CPPI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_datareader.data as web\n",
    "import pandas as pd\n",
    "from datetime import date\n",
    "import yahoo_fin.stock_info as si\n",
    "from yahoo_fin.stock_info import get_data\n",
    "import numpy as np\n",
    "import math\n",
    "import time\n",
    "from yahoofinancials import YahooFinancials\n",
    "from scipy import stats\n",
    "from statistics import mean\n",
    "from scipy.stats import percentileofscore\n",
    "from datetime import datetime, timedelta\n",
    "import pandas_market_calendars as mcal\n",
    "from decimal import Decimal\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import plotly.graph_objects as go\n",
    "from plotly.offline import iplot\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "import pickle\n",
    "import yfinance as yf\n",
    "from matplotlib.pyplot import figure\n",
    "import robin_stocks as rs\n",
    "import mplfinance as mpf\n",
    "import matplotlib.patches as mpatches\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting sector and market cap from robin:\n",
    "1. Using yahoo_fin.stock_info to get the list of all tickers in SP500\n",
    "2. Using robin_stocks to match each ticker with its sector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp_list = si.tickers_sp500()\n",
    "sp_list = [x for x in sp_list if x != 'CXO']\n",
    "ll = []\n",
    "for tick in sp_list:\n",
    "    sector = rs.stocks.get_fundamentals(tick, info='sector')\n",
    "    market_cap = rs.stocks.get_fundamentals(tick, info='market_cap')\n",
    "    ll.append([tick, sector, market_cap])\n",
    "col = ['Ticker', 'Sector', 'Market Cap']    \n",
    "df_sector = pd.DataFrame(ll, columns=col)\n",
    "df_sector['Sector'] = df_sector['Sector'].str[0]\n",
    "df_sector['Market Cap'] = df_sector['Market Cap'].str[0]\n",
    "df_sector['Market Cap'] = df_sector['Market Cap'].astype(float)\n",
    "df_sector = df_sector.set_index('Ticker')\n",
    "df_sector['Weight'] = 'N/A'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assigning weights to each company in its sector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sector</th>\n",
       "      <th>Market Cap</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Health Technology</td>\n",
       "      <td>3.676362e+10</td>\n",
       "      <td>0.0105248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAL</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>1.110287e+10</td>\n",
       "      <td>0.0177007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAP</th>\n",
       "      <td>Retail Trade</td>\n",
       "      <td>1.032378e+10</td>\n",
       "      <td>0.00318735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>Electronic Technology</td>\n",
       "      <td>2.234439e+12</td>\n",
       "      <td>0.454762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABBV</th>\n",
       "      <td>Health Technology</td>\n",
       "      <td>1.866824e+11</td>\n",
       "      <td>0.0534439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Sector    Market Cap      Weight\n",
       "Ticker                                                 \n",
       "A           Health Technology  3.676362e+10   0.0105248\n",
       "AAL            Transportation  1.110287e+10   0.0177007\n",
       "AAP              Retail Trade  1.032378e+10  0.00318735\n",
       "AAPL    Electronic Technology  2.234439e+12    0.454762\n",
       "ABBV        Health Technology  1.866824e+11   0.0534439"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in df_sector['Sector'].unique().tolist():\n",
    "    total_sector_cap = df_sector[df_sector['Sector'] == i]['Market Cap'].sum()\n",
    "    for ii in df_sector[df_sector['Sector'] == i].index.tolist():\n",
    "        df_sector.loc[ii, 'Weight'] = df_sector.loc[ii, 'Market Cap'] / total_sector_cap        \n",
    "df_sector.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading prices for each ticker in S&P500:\n",
    "for the last 5 years using robin_stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp_list = si.tickers_sp500()\n",
    "#CXO is not a valid member of SP500, getting rid of it:\n",
    "sp_list = [x for x in sp_list if x != 'CXO']\n",
    "l = []\n",
    "for tick in sp_list:\n",
    "    data = rs.stocks.get_stock_historicals(tick, interval=\"day\", span=\"5year\")\n",
    "    df_data = pd.DataFrame(data)\n",
    "    df_data = df_data.set_index('begins_at')\n",
    "    df_data = df_data[['close_price', 'volume']]\n",
    "    df_data = df_data.add_suffix(f'_{tick}')\n",
    "    l.append(df_data)\n",
    "final = pd.concat(l, axis=1)   \n",
    "final.index = pd.to_datetime(final.index)\n",
