%               -0.98% 0.12%\n",
       "2017-09-01    -0.05%  0.52%   1.97% -2.18%               10.43% 0.12%\n",
       "2017-10-01     1.64% -0.02%   5.16%  1.96%                6.21% 0.17%\n",
       "2017-11-01    -2.26%  0.21%  -1.56% -2.70%               -7.08% 0.16%\n",
       "2017-12-01    -2.57%  0.48%  -0.44% -0.80%               -4.30% 0.16%\n",
       "2018-01-01     2.05%  0.36%   1.10% -0.74%                5.61% 0.22%\n",
       "2018-02-01     3.64% -0.00%  -2.82% -0.65%               -9.70% 0.22%\n",
       "2018-03-01    -6.02%  0.66%  -2.38%  0.15%               -3.88% 0.22%\n",
       "2018-04-01    -3.52%  0.96%   3.96%  2.09%               13.21% 0.26%\n",
       "2018-05-01     1.11%  0.30%   4.08%  3.24%               -3.67% 0.26%\n",
       "2018-06-01    -0.85% -0.20%   1.68% -2.11%                1.36% 0.26%\n",
       "2018-07-01    -5.38%  0.15%   3.73% -1.72%                9.44% 0.26%\n",
       "2018-08-01     3.72%  0.35%  -0.69% -3.00%               -9.42% 0.26%\n",
       "2018-09-01    -2.57%  0.20%   1.76% -0.00%                1.17% 0.26%\n",
       "2018-10-01    -1.47%  0.15%  -5.86%  4.11%              -12.00% 0.26%\n",
       "2018-11-01    -8.39%  0.34%   2.61%  1.30%               -0.03% 0.26%\n",
       "2018-12-01     3.28%  0.71%  -8.81%  3.22%              -11.50% 0.26%\n",
       "2019-01-01    -0.88%  0.97%   7.51%  2.85%               11.22% 0.25%\n",
       "2019-02-01     4.28%  0.51%   2.69% -0.34%                8.20% 0.25%\n",
       "2019-03-01     1.42% -0.50%   3.97%  0.76%                4.67% 0.25%\n",
       "2019-04-01     0.36%  0.58%   3.61% -0.84%               11.23% 0.23%\n",
       "2019-05-01    -0.09% -0.37%  -5.70%  1.19%              -10.56% 0.23%\n",
       "2019-06-01    -3.77%  1.17%   4.35%  6.47%               12.25% 0.23%\n",
       "2019-07-01     3.75%  0.74%   1.52%  2.53%               -1.02% 0.24%\n",
       "2019-08-01    -2.48%  0.88%  -0.27%  9.32%               -1.44% 0.24%\n",
       "2019-09-01    -4.01%  1.03%   5.57%  0.43%               10.85% 0.25%\n",
       "2019-10-01    -1.77% -0.26%   0.24% -0.77%                1.93% 0.26%\n",
       "2019-11-01     0.34%  0.35%   1.51% -4.28%                3.07% 0.27%\n",
       "2019-12-01     2.12%  0.01%   2.48%  3.21%                3.60% 0.27%\n",
       "2020-01-01     3.24% -0.25%  -0.86%  4.25%               -6.66% 0.27%\n",
       "2020-02-01     2.57%  0.07%  -6.48%  3.34%              -15.78% 0.27%\n",
       "2020-03-01   -10.64%  1.29% -12.62%  0.89%              -32.83% 0.26%\n",
       "2020-04-01   -20.44%  1.27%  17.47% 12.24%               15.67% 0.31%\n",
       "2020-05-01    15.09%  1.16%   1.68% -1.13%                9.00% 0.31%\n",
       "2020-06-01     2.58%  1.50%   0.49% -0.44%               10.05% 0.30%\n",
       "2020-07-01     3.75% -0.31%  -0.49%  4.99%               -3.39% 0.35%\n",
       "2020-08-01     2.90%  0.48%   4.52% -1.92%                5.46% 0.35%\n",
       "2020-09-01     4.03% -0.38%  -1.61% -2.03%               -5.23% 0.34%\n",
       "2020-10-01    -2.99%  0.49%   0.62%  2.82%              -11.77% 0.37%\n",
       "2020-11-01   -10.25%  0.71%  11.58% -6.99%               41.00% 0.37%\n",
       "2020-12-01    16.64% -0.05%  -0.62%  1.86%                0.62% 0.37%\n",
       "2021-01-01     6.67%  0.04%   2.55%  1.51%               -2.00% 0.38%"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_assets = assets[['Mwig40tr', 'TBSP', 'ACWI', 'Gold', 'EURO_STOXX_Leverage', 'EDO']]\n",
    "means = pd.DataFrame(portfolio_assets.mean()*12)\n",
    "cov = pd.DataFrame(portfolio_assets.cov())\n",
    "portfolio_assets['01-01-2003':'01-01-2004']\n",
    "portfolio_assets['01-12-2017':'01-12-2021']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'rp' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\matsz\\programowanie\\Optymalizacja portfela\\analisys_efficient_frontier3.ipynb Komórka 24\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/matsz/programowanie/Optymalizacja%20portfela/analisys_efficient_frontier3.ipynb#X32sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m#assets ['Mwig40tr', 'TBSP', 'ACWI', 'GovBondG7', 'GlobalCorporate', 'Gold', 'GABHDG', 'EURO_STOXX_Leverage', 'Fallen_Angels', 'EDO']\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/matsz/programowanie/Optymalizacja%20portfela/analisys_efficient_frontier3.ipynb#X32sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39m#['Mwig40tr', 'TBSP', 'ACWI', 'Gold', 'GovBondG7', 'EDO]\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/matsz/programowanie/Optymalizacja%20portfela/analisys_efficient_frontier3.ipynb#X32sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m porfolio_assets \u001b[39m=\u001b[39m assets[[\u001b[39m'\u001b[39m\u001b[39mMwig40tr\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mTBSP\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mACWI\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mGold\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mGovBondG7\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mEDO\u001b[39m\u001b[39m'\u001b[39m]]\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/matsz/programowanie/Optymalizacja%20portfela/analisys_efficient_frontier3.ipynb#X32sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m port \u001b[39m=\u001b[39m rp\u001b[39m.\u001b[39mPortfolio(porfolio_assets[\u001b[39m-\u001b[39m\u001b[39m123\u001b[39m:])\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/matsz/programowanie/Optymalizacja%20portfela/analisys_efficient_frontier3.ipynb#X32sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m means \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mDataFrame(portfolio_assets[\u001b[39m'\u001b[39m\u001b[39m01-01-2004\u001b[39m\u001b[39m'\u001b[39m:\u001b[39m'\u001b[39m\u001b[39m01-12-2022\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mmean()\u001b[39m*\u001b[39m\u001b[39m12\u001b[39m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/matsz/programowanie/Optymalizacja%20portfela/analisys_efficient_frontier3.ipynb#X32sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m cov \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mDataFrame(portfolio_assets[\u001b[39m'\u001b[39m\u001b[39m01-01-2004\u001b[39m\u001b[39m'\u001b[39m:\u001b[39m'\u001b[39m\u001b[39m01-12-2022\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mcov())\n",
      "\u001b[1;31mNameError\u001b[0m: name 'rp' is not defined"
     ]
    }
   ],
   "source": [
    "#assets ['Mwig40tr', 'TBSP', 'ACWI', 'GovBondG7', 'GlobalCorporate', 'Gold', 'GABHDG', 'EURO_STOXX_Leverage', 'Fallen_Angels', 'EDO']\n",
    "#['Mwig40tr', 'TBSP', 'ACWI', 'Gold', 'GovBondG7', 'EDO]\n",
    "\n",
    "porfolio_assets = assets[['Mwig40tr', 'TBSP', 'ACWI', 'Gold', 'GovBondG7', 'EDO']]\n",
    "port = rp.Portfolio(porfolio_assets[-123:])\n",
    "\n",
    "\n",
    "\n",
    "means = pd.DataFrame(portfolio_assets['01-01-2004':'01-12-2022'].mean()*12)\n",
    "cov = pd.DataFrame(portfolio_assets['01-01-2004':'01-12-2022'].cov())\n",
    "\n",
    "method_mu='hist' # Method to estimate expected returns based on historical data.\n",
    "method_cov='hist' # Method to estimate covariance matrix based on historical data.\n",
    "\n",
    "port.assets_stats(method_mu=method_mu, method_cov=method_cov, d=0.94)\n",
    "\n",
    "points = 15 # Number of points of the frontier\n",
    "model='Classic' # Could be Classic (historical), BL (Black Litterman) or FM (Factor Model)\n",
    "rm =  'MV' #'MV' vs 'MDD' # Risk measure used, this time will be variance\n",
    "hist = True # Use historical scenarios for risk measures that depend on scenarios\n",
    "rf = 0.00 # Risk free rate\n",
    "\n",
    "frontier = port.efficient_frontier(model=model, rm=rm, points=points, rf=rf, hist=hist)\n",
    "\n",
    "display(frontier.T.head(50))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = rp.plot_frontier_area(w_frontier=frontier, cmap=\"tab20\", height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0838378 , 0.03067022, 0.12365509, 0.06559336, 0.02720226,\n",
       "       0.03527836])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "porfolio_assets[-150:].cov().to_numpy()\n",
    "frontier.values.T\n",
    "porfolio_assets[-150:].mean().values.T*12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.03519068, 0.00528686],\n",
       "       [0.0469084 , 0.01548693],\n",
       "       [0.05505872, 0.02569391],\n",
       "       [0.06301184, 0.03588331],\n",
       "       [0.07090147, 0.04606665],\n",
       "       [0.07876275, 0.05624732],\n",
       "       [0.08660875, 0.06642652],\n",
       "       [0.09444595, 0.07660495],\n",
       "       [0.10227776, 0.08678306],\n",
       "       [0.10792318, 0.09652981],\n",
       "       [0.11345244, 0.10751325],\n",
       "       [0.11660844, 0.11831416],\n",
       "       [0.1191954 , 0.12902325],\n",
       "       [0.12150745, 0.13967592],\n",
       "       [0.12365509, 0.15028918]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measures = portfolioMeasures(frontier.values.T,porfolio_assets[-150:].mean().values.T, porfolio_assets[-150:].cov().to_numpy())\n",
    "measures[:,0] = measures[:,0]*12\n",
    "measures[:,1] = measures[:,1]*np.sqrt(12)\n",
    "\n",
    "measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Mwig40tr</th>\n",
       "      <th>TBSP</th>\n",
       "      <th>ACWI</th>\n",
       "      <th>Gold</th>\n",