    "new_format = \"%Y-%m-%d\"\n",
    "final.index = final.index.strftime(new_format)\n",
    "final = final.dropna()\n",
    "final = final.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>close_price_A</th>\n",
       "      <th>volume_A</th>\n",
       "      <th>close_price_AAL</th>\n",
       "      <th>volume_AAL</th>\n",
       "      <th>close_price_AAP</th>\n",
       "      <th>volume_AAP</th>\n",
       "      <th>close_price_AAPL</th>\n",
       "      <th>volume_AAPL</th>\n",
       "      <th>close_price_ABBV</th>\n",
       "      <th>volume_ABBV</th>\n",
       "      <th>...</th>\n",
       "      <th>close_price_YUM</th>\n",
       "      <th>volume_YUM</th>\n",
       "      <th>close_price_ZBH</th>\n",
       "      <th>volume_ZBH</th>\n",
       "      <th>close_price_ZBRA</th>\n",
       "      <th>volume_ZBRA</th>\n",
       "      <th>close_price_ZION</th>\n",
       "      <th>volume_ZION</th>\n",
       "      <th>close_price_ZTS</th>\n",
       "      <th>volume_ZTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-04</th>\n",
       "      <td>37.420000</td>\n",
       "      <td>2833492.0</td>\n",
       "      <td>38.210000</td>\n",
       "      <td>8599627.0</td>\n",
       "      <td>147.850000</td>\n",
       "      <td>1573505.0</td>\n",
       "      <td>24.150000</td>\n",
       "      <td>185886608.0</td>\n",
       "      <td>56.760000</td>\n",
       "      <td>10351220.0</td>\n",
       "      <td>...</td>\n",
       "      <td>50.512078</td>\n",
       "      <td>17638480.0</td>\n",
       "      <td>97.550000</td>\n",
       "      <td>1561932.0</td>\n",
       "      <td>61.010000</td>\n",
       "      <td>864605.0</td>\n",
       "      <td>22.050000</td>\n",
       "      <td>5028633.0</td>\n",
       "      <td>41.540000</td>\n",
       "      <td>3949771.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-05</th>\n",
       "      <td>36.040000</td>\n",
       "      <td>4424609.0</td>\n",
       "      <td>36.750000</td>\n",
       "      <td>10072744.0</td>\n",
       "      <td>143.940000</td>\n",
       "      <td>1284245.0</td>\n",
       "      <td>23.505000</td>\n",
       "      <td>185672256.0</td>\n",
       "      <td>53.120000</td>\n",
       "      <td>13263242.0</td>\n",
       "      <td>...</td>\n",
       "      <td>48.730778</td>\n",
       "      <td>11173493.0</td>\n",
       "      <td>95.010000</td>\n",
       "      <td>3952411.0</td>\n",
       "      <td>57.380000</td>\n",
       "      <td>877571.0</td>\n",
       "      <td>21.630000</td>\n",
       "      <td>3199305.0</td>\n",
       "      <td>40.910000</td>\n",
       "      <td>4058061.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-08</th>\n",
       "      <td>34.800000</td>\n",
       "      <td>3888601.0</td>\n",
       "      <td>35.550000</td>\n",
       "      <td>12821858.0</td>\n",
       "      <td>141.450000</td>\n",
       "      <td>2109895.0</td>\n",
       "      <td>23.752500</td>\n",
       "      <td>216085500.0</td>\n",
       "      <td>52.890000</td>\n",
       "      <td>9369377.0</td>\n",
       "      <td>...</td>\n",
       "      <td>47.103159</td>\n",
       "      <td>7188275.0</td>\n",
       "      <td>92.030000</td>\n",
       "      <td>2825845.0</td>\n",
       "      <td>54.440000</td>\n",
       "      <td>728786.0</td>\n",
       "      <td>20.720000</td>\n",
       "      <td>4836154.0</td>\n",
       "      <td>40.190000</td>\n",
       "      <td>4314301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-09</th>\n",
       "      <td>35.370000</td>\n",
       "      <td>3464464.0</td>\n",
       "      <td>36.190000</td>\n",
       "      <td>11143254.0</td>\n",
       "      <td>141.500000</td>\n",
       "      <td>1239814.0</td>\n",
       "      <td>23.747500</td>\n",
       "      <td>177324780.0</td>\n",
       "      <td>53.480000</td>\n",
       "      <td>7655796.0</td>\n",
       "      <td>...</td>\n",
       "      <td>46.432552</td>\n",
       "      <td>6455415.0</td>\n",
       "      <td>91.900000</td>\n",
       "      <td>1750579.0</td>\n",
       "      <td>54.680000</td>\n",
       "      <td>662575.0</td>\n",
       "      <td>20.770000</td>\n",
       "      <td>4782764.0</td>\n",
       "      <td>39.860000</td>\n",
       "      <td>6277573.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-10</th>\n",
       "      <td>35.850000</td>\n",
       "      <td>3169951.0</td>\n",
       "      <td>37.120000</td>\n",
       "      <td>8114127.0</td>\n",
       "      <td>138.570000</td>\n",
       "      <td>2203727.0</td>\n",
       "      <td>23.567500</td>\n",
       "      <td>169374404.0</td>\n",
       "      <td>52.720000</td>\n",