       "      <th>GovBondG7</th>\n",
       "      <th>EDO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mwig40tr</th>\n",
       "      <td>0.26%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>-0.07%</td>\n",
       "      <td>-0.07%</td>\n",
       "      <td>-0.06%</td>\n",
       "      <td>-0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TBSP</th>\n",
       "      <td>0.00%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACWI</th>\n",
       "      <td>-0.07%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>0.19%</td>\n",
       "      <td>0.04%</td>\n",
       "      <td>0.04%</td>\n",
       "      <td>-0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gold</th>\n",
       "      <td>-0.07%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>0.04%</td>\n",
       "      <td>0.22%</td>\n",
       "      <td>0.07%</td>\n",
       "      <td>-0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GovBondG7</th>\n",
       "      <td>-0.06%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>0.04%</td>\n",
       "      <td>0.07%</td>\n",
       "      <td>0.08%</td>\n",
       "      <td>-0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDO</th>\n",
       "      <td>-0.00%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Mwig40tr   TBSP   ACWI   Gold  GovBondG7    EDO\n",
       "Mwig40tr      0.26%  0.00% -0.07% -0.07%     -0.06% -0.00%\n",
       "TBSP          0.00%  0.02% -0.01% -0.01%     -0.01% -0.00%\n",
       "ACWI         -0.07% -0.01%  0.19%  0.04%      0.04% -0.00%\n",
       "Gold         -0.07% -0.01%  0.04%  0.22%      0.07% -0.00%\n",
       "GovBondG7    -0.06% -0.01%  0.04%  0.07%      0.08% -0.00%\n",
       "EDO          -0.00% -0.00% -0.00% -0.00%     -0.00%  0.00%"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.optimize import minimize, NonlinearConstraint\n",
    "\n",
    "def pstd(weights, cov_matrix):\n",
    "    #variance = np.transpose(weights)@cov_matrix@weights\n",
    "    variance = np.dot(weights.T, np.dot(cov_matrix, weights))\n",
    "    return np.sqrt(variance)*np.sqrt(12)\n",
    "\n",
    "def pmean(weightsDf, means):  \n",
    "    weights = weightsDf\n",
    "    return weights.dot(means) *12\n",
    "\n",
    "\n",
    "\n",
    "def optimize(expectedMean, means: np.array, cov_table: np.array):\n",
    "\n",
    "    constraint1 = NonlinearConstraint(lambda x : x.sum(),1,1)\n",
    "    constraint2 = NonlinearConstraint(lambda x: x.dot(means)*12, expectedMean, expectedMean)\n",
    "    constraint3 = NonlinearConstraint(lambda x: np.all(np.any([x >=0.05, x == 0], axis=0)).astype(int),True,True)\n",
    "    assets_constraints = [(0.0,1),(0.0,1), (0,0.09)]\n",
    "    obj = lambda x: x.T@cov_table@x\n",
    "    start = [1/len(means) for n in range(len(means))]\n",
    "    result = minimize(obj,start,constraints=[constraint1, constraint2],bounds=assets_constraints)\n",
    "    #result = np.append(result.x, [expectedMean], [result.fun])    \n",
    "\n",
    "    return np.array(result.x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TBSP    3.68%\n",
      "ACWI   10.39%\n",
      "dtype: float64\n",
      "       TBSP   ACWI\n",
      "TBSP  0.02% -0.00%\n",
      "ACWI -0.00%  0.21%\n",
      "TBSP    4.39%\n",
      "ACWI   15.81%\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#assets ['Mwig40tr', 'TBSP', 'ACWI', 'GovBondG7', 'GlobalCorporate', 'Gold', 'GABHDG', 'EURO_STOXX_Leverage', 'Fallen_Angels', 'EDO']\n",
    "#['Mwig40tr', 'TBSP', 'ACWI', 'Gold', 'GovBondG7', 'EDO', 'GlobalCorporate', 'EURO_STOXX_Leverage']\n",
    "assets_names = ['TBSP', 'ACWI']\n",
    "\n",
    "portfolio = assets.iloc[-180:][assets_names]\n",
    "means = portfolio.mean()\n",
    "cov_table = portfolio.cov()\n",
    "stds = portfolio.std()\n",
    "\n",
    "\n",
    "print(means*12)\n",
    "print(cov_table)\n",
    "print(stds*np.sqrt(12))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00305465, 0.00871509])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret = np.random.multivariate_normal(means, cov_table,36)\n",
    "np.round(np.cov(ret),3)\n",
    "return_table = np.zeros((10000,36,2))\n",
    "for n in range(10000):\n",
    "    return_table[n] = np.random.multivariate_normal(means, cov_table,36)\n",
    "\n",
    "means_ret = return_table.sum(0)/10000\n",
    "means_ret.mean(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (2,) (3,) (3,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\matsz\\programowanie\\Optymalizacja portfela\\analisys_efficient_frontier3.ipynb Komórka 31\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/matsz/programowanie/Optymalizacja%20portfela/analisys_efficient_frontier3.ipynb#X42sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m weights \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mempty((\u001b[39m0\u001b[39m,\u001b[39mlen\u001b[39m(assets_names)\u001b[39m+\u001b[39m\u001b[39m2\u001b[39m))\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/matsz/programowanie/Optymalizacja%20portfela/analisys_efficient_frontier3.ipynb#X42sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m \u001b[39mfor\u001b[39;00m mean \u001b[39min\u001b[39;00m portfolio_means:\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/matsz/programowanie/Optymalizacja%20portfela/analisys_efficient_frontier3.ipynb#X42sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m     weight \u001b[39m=\u001b[39m optimize(mean,means,cov_table)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/matsz/programowanie/Optymalizacja%20portfela/analisys_efficient_frontier3.ipynb#X42sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m     std \u001b[39m=\u001b[39m pstd(weight,cov_table)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/matsz/programowanie/Optymalizacja%20portfela/analisys_efficient_frontier3.ipynb#X42sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m     weight \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mappend(weight, [mean, std])\n",
      "\u001b[1;32mc:\\Users\\matsz\\programowanie\\Optymalizacja portfela\\analisys_efficient_frontier3.ipynb Komórka 31\u001b[0m in \u001b[0;36moptimize\u001b[1;34m(expectedMean, means, cov_table)\u001b[0m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/matsz/programowanie/Optymalizacja%20portfela/analisys_efficient_frontier3.ipynb#X42sZmlsZQ%3D%3D?line=20'>21</a>\u001b[0m obj \u001b[39m=\u001b[39m \u001b[39mlambda\u001b[39;00m x: x\u001b[39m.\u001b[39mT\u001b[39m@cov_table\u001b[39m\u001b[39m@x\u001b[39m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/matsz/programowanie/Optymalizacja%20portfela/analisys_efficient_frontier3.ipynb#X42sZmlsZQ%3D%3D?line=21'>22</a>\u001b[0m start \u001b[39m=\u001b[39m [\u001b[39m1\u001b[39m\u001b[39m/\u001b[39m\u001b[39mlen\u001b[39m(means) \u001b[39mfor\u001b[39;00m n \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(means))]\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/matsz/programowanie/Optymalizacja%20portfela/analisys_efficient_frontier3.ipynb#X42sZmlsZQ%3D%3D?line=22'>23</a>\u001b[0m result \u001b[39m=\u001b[39m minimize(obj,start,constraints\u001b[39m=\u001b[39;49m[constraint1, constraint2],bounds\u001b[39m=\u001b[39;49massets_constraints)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/matsz/programowanie/Optymalizacja%20portfela/analisys_efficient_frontier3.ipynb#X42sZmlsZQ%3D%3D?line=23'>24</a>\u001b[0m \u001b[39m#result = np.append(result.x, [expectedMean], [result.fun])    \u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/matsz/programowanie/Optymalizacja%20portfela/analisys_efficient_frontier3.ipynb#X42sZmlsZQ%3D%3D?line=25'>26</a>\u001b[0m \u001b[39mreturn\u001b[39;00m np\u001b[39m.\u001b[39marray(result\u001b[39m.\u001b[39mx)\n",
      "File \u001b[1;32mc:\\Users\\matsz\\miniconda3\\envs\\portfolio-optimisation\\lib\\site-packages\\scipy\\optimize\\_minimize.py:631\u001b[0m, in \u001b[0;36mminimize\u001b[1;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[0;32m    629\u001b[0m     \u001b[39mreturn\u001b[39;00m _minimize_cobyla(fun, x0, args, constraints, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39moptions)\n\u001b[0;32m    630\u001b[0m \u001b[39melif\u001b[39;00m meth \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mslsqp\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[1;32m--> 631\u001b[0m     \u001b[39mreturn\u001b[39;00m _minimize_slsqp(fun, x0, args, jac, bounds,\n\u001b[0;32m    632\u001b[0m                            constraints, callback\u001b[39m=\u001b[39mcallback, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39moptions)\n\u001b[0;32m    633\u001b[0m \u001b[39melif\u001b[39;00m meth \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mtrust-constr\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[0;32m    634\u001b[0m     \u001b[39mreturn\u001b[39;00m _minimize_trustregion_constr(fun, x0, args, jac, hess, hessp,\n\u001b[0;32m    635\u001b[0m                                         bounds, constraints,\n\u001b[0;32m    636\u001b[0m                                         callback\u001b[39m=\u001b[39mcallback, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39moptions)\n",