       "      <td>9302768.0</td>\n",
       "      <td>...</td>\n",
       "      <td>46.635131</td>\n",
       "      <td>3601678.0</td>\n",
       "      <td>92.910000</td>\n",
       "      <td>1279105.0</td>\n",
       "      <td>57.800000</td>\n",
       "      <td>890252.0</td>\n",
       "      <td>20.740000</td>\n",
       "      <td>4048528.0</td>\n",
       "      <td>39.330000</td>\n",
       "      <td>6629167.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1010 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           close_price_A   volume_A close_price_AAL  volume_AAL  \\\n",
       "2016-02-04     37.420000  2833492.0       38.210000   8599627.0   \n",
       "2016-02-05     36.040000  4424609.0       36.750000  10072744.0   \n",
       "2016-02-08     34.800000  3888601.0       35.550000  12821858.0   \n",
       "2016-02-09     35.370000  3464464.0       36.190000  11143254.0   \n",
       "2016-02-10     35.850000  3169951.0       37.120000   8114127.0   \n",
       "\n",
       "           close_price_AAP  volume_AAP close_price_AAPL  volume_AAPL  \\\n",
       "2016-02-04      147.850000   1573505.0        24.150000  185886608.0   \n",
       "2016-02-05      143.940000   1284245.0        23.505000  185672256.0   \n",
       "2016-02-08      141.450000   2109895.0        23.752500  216085500.0   \n",
       "2016-02-09      141.500000   1239814.0        23.747500  177324780.0   \n",
       "2016-02-10      138.570000   2203727.0        23.567500  169374404.0   \n",
       "\n",
       "           close_price_ABBV  volume_ABBV  ... close_price_YUM  volume_YUM  \\\n",
       "2016-02-04        56.760000   10351220.0  ...       50.512078  17638480.0   \n",
       "2016-02-05        53.120000   13263242.0  ...       48.730778  11173493.0   \n",
       "2016-02-08        52.890000    9369377.0  ...       47.103159   7188275.0   \n",
       "2016-02-09        53.480000    7655796.0  ...       46.432552   6455415.0   \n",
       "2016-02-10        52.720000    9302768.0  ...       46.635131   3601678.0   \n",
       "\n",
       "           close_price_ZBH  volume_ZBH close_price_ZBRA  volume_ZBRA  \\\n",
       "2016-02-04       97.550000   1561932.0        61.010000     864605.0   \n",
       "2016-02-05       95.010000   3952411.0        57.380000     877571.0   \n",
       "2016-02-08       92.030000   2825845.0        54.440000     728786.0   \n",
       "2016-02-09       91.900000   1750579.0        54.680000     662575.0   \n",
       "2016-02-10       92.910000   1279105.0        57.800000     890252.0   \n",
       "\n",
       "           close_price_ZION  volume_ZION close_price_ZTS  volume_ZTS  \n",
       "2016-02-04        22.050000    5028633.0       41.540000   3949771.0  \n",
       "2016-02-05        21.630000    3199305.0       40.910000   4058061.0  \n",
       "2016-02-08        20.720000    4836154.0       40.190000   4314301.0  \n",
       "2016-02-09        20.770000    4782764.0       39.860000   6277573.0  \n",
       "2016-02-10        20.740000    4048528.0       39.330000   6629167.0  \n",
       "\n",
       "[5 rows x 1010 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We'll keep only price data and change column names to tickers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>AAL</th>\n",
       "      <th>AAP</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABBV</th>\n",
       "      <th>ABC</th>\n",
       "      <th>ABMD</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACN</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>...</th>\n",
       "      <th>XLNX</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XRX</th>\n",
       "      <th>XYL</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBH</th>\n",
       "      <th>ZBRA</th>\n",
       "      <th>ZION</th>\n",
       "      <th>ZTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-28</th>\n",
       "      <td>121.56</td>\n",
       "      <td>18.10</td>\n",
       "      <td>153.34</td>\n",
       "      <td>137.09</td>\n",
       "      <td>104.21</td>\n",
       "      <td>106.90</td>\n",
       "      <td>355.13</td>\n",
       "      <td>120.39</td>\n",
       "      <td>249.34</td>\n",
       "      <td>465.67</td>\n",
       "      <td>...</td>\n",
       "      <td>133.57</td>\n",
       "      <td>46.06</td>\n",
       "      <td>55.34</td>\n",
       "      <td>21.22</td>\n",
       "      <td>99.09</td>\n",
       "      <td>102.53</td>\n",
       "      <td>157.03</td>\n",
       "      <td>393.03</td>\n",
       "      <td>45.17</td>\n",
       "      <td>156.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-29</th>\n",
       "      <td>120.17</td>\n",