      "File \u001b[1;32mc:\\Users\\matsz\\miniconda3\\envs\\portfolio-optimisation\\lib\\site-packages\\scipy\\optimize\\slsqp.py:262\u001b[0m, in \u001b[0;36m_minimize_slsqp\u001b[1;34m(func, x0, args, jac, bounds, constraints, maxiter, ftol, iprint, disp, eps, callback, finite_diff_rel_step, **unknown_options)\u001b[0m\n\u001b[0;32m    259\u001b[0m     new_bounds \u001b[39m=\u001b[39m old_bound_to_new(bounds)\n\u001b[0;32m    261\u001b[0m \u001b[39m# clip the initial guess to bounds, otherwise ScalarFunction doesn't work\u001b[39;00m\n\u001b[1;32m--> 262\u001b[0m x \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mclip(x, new_bounds[\u001b[39m0\u001b[39;49m], new_bounds[\u001b[39m1\u001b[39;49m])\n\u001b[0;32m    264\u001b[0m \u001b[39m# Constraints are triaged per type into a dictionary of tuples\u001b[39;00m\n\u001b[0;32m    265\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(constraints, \u001b[39mdict\u001b[39m):\n",
      "File \u001b[1;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mclip\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\matsz\\miniconda3\\envs\\portfolio-optimisation\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2152\u001b[0m, in \u001b[0;36mclip\u001b[1;34m(a, a_min, a_max, out, **kwargs)\u001b[0m\n\u001b[0;32m   2083\u001b[0m \u001b[39m@array_function_dispatch\u001b[39m(_clip_dispatcher)\n\u001b[0;32m   2084\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mclip\u001b[39m(a, a_min, a_max, out\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m   2085\u001b[0m     \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m   2086\u001b[0m \u001b[39m    Clip (limit) the values in an array.\u001b[39;00m\n\u001b[0;32m   2087\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   2150\u001b[0m \n\u001b[0;32m   2151\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 2152\u001b[0m     \u001b[39mreturn\u001b[39;00m _wrapfunc(a, \u001b[39m'\u001b[39m\u001b[39mclip\u001b[39m\u001b[39m'\u001b[39m, a_min, a_max, out\u001b[39m=\u001b[39mout, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\matsz\\miniconda3\\envs\\portfolio-optimisation\\lib\\site-packages\\numpy\\core\\fromnumeric.py:57\u001b[0m, in \u001b[0;36m_wrapfunc\u001b[1;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[0;32m     54\u001b[0m     \u001b[39mreturn\u001b[39;00m _wrapit(obj, method, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)\n\u001b[0;32m     56\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m---> 57\u001b[0m     \u001b[39mreturn\u001b[39;00m bound(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)\n\u001b[0;32m     58\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[0;32m     59\u001b[0m     \u001b[39m# A TypeError occurs if the object does have such a method in its\u001b[39;00m\n\u001b[0;32m     60\u001b[0m     \u001b[39m# class, but its signature is not identical to that of NumPy's. This\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     64\u001b[0m     \u001b[39m# Call _wrapit from within the except clause to ensure a potential\u001b[39;00m\n\u001b[0;32m     65\u001b[0m     \u001b[39m# exception has a traceback chain.\u001b[39;00m\n\u001b[0;32m     66\u001b[0m     \u001b[39mreturn\u001b[39;00m _wrapit(obj, method, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)\n",
      "File \u001b[1;32mc:\\Users\\matsz\\miniconda3\\envs\\portfolio-optimisation\\lib\\site-packages\\numpy\\core\\_methods.py:159\u001b[0m, in \u001b[0;36m_clip\u001b[1;34m(a, min, max, out, casting, **kwargs)\u001b[0m\n\u001b[0;32m    156\u001b[0m     \u001b[39mreturn\u001b[39;00m _clip_dep_invoke_with_casting(\n\u001b[0;32m    157\u001b[0m         um\u001b[39m.\u001b[39mmaximum, a, \u001b[39mmin\u001b[39m, out\u001b[39m=\u001b[39mout, casting\u001b[39m=\u001b[39mcasting, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m    158\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 159\u001b[0m     \u001b[39mreturn\u001b[39;00m _clip_dep_invoke_with_casting(\n\u001b[0;32m    160\u001b[0m         um\u001b[39m.\u001b[39mclip, a, \u001b[39mmin\u001b[39m, \u001b[39mmax\u001b[39m, out\u001b[39m=\u001b[39mout, casting\u001b[39m=\u001b[39mcasting, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\matsz\\miniconda3\\envs\\portfolio-optimisation\\lib\\site-packages\\numpy\\core\\_methods.py:113\u001b[0m, in \u001b[0;36m_clip_dep_invoke_with_casting\u001b[1;34m(ufunc, out, casting, *args, **kwargs)\u001b[0m\n\u001b[0;32m    111\u001b[0m \u001b[39m# try to deal with broken casting rules\u001b[39;00m\n\u001b[0;32m    112\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 113\u001b[0m     \u001b[39mreturn\u001b[39;00m ufunc(\u001b[39m*\u001b[39margs, out\u001b[39m=\u001b[39mout, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m    114\u001b[0m \u001b[39mexcept\u001b[39;00m _exceptions\u001b[39m.\u001b[39m_UFuncOutputCastingError \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m    115\u001b[0m     \u001b[39m# Numpy 1.17.0, 2019-02-24\u001b[39;00m\n\u001b[0;32m    116\u001b[0m     warnings\u001b[39m.