       "      <td>17.17</td>\n",
       "      <td>149.14</td>\n",
       "      <td>131.96</td>\n",
       "      <td>102.48</td>\n",
       "      <td>104.20</td>\n",
       "      <td>348.25</td>\n",
       "      <td>123.59</td>\n",
       "      <td>241.92</td>\n",
       "      <td>458.77</td>\n",
       "      <td>...</td>\n",
       "      <td>130.57</td>\n",
       "      <td>44.84</td>\n",
       "      <td>53.49</td>\n",
       "      <td>21.03</td>\n",
       "      <td>96.59</td>\n",
       "      <td>101.49</td>\n",
       "      <td>153.67</td>\n",
       "      <td>387.83</td>\n",
       "      <td>44.14</td>\n",
       "      <td>154.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-01</th>\n",
       "      <td>120.83</td>\n",
       "      <td>16.84</td>\n",
       "      <td>150.97</td>\n",
       "      <td>134.14</td>\n",
       "      <td>102.30</td>\n",
       "      <td>106.89</td>\n",
       "      <td>341.86</td>\n",
       "      <td>122.54</td>\n",
       "      <td>247.57</td>\n",
       "      <td>470.00</td>\n",
       "      <td>...</td>\n",
       "      <td>135.31</td>\n",
       "      <td>44.92</td>\n",
       "      <td>53.92</td>\n",
       "      <td>22.59</td>\n",
       "      <td>97.14</td>\n",
       "      <td>103.06</td>\n",
       "      <td>153.90</td>\n",
       "      <td>397.33</td>\n",
       "      <td>44.70</td>\n",
       "      <td>155.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-02</th>\n",
       "      <td>123.19</td>\n",
       "      <td>16.59</td>\n",
       "      <td>154.08</td>\n",
       "      <td>134.99</td>\n",
       "      <td>103.47</td>\n",
       "      <td>105.62</td>\n",
       "      <td>347.84</td>\n",
       "      <td>120.24</td>\n",
       "      <td>248.75</td>\n",
       "      <td>484.93</td>\n",
       "      <td>...</td>\n",
       "      <td>137.31</td>\n",
       "      <td>45.63</td>\n",
       "      <td>54.56</td>\n",
       "      <td>22.85</td>\n",
       "      <td>97.18</td>\n",
       "      <td>106.30</td>\n",
       "      <td>158.50</td>\n",
       "      <td>405.19</td>\n",
       "      <td>45.80</td>\n",
       "      <td>157.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-03</th>\n",
       "      <td>122.15</td>\n",
       "      <td>17.60</td>\n",
       "      <td>155.28</td>\n",
       "      <td>133.94</td>\n",
       "      <td>106.95</td>\n",
       "      <td>105.53</td>\n",
       "      <td>326.23</td>\n",
       "      <td>120.22</td>\n",
       "      <td>248.37</td>\n",
       "      <td>481.92</td>\n",
       "      <td>...</td>\n",
       "      <td>135.39</td>\n",
       "      <td>47.42</td>\n",
       "      <td>54.64</td>\n",
       "      <td>23.57</td>\n",
       "      <td>95.12</td>\n",
       "      <td>104.73</td>\n",
       "      <td>158.56</td>\n",
       "      <td>403.69</td>\n",
       "      <td>46.03</td>\n",
       "      <td>155.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 505 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 A    AAL     AAP    AAPL    ABBV     ABC    ABMD     ABT  \\\n",
       "2021-01-28  121.56  18.10  153.34  137.09  104.21  106.90  355.13  120.39   \n",
       "2021-01-29  120.17  17.17  149.14  131.96  102.48  104.20  348.25  123.59   \n",
       "2021-02-01  120.83  16.84  150.97  134.14  102.30  106.89  341.86  122.54   \n",
       "2021-02-02  123.19  16.59  154.08  134.99  103.47  105.62  347.84  120.24   \n",
       "2021-02-03  122.15  17.60  155.28  133.94  106.95  105.53  326.23  120.22   \n",
       "\n",
       "               ACN    ADBE  ...    XLNX    XOM   XRAY    XRX    XYL     YUM  \\\n",
       "2021-01-28  249.34  465.67  ...  133.57  46.06  55.34  21.22  99.09  102.53   \n",
       "2021-01-29  241.92  458.77  ...  130.57  44.84  53.49  21.03  96.59  101.49   \n",
       "2021-02-01  247.57  470.00  ...  135.31  44.92  53.92  22.59  97.14  103.06   \n",
       "2021-02-02  248.75  484.93  ...  137.31  45.63  54.56  22.85  97.18  106.30   \n",
       "2021-02-03  248.37  481.92  ...  135.39  47.42  54.64  23.57  95.12  104.73   \n",
       "\n",
       "               ZBH    ZBRA   ZION     ZTS  \n",
       "2021-01-28  157.03  393.03  45.17  156.53  \n",
       "2021-01-29  153.67  387.83  44.14  154.25  \n",
       "2021-02-01  153.90  397.33  44.70  155.58  \n",
       "2021-02-02  158.50  405.19  45.80  157.39  \n",
       "2021-02-03  158.56  403.69  46.03  155.23  \n",
       "\n",
       "[5 rows x 505 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price = final.filter(like='close_price_')\n",
    "ii = sp_list\n",
    "price = price.rename(columns={f'close_price_{i}': i for i in ii})\n",