\u001b[39mwarn(\n\u001b[0;32m    117\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mConverting the output of clip from \u001b[39m\u001b[39m{!r}\u001b[39;00m\u001b[39m to \u001b[39m\u001b[39m{!r}\u001b[39;00m\u001b[39m is deprecated. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    118\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mPass `casting=\u001b[39m\u001b[39m\\\"\u001b[39;00m\u001b[39munsafe\u001b[39m\u001b[39m\\\"\u001b[39;00m\u001b[39m` explicitly to silence this warning, or \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    121\u001b[0m         stacklevel\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m\n\u001b[0;32m    122\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,) (3,) (3,) "
     ]
    }
   ],
   "source": [
    "mu_min = means[0]*12 #means.min()*12\n",
    "mu_max = 0.12 #means.max()*12\n",
    "\n",
    "portfolio_means = np.linspace(mu_min, mu_max, 12)\n",
    "\n",
    "weights = np.empty((0,len(assets_names)+2))\n",
    "for mean in portfolio_means:\n",
    "    weight = optimize(mean,means,cov_table)\n",
    "    std = pstd(weight,cov_table)\n",
    "    weight = np.append(weight, [mean, std])\n",
    "    weights = np.append(weights, [weight], axis=0)\n",
    "\n",
    "columns = np.append(assets_names, ['mu', 'std'])\n",
    "ef = pd.DataFrame(weights, columns=columns) \n",
    "ef\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minimal_weights = lambda x: np.all((np.any([x >=0.05, x == 0], axis=0))).astype(int)\n",
    "\n",
    "weights = np.array([0, 0.051, 0.5, 0.04])\n",
    "\n",
    "minimal_weights(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "W0 = 30000\n",
    "WT1 = 50000\n",
    "WT2 = 35000\n",
    "\n",
    "t = 10\n",
    "p = 0.999 #target propability\n",
    "std = 0.09\n",
    "mu = lambda std, WT: (1/t)*np.log(WT/W0) + p/np.sqrt(t)*std + 0.5*std**2\n",
    "\n",
    "stds = ef['std']\n",
    "\n",
    "mus1 = [mu(std, WT1) for std in stds ]\n",
    "\n",
    "mus2 = [mu(std, WT2) for std in stds ]\n",
    "\n",
    "plt.plot(stds, mus1)\n",
    "plt.plot(stds, mus2)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       TBSP   ACWI\n",
      "TBSP  0.22% -0.07%\n",
      "ACWI -0.07%  2.31%\n",
      "[[0.04725282 0.        ]\n",
      " [0.         0.15208061]]\n",
      "        TBSP    ACWI\n",
      "TBSP 100.00%  -9.22%\n",
      "ACWI  -9.22% 100.00%\n",
      "        TBSP    ACWI\n",
      "TBSP 100.00%  45.39%\n",
      "ACWI  45.39% 100.00%\n",
      "      0     1\n",
      "0 0.22% 0.33%\n",
      "1 0.33% 2.31%\n",
      "---\n",
      "0.09146392619858458\n",
      "---\n",
      "0.10123873094081719\n"
     ]
    }
   ],
   "source": [
    "def shrinkedCovariance(returns: pd.DataFrame, w: int):\n",
    "    std = np.diag(returns.std())*np.sqrt(12)\n",
    "    corr = returns.corr('pearson')\n",
    "    shrinked_corr = (1-w)*corr + np.ones_like(corr)*w \n",
    "    result = std @ shrinked_corr @ std\n",
    "    return std,corr, shrinked_corr, result\n",
    "\n",
    "def pstd(cov_matrix, weights):\n",
    "    return np.sqrt(weights.T @ cov_matrix @ weights)\n",
    "\n",
    "assets_names = ['TBSP', 'ACWI']\n",
    "\n",
    "portfolio_returns = assets.iloc[-120:][assets_names]\n",
    "\n",
    "std, corr, sh_corr, res = shrinkedCovariance(portfolio_returns,0.5)\n",
    "cov = portfolio_returns.cov()*12\n",
    "\n",
    "print (portfolio_returns.cov()*12)\n",
    "print(std)\n",
    "print(corr)\n",
    "print(sh_corr)\n",
    "print(res)\n",
    "print('---')\n",
    "print(pstd(cov, np.array([0.4, 0.6])))\n",
    "print('---')\n",
    "print(pstd(res, np.array([0.4, 0.6])))\n",
    "\n",
    "#print(computed_std)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TBSP    5.83%\n",
      "ACWI   18.02%\n",
      "dtype: float64\n",
      "TBSP    1.97%\n",
      "ACWI   15.60%\n",
      "dtype: float64\n",
      "TBSP    2.47%\n",
      "ACWI   12.93%\n",
      "dtype: float64\n",
      "---\n",
      "0.10942527346768507\n",
      "0.09450573239453121\n",
      "0.07687067421906144\n",
      "---\n",
      "0.126273078746576\n",
      "0.09980622208789802\n",
      "0.08496410489639229\n"
     ]
    }
   ],
   "source": [
    "ret = assets.iloc[-60:][assets_names]\n",
    "ret_24 = assets.iloc[-84:-24][assets_names]\n",
    "ret_48 = assets.iloc[-108:-48][assets_names]\n",
    "print(ret.std()*np.sqrt(12))\n",
    "print(ret_24.std()*np.sqrt(12))\n",
    "print(ret_48.std()*np.sqrt(12))\n",
    "print('---')\n",
    "\n",
    "std, corr, sh_corr, res = shrinkedCovariance(ret,0)\n",
    "std, corr, sh_corr, res_24 = shrinkedCovariance(ret_24,0)\n",
    "std, corr, sh_corr, res_48 = shrinkedCovariance(ret_48,0)\n",
    "\n",
    "\n",
    "print(pstd(res, np.array([0.4, 0.6])))\n",
    "print(pstd(res_24, np.array([0.4, 0.6])))\n",
    "print(pstd(res_48, np.array([0.4, 0.6])))\n",
    "\n",
    "\n",
    "print('---')\n",
    "std, corr, sh_corr, res = shrinkedCovariance(ret,0.75)\n",
    "std, corr, sh_corr, res_24 = shrinkedCovariance(ret_24,0.75)\n",
    "std, corr, sh_corr, res_48 = shrinkedCovariance(ret_48,0.75)\n",
    "\n",
    "\n",
    "print(pstd(res, np.array([0.4, 0.6])))\n",
    "print(pstd(res_24, np.array([0.4, 0.6])))\n",
    "print(pstd(res_48, np.array([0.4, 0.6])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.421"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "mean = 0.0362\n",
    "std = 0.15\n",
    "W0 = 100\n",
    "W1 = 105.62\n",
    "h = 1\n",
    "\n",
    "def prob2(W0, W1, mean, std, h):\n",
    "    return 1-norm.cdf((np.log(W1/W0)-(mean-0.5*std**2)*h)/(std*np.sqrt(h)))\n",
    "\n",
    "np.round(prob2(W0,W1,mean, std, 1),3)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'calculateValues' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\mszczecinski\\Analizy\\Portfolio\\analisys_efficient_frontier2.ipynb Komórka 36\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/mszczecinski/Analizy/Portfolio/analisys_efficient_frontier2.ipynb#X46sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m VT, l, k \u001b[39m=\u001b[39m calculateValues()\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/mszczecinski/Analizy/Portfolio/analisys_efficient_frontier2.ipynb#X46sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m l \u001b[39m=\u001b[39m [\u001b[39m0\u001b[39m,\u001b[39m0\u001b[39m,\u001b[39m1\u001b[39m,\u001b[39m1\u001b[39m,\u001b[39m2\u001b[39m]\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/mszczecinski/Analizy/Portfolio/analisys_efficient_frontier2.ipynb#X46sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m k \u001b[39m=\u001b[39m [\u001b[39m0\u001b[39m,\u001b[39m0\u001b[39m,\u001b[39m0\u001b[39m,\u001b[39m1\u001b[39m,\u001b[39m2\u001b[39m]\n",
      "\u001b[1;31mNameError\u001b[0m: name 'calculateValues' is not defined"
     ]
    }
   ],
   "source": [
    "VT, l, k = calculateValues()\n",
    "l = [0,0,1,1,2]\n",
    "k = [0,0,0,1,2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 2, 2, 2],\n",
       "       [0, 0, 4, 4]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = np.array([[1,2,4,5], [1,2,4,5]])\n",
    "arr2 = np.array([[2],[4]])\n",
    "np.where(arr1 >= arr2, arr2, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32976540555225764"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def __prob2(W0, W1, mean, std, h):\n",
    "    return norm.pdf((np.log(W1/W0)-(mean-0.5*std**2)*h)/(std*np.sqrt(h)))\n",
    "W0 = 1001\n",
    "W1 = 1200\n",
    "m = 0.1\n",
    "std = 0.15\n",
    "__prob2(W0,W1,m,std,h=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_strategy_for_wealth_value(WT, portfolio_strategies, Wti):\n",
    "    difference = np.absolute(WT - Wti)\n",
    "    index_minx_value = np.argmin(difference)\n",
    "    return portfolio_strategies[index_minx_value]\n",
    "\n",
    "portfolio_strategies = [2, 2, 1, 0, 0, 0]\n",
    "WT = np.array([49,50,60, 80, 100,105])\n",
    "Wti = 105 - 40\n",
    "get_strategy_for_wealth_value(WT, portfolio_strategies, Wti)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([[1,2], [3,4], [5,6]])\n",
    "np.linalg.matrix_rank(arr)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def WMax(t,W0, infusions, meanMax,stdMin,stdMax):\n",
    "    valueOfInfusions = 0\n",
    "    for i in range(t):\n",
    "        valueOfInfusions += infusions[i]*np.exp((meanMax - (stdMin**2)/2)*(t-i) + (3*stdMax*np.sqrt(t-i)))\n",
    "                                                           \n",
    "    return W0*np.exp((meanMax-(stdMin**2/2))*t + 3*stdMax*np.sqrt(t)) + valueOfInfusions\n",
    "\n",
    "def WMin(t, W0, infusions, goals, meanMin, stdMin, stdMax):\n",
    "    valueOfInfusions = 0\n",
    "    for i in range(t+1):\n",
    "        valueOfInfusions += (infusions[i]-goals[i])*np.exp((meanMin - (stdMin**2)/2)*(t-i) - (3*stdMax*np.sqrt(t-i)))\n",
    "\n",
    "    return W0*np.e**((meanMin-stdMax**2/2)*t - 3*stdMax*np.sqrt(t)) + valueOfInfusions\n",
    "\n",
    "\n",
    "def deductE(row, logW0):\n",
    "    diff = row - logW0\n",
    "    e = diff[diff >= 0].min()\n",
    "    return row - e\n",
    "\n",
    "def generateGrid(W0, T, iMax, infusions, goals, minMean, minStd, maxMean, maxStd) ->np.array:\n",
    "    grid = np.zeros((T,iMax))\n",
    "    logW0 = np.log(W0)\n",
    "    Wmin = 1\n",
    "    for t in range(1,T+1):\n",
    "        wMin = WMin(t,W0,infusions,goals, minMean,minStd,maxStd)\n",
    "        wMin = Wmin if wMin < Wmin else wMin\n",
    "        wMax = WMax(t,W0, infusions, maxMean,minStd, maxStd)\n",
    "        row = np.linspace(np.log(wMin),np.log(wMax),iMax)\n",
    "        row = deductE(row,logW0)\n",
    "        grid[t-1] = row\n",
    "    return np.exp(grid)\n",
    "\n",
    "\n",
    "def calculateTransitionPropabilities(portfolioMeasures, W0: int, W1: np.array, infusions, costs, h=1):\n",
    "    mean = portfolioMeasures[0]\n",
    "    std = portfolioMeasures[1]\n",
    "    p = norm.pdf((np.log(W1/(W0+infusions-costs))-(mean-0.5*std**2)*h)/(std*np.sqrt(h)))\n",
    "    return p/p.sum()\n",
    "\n",
    "def calculateValuesForLastPeriod(W: np.array, k: np.array):\n",
    "    values = np.zeros((len(k), len(W)))\n",
    "    for i in range(len(k)):\n",
    "        values[i] = np.where(W >= k[i,0], k[i,1], 0 )\n",
    "    return np.amax(values, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolios = np.array([[0.0526, 0.0374], [0.07059443, 0.103057], [0.0886, 0.1954]])\n",
    "W0 = 10\n",
    "T = 12\n",
    "#infusions = [0,25000,25000,25000,25000,25000,25000,25000,25000,25000,25000]\n",
    "infusions = np.zeros(13)\n",
    "goals = [0,0,0,0,0,0,0,100,0,0,0,0,150]\n",
    "iMax = 500\n",
    "\n",
    "grid = generateGrid(W0,T,iMax, infusions, goals, portfolios[0,0], portfolios[0,1], portfolios[2,0], portfolios[2,1])\n",
    "\n",
    "gridT = grid.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2499999999999998"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = [1,2,3,4,5]\n",
    "arr2 = [4,5,6,7,8]\n",
    "np.sqrt(np.var(arr))\n",
    "np.std(arr)\n",
    "np.mean(arr)\n",
    "np.cov(arr,arr2)[0,1]/(np.std(arr)*np.std(arr2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculateTransitionPropabilitiesForAllPorfolios(portfolios,WT,WT1,infusions, h=1):\n",
    "    l = len(portfolios)\n",
    "    i = len(WT)\n",
    "    b = (portfolios[:,0]-0.5*portfolios[:,1]**2)*h\n",
    "    bi = np.repeat(b, i).reshape(l*i,1)\n",
    "    c = portfolios[:,1]*np.sqrt(h)\n",
    "    ci = np.repeat(c, i).reshape(l*i,1)\n",
    "   \n",
    "    Wt1 = np.tile(WT1, (l*i,1))\n",
    "    Wt = np.tile(WT,(l,1)).reshape(i*l,1)+infusions\n",
    "    result = norm.pdf((np.log(Wt1/Wt)- bi)/ci).reshape(l,i,len(WT1))\n",
    "    return result/result.sum(2).reshape(l,i,1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[1.   0.   0.   0.   0.   0.  ]\n",
      "  [1.   0.   0.   0.   0.   0.  ]\n",
      "  [1.   0.   0.   0.   0.   0.  ]\n",
      "  [0.77 0.22 0.01 0.   0.   0.  ]\n",
      "  [0.   0.   0.02 0.1  0.24 0.63]\n",
      "  [0.   0.   0.   0.02 0.07 0.91]]\n",
      "\n",
      " [[0.91 0.08 0.01 0.   0.   0.  ]\n",
      "  [0.9  0.09 0.01 0.   0.   0.  ]\n",
      "  [0.77 0.18 0.04 0.01 0.01 0.  ]\n",
      "  [0.28 0.26 0.18 0.13 0.1  0.05]\n",
      "  [0.03 0.08 0.16 0.21 0.24 0.29]\n",
      "  [0.02 0.05 0.13 0.2  0.24 0.36]]\n",
      "\n",
      " [[0.47 0.24 0.12 0.08 0.06 0.03]\n",
      "  [0.46 0.24 0.12 0.08 0.06 0.03]\n",
      "  [0.35 0.24 0.15 0.11 0.09 0.05]\n",
      "  [0.2  0.19 0.18 0.16 0.15 0.12]\n",
      "  [0.11 0.14 0.17 0.18 0.19 0.2 ]\n",
      "  [0.09 0.13 0.17 0.19 0.2  0.22]]]\n",
      "[2 2 2 2 0 0]\n",
      "[16.4 17.2 25.9 43.6 98.3 99.9]\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "def get_portfolios_strategies(VT1, propabilities):\n",
    "    Vt = VT1 * propabilities\n",
    "    sums = Vt.sum(2)\n",
    "    maxes = np.amax(sums,0)\n",
    "    portfolios_ids = np.argmax(sums,0)\n",
    "    chosen_propabilities = 0#np.take(propabilities,portfolios_ids,0)\n",
    "    return portfolios_ids, maxes, chosen_propabilities\n",
    "\n",
    "portfolios = np.array([[0.0526, 0.0374], [0.07059443, 0.103057  ], [0.0886, 0.1954]])\n",
    "WT = np.array([49,50,60,80,100,105])\n",
    "WT1 = np.array([90,95,100,103,105,110])\n",
    "VT1 = [0,0,0,100,100,100]\n",
    "c = 50\n",
    "#goals = [[50, 80], [30, 50],]\n",
    "VTc0 = np.array([29.014, 29.55, 77.286, 95.997])\n",
    "\n",
    "probabilities = calculateTransitionPropabilitiesForAllPorfolios(portfolios,WT,WT1,5,1)\n",
    "print(np.round(probabilities,3))\n",
    "\"\"\"\"\n",
    "portfolios_strategies, VT, chosen_propabilities, V = get_portfolios_strategies(VT1,probabilities)\n",
    "print(portfolios_strategies)\n",
    "print(VT)\n",
    "print(np.round(chosen_propabilities,3))\n",
    "print(np.round(V,3)) \n",
    "\"\"\"\n",
    "porfolios_strategies, VT, chosen_propabilities = get_portfolios_strategies(VT1,probabilities)\n",
    "print(np.round(porfolios_strategies,1))\n",
    "print(np.round(VT,1))\n",
    "print(np.round(chosen_propabilities,2))\n",
    "\n"
   ]
  }
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