    "price[ii] = price[ii].astype(float)\n",
    "#returns = price.pct_change()\n",
    "#returns\n",
    "price.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sector prices\n",
    "In order to get prices for each sector we multiply each ticker price by its weight and sum the result for each ticker in sector list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Multiplying ticker price by its weight:\n",
    "price_weighted = price.copy()\n",
    "all_sectors = df_sector['Sector'].tolist()\n",
    "for sect in set(all_sectors):\n",
    "    in_sector = df_sector[df_sector['Sector'] == sect].index.tolist()\n",
    "    for i in in_sector:\n",
    "        price_weighted[i] = price_weighted[i] * df_sector.loc[i, 'Weight']\n",
    "\n",
    "#Getting sum of each weighted price to come to sector price\n",
    "price_sector = pd.DataFrame()\n",
    "for sect in set(all_sectors):\n",
    "    in_sector = df_sector[df_sector['Sector'] == sect].index.tolist()\n",
    "    price_sector[sect] = (price_weighted[[i for i in in_sector]]).sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Utilities</th>\n",
       "      <th>Energy Minerals</th>\n",
       "      <th>Consumer Services</th>\n",
       "      <th>Producer Manufacturing</th>\n",
       "      <th>Industrial Services</th>\n",
       "      <th>Process Industries</th>\n",
       "      <th>Consumer Durables</th>\n",
       "      <th>Non-Energy Minerals</th>\n",
       "      <th>Consumer Non-Durables</th>\n",
       "      <th>Commercial Services</th>\n",
       "      <th>Retail Trade</th>\n",
       "      <th>Health Services</th>\n",
       "      <th>Finance</th>\n",
       "      <th>Distribution Services</th>\n",
       "      <th>Health Technology</th>\n",
       "      <th>Transportation</th>\n",
       "      <th>Technology Services</th>\n",
       "      <th>Electronic Technology</th>\n",
       "      <th>Communications</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-04</th>\n",
       "      <td>51.232834</td>\n",
       "      <td>71.238186</td>\n",
       "      <td>129.970270</td>\n",
       "      <td>70.488847</td>\n",
       "      <td>38.827826</td>\n",
       "      <td>102.543029</td>\n",
       "      <td>60.882542</td>\n",
       "      <td>40.975913</td>\n",
       "      <td>71.862738</td>\n",
       "      <td>70.284957</td>\n",
       "      <td>321.360498</td>\n",
       "      <td>108.838116</td>\n",
       "      <td>79.999356</td>\n",
       "      <td>88.522304</td>\n",
       "      <td>89.086681</td>\n",
       "      <td>73.048758</td>\n",
       "      <td>298.051757</td>\n",
       "      <td>45.371420</td>\n",
       "      <td>42.112645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-05</th>\n",
       "      <td>51.398537</td>\n",
       "      <td>69.598051</td>\n",
       "      <td>124.956039</td>\n",
       "      <td>70.137023</td>\n",
       "      <td>38.176522</td>\n",
       "      <td>101.570194</td>\n",
       "      <td>58.333479</td>\n",
       "      <td>41.048228</td>\n",
       "      <td>71.528737</td>\n",
       "      <td>68.696447</td>\n",
       "      <td>303.130779</td>\n",
       "      <td>106.491770</td>\n",
       "      <td>78.511381</td>\n",
       "      <td>88.169410</td>\n",
       "      <td>86.664011</td>\n",
       "      <td>72.309357</td>\n",
       "      <td>286.986818</td>\n",
       "      <td>44.201737</td>\n",
       "      <td>41.643694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-08</th>\n",
       "      <td>51.265581</td>\n",
       "      <td>70.582532</td>\n",
       "      <td>121.820972</td>\n",
       "      <td>69.514533</td>\n",
       "      <td>37.286484</td>\n",
       "      <td>99.456788</td>\n",
       "      <td>55.710104</td>\n",
       "      <td>39.928755</td>\n",
       "      <td>71.368452</td>\n",
       "      <td>66.162753</td>\n",
       "      <td>295.077666</td>\n",
       "      <td>102.988669</td>\n",
       "      <td>76.385867</td>\n",
       "      <td>87.805230</td>\n",
       "      <td>85.379217</td>\n",
       "      <td>72.773277</td>\n",
       "      <td>285.638560</td>\n",
       "      <td>43.645444</td>\n",
       "      <td>41.170192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-09</th>\n",
       "      <td>51.545823</td>\n",
       "      <td>68.931031</td>\n",
       "      <td>123.038100</td>\n",
       "      <td>69.770054</td>\n",
       "      <td>36.939404</td>\n",
       "      <td>100.604230</td>\n",
       "      <td>55.733193</td>\n",
       "      <td>41.079920</td>\n",
       "      <td>71.833832</td>\n",
       "      <td>66.170526</td>\n",
       "      <td>292.240199</td>\n",
       "      <td>104.428941</td>\n",
       "      <td>76.568822</td>\n",
       "      <td>87.766610</td>\n",
       "      <td>85.468641</td>\n",
       "      <td>73.366769</td>\n",
       "      <td>284.134394</td>\n",
       "      <td>43.377736</td>\n",
       "      <td>40.755007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-10</th>\n",
       "      <td>51.430641</td>\n",
       "      <td>68.552231</td>\n",
       "      <td>124.561863</td>\n",
       "      <td>69.047340</td>\n",
       "      <td>37.107421</td>\n",
       "      <td>100.327113</td>\n",
       "      <td>55.446956</td>\n",
       "      <td>41.648387</td>\n",
       "      <td>71.647383</td>\n",
       "      <td>66.865668</td>\n",
       "      <td>296.604856</td>\n",
       "      <td>105.870940</td>\n",
       "      <td>76.746514</td>\n",
       "      <td>87.935774</td>\n",
       "      <td>86.046782</td>\n",
       "      <td>72.990272</td>\n",
       "      <td>286.648939</td>\n",
       "      <td>43.194832</td>\n",
       "      <td>40.788630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Utilities  Energy Minerals  Consumer Services  \\\n",
       "2016-02-04  51.232834        71.238186         129.970270   \n",
       "2016-02-05  51.398537        69.598051         124.956039   \n",
       "2016-02-08  51.265581        70.582532         121.820972   \n",
       "2016-02-09  51.545823        68.931031         123.038100   \n",
       "2016-02-10  51.430641        68.552231         124.561863   \n",
       "\n",
       "            Producer Manufacturing  Industrial Services  Process Industries  \\\n",
       "2016-02-04               70.488847            38.827826          102.543029   \n",
       "2016-02-05               70.137023            38.176522          101.570194   \n",
       "2016-02-08               69.514533            37.286484           99.456788   \n",
       "2016-02-09               69.770054            36.939404          100.604230   \n",
       "2016-02-10               69.047340            37.107421          100.327113   \n",
       "\n",
       "            Consumer Durables  Non-Energy Minerals  Consumer Non-Durables  \\\n",
       "2016-02-04          60.882542            40.975913              71.862738   \n",
       "2016-02-05          58.333479            41.048228              71.528737   \n",
       "2016-02-08          55.710104            39.928755              71.368452   \n",
       "2016-02-09          55.733193            41.079920              71.833832   \n",
       "2016-02-10          55.446956            41.648387              71.647383   \n",
       "\n",
       "            Commercial Services  Retail Trade  Health Services    Finance  \\\n",
       "2016-02-04            70.284957    321.360498       108.838116  79.999356   \n",
       "2016-02-05            68.696447    303.130779       106.491770  78.511381   \n",
       "2016-02-08            66.162753    295.077666       102.988669  76.385867   \n",
       "2016-02-09            66.170526    292.240199       104.428941  76.568822   \n",
       "2016-02-10            66.865668    296.604856       105.870940  76.746514   \n",
       "\n",
       "            Distribution Services  Health Technology  Transportation  \\\n",
       "2016-02-04              88.522304          89.086681       73.048758   \n",
       "2016-02-05              88.169410          86.664011       72.309357   \n",
       "2016-02-08              87.805230          85.379217       72.773277   \n",
       "2016-02-09              87.766610          85.468641       73.366769   \n",
       "2016-02-10              87.935774          86.046782       72.990272   \n",
       "\n",
       "            Technology Services  Electronic Technology  Communications  \n",
       "2016-02-04           298.051757              45.371420       42.112645  \n",
       "2016-02-05           286.986818              44.201737       41.643694  \n",
       "2016-02-08           285.638560              43.645444       41.170192  \n",
       "2016-02-09           284.134394              43.377736       40.755007  \n",
       "2016-02-10           286.648939              43.194832       40.788630  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_sector.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining Constant Proportion Portfolio Return (CPPI)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "${V}_{risky} = m*(V - F)$ <br>\n",
    "where\n",
    "${V}_{risky}$ is the value of assets in the risky portfolio\n",
    "$V$ is the starting value of the total portfolio\n",
    "$F$ is the asset level below which the total portfolio should not fall and\n",
    "$m \\geq 1$ is the multiplier (how risky you wanna be) <br>\n",
    "<br>\n",
    "We are going to use STIP ETF as \"safe\" asset where $V - {V}_{risky}$ will be invested"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "safe = rs.stocks.get_stock_historicals('STIP', interval=\"day\", span=\"5year\")\n",
    "df_safe = pd.DataFrame(safe)\n",
    "df_safe = df_safe.set_index('begins_at')\n",
    "df_safe = df_safe[['close_price', 'volume']]\n",
    "df_safe.index = pd.to_datetime(df_safe.index)\n",
    "df_safe['close_price'] = df_safe['close_price'].astype(float)\n",
    "df_safe['Return'] = df_safe['close_price'].pct_change()\n",
    "df_safe = df_safe.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close_price</th>\n",
       "      <th>volume</th>\n",
       "      <th>Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>begins_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-05 00:00:00+00:00</th>\n",
       "      <td>99.2600</td>\n",
       "      <td>19417</td>\n",
       "      <td>-0.001408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-08 00:00:00+00:00</th>\n",
       "      <td>99.1400</td>\n",
       "      <td>23218</td>\n",
       "      <td>-0.001209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-09 00:00:00+00:00</th>\n",
       "      <td>99.0400</td>\n",
       "      <td>22708</td>\n",
       "      <td>-0.001009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-10 00:00:00+00:00</th>\n",
       "      <td>99.0891</td>\n",
       "      <td>46239</td>\n",
       "      <td>0.000496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-11 00:00:00+00:00</th>\n",
       "      <td>99.1247</td>\n",
       "      <td>61770</td>\n",
       "      <td>0.000359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           close_price  volume    Return\n",
       "begins_at                                               \n",
       "2016-02-05 00:00:00+00:00      99.2600   19417 -0.001408\n",
       "2016-02-08 00:00:00+00:00      99.1400   23218 -0.001209\n",
       "2016-02-09 00:00:00+00:00      99.0400   22708 -0.001009\n",
       "2016-02-10 00:00:00+00:00      99.0891   46239  0.000496\n",
       "2016-02-11 00:00:00+00:00      99.1247   61770  0.000359"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_safe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CPPI model:\n",
    "Input DataFrame of asset returns (risky_r), Series of a safe return and specify the drawdown level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_cppi(risky_r, safe_r=None, m=3, start=1000, floor=0.8, riskfree_rate=0.03, drawdown=None):\n",
    "    \"\"\"\n",
    "    Run a backtest of the CPPI strategy, given a set of returns for the risky asset\n",
    "    Returns a dictionary containing: Asset Value History, Risk Budget History, Risky Weight History\n",
    "    \"\"\"\n",
    "    # set up the CPPI parameters\n",
    "    dates = risky_r.index\n",
    "    n_steps = len(dates)\n",
    "    account_value = start\n",
    "    floor_value = start*floor\n",
    "    peak = account_value\n",
    "    if isinstance(risky_r, pd.Series): \n",
    "        risky_r = pd.DataFrame(risky_r, columns=[\"R\"])\n",
    "\n",
    "    if safe_r is None:\n",
    "        safe_r = pd.DataFrame().reindex_like(risky_r)\n",
    "        safe_r.values[:] = riskfree_rate/350 # fast way to set all values to a number\n",
    "    #else:\n",
    "        #o = safe_r\n",
    "        #safe_r = pd.DataFrame().reindex_like(risky_r)\n",
    "        #safe_r.loc[:,:] = o.values[:,None]\n",
    "    \n",
    "    # set up some DataFrames for saving intermediate values\n",
    "    account_history = pd.DataFrame().reindex_like(risky_r)\n",
    "    risky_w_history = pd.DataFrame().reindex_like(risky_r)\n",
    "    cushion_history = pd.DataFrame().reindex_like(risky_r)\n",
    "    floorval_history = pd.DataFrame().reindex_like(risky_r)\n",
    "    peak_history = pd.DataFrame().reindex_like(risky_r)\n",
    "\n",
    "    for step in range(n_steps):\n",
    "        if drawdown is not None:\n",
    "            peak = np.maximum(peak, account_value)\n",
    "            floor_value = peak*(1-drawdown)\n",
    "        cushion = (account_value - floor_value)/account_value\n",
    "        risky_w = m*cushion\n",
    "        risky_w = np.minimum(risky_w, 1)\n",
    "        risky_w = np.maximum(risky_w, 0)\n",
    "        safe_w = 1-risky_w\n",
    "        risky_alloc = account_value*risky_w\n",
    "        safe_alloc = account_value*safe_w\n",
    "        # recompute the new account value at the end of this step\n",
    "        account_value = risky_alloc*(1+risky_r.iloc[step]) + safe_alloc*(1+safe_r.iloc[step])\n",
    "       \n",
    "        # save the histories for analysis and plotting\n",
    "        cushion_history.iloc[step] = cushion\n",
    "        risky_w_history.iloc[step] = risky_w\n",
    "        account_history.iloc[step] = account_value\n",
    "        floorval_history.iloc[step] = floor_value\n",
    "        peak_history.iloc[step] = peak\n",
    "    risky_wealth = start*(1+risky_r).cumprod()\n",
    "    backtest_result = {\n",
    "        \"Wealth\": account_history,\n",
    "        \"Risky Wealth\": risky_wealth, \n",
    "        \"Risk Budget\": cushion_history,\n",
    "        \"Risky Allocation\": risky_w_history,\n",
    "        \"m\": m,\n",
    "        \"start\": start,\n",
    "        \"floor\": floor,\n",
    "        \"risky_r\":risky_r,\n",
    "        \"safe_r\": safe_r,\n",
    "        \"drawdown\": drawdown,\n",
    "        \"peak\": peak_history,\n",
    "        \"floor\": floorval_history\n",
    "    }\n",
    "    return backtest_result, safe_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def summary_stats(r, riskfree_rate=0.03):\n",
    "    \"\"\"\n",
    "    Return a DataFrame that contains aggregated summary stats for the returns in the columns of r\n",
    "    \"\"\"\n",
    "    ann_r = r.aggregate(annualize_rets, periods_per_year=253)\n",
    "    ann_vol = r.aggregate(annualize_vol, periods_per_year=253)\n",
    "    ann_sr = r.aggregate(sharpe_ratio, riskfree_rate=riskfree_rate, periods_per_year=253)\n",
    "    dd = r.aggregate(lambda r: drawdown(r).Drawdown.min())\n",
    "    skew = r.aggregate(skewness)\n",
    "    kurt = r.aggregate(kurtosis)\n",
    "    cf_var5 = r.aggregate(var_gaussian, modified=True)\n",
    "    hist_cvar5 = r.aggregate(cvar_historic)\n",
    "    return pd.DataFrame({\n",
    "        \"Annualized Return\": ann_r,\n",
    "        \"Annualized Vol\": ann_vol,\n",
    "        \"Skewness\": skew,\n",
    "        \"Kurtosis\": kurt,\n",
    "        \"Cornish-Fisher VaR (5%)\": cf_var5,\n",
    "        \"Historic CVaR (5%)\": hist_cvar5,\n",
    "        \"Sharpe Ratio\": ann_sr,\n",
    "        \"Max Drawdown\": dd\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def annualize_rets(r, periods_per_year):\n",
    "    \"\"\"\n",
    "    Annualizes a set of returns\n",
    "    We should infer the periods per year\n",
    "    but that is currently left as an exercise\n",
    "    to the reader :-)\n",
    "    \"\"\"\n",
    "    compounded_growth = (1+r).prod()\n",
    "    n_periods = r.shape[0]\n",
    "    return compounded_growth**(periods_per_year/n_periods)-1\n",
    "\n",
    "\n",
    "def annualize_vol(r, periods_per_year):\n",
    "    \"\"\"\n",
    "    Annualizes the vol of a set of returns\n",
    "    We should infer the periods per year\n",
    "    but that is currently left as an exercise\n",
    "    to the reader :-)\n",
    "    \"\"\"\n",
    "    return r.std()*(periods_per_year**0.5)\n",
    "\n",
    "def sharpe_ratio(r, riskfree_rate, periods_per_year):\n",
    "    \"\"\"\n",
    "    Computes the annualized sharpe ratio of a set of returns\n",
    "    \"\"\"\n",
    "    # convert the annual riskfree rate to per period\n",
    "    rf_per_period = (1+riskfree_rate)**(1/periods_per_year)-1\n",
    "    excess_ret = r - rf_per_period\n",
    "    ann_ex_ret = annualize_rets(excess_ret, periods_per_year)\n",
    "    ann_vol = annualize_vol(r, periods_per_year)\n",
    "    return ann_ex_ret/ann_vol\n",
    "\n",
    "def drawdown(return_series: pd.Series):\n",
    "    \"\"\"Takes a time series of asset returns.\n",
    "       returns a DataFrame with columns for\n",
    "       the wealth index, \n",
    "       the previous peaks, and \n",
    "       the percentage drawdown\n",
    "    \"\"\"\n",
    "    wealth_index = 1000*(1+return_series).cumprod()\n",
    "    previous_peaks = wealth_index.cummax()\n",
    "    drawdowns = (wealth_index - previous_peaks)/previous_peaks\n",
    "    return pd.DataFrame({\"Wealth\": wealth_index, \n",
    "                         \"Previous Peak\": previous_peaks, \n",
    "                         \"Drawdown\": drawdowns})\n",
    "\n",
    "def skewness(r):\n",
    "    \"\"\"\n",
    "    Alternative to scipy.stats.skew()\n",
    "    Computes the skewness of the supplied Series or DataFrame\n",
    "    Returns a float or a Series\n",
    "    \"\"\"\n",
    "    demeaned_r = r - r.mean()\n",
    "    # use the population standard deviation, so set dof=0\n",
    "    sigma_r = r.std(ddof=0)\n",
    "    exp = (demeaned_r**3).mean()\n",
    "    return exp/sigma_r**3\n",
    "\n",
    "def kurtosis(r):\n",
    "    \"\"\"\n",
    "    Alternative to scipy.stats.kurtosis()\n",
    "    Computes the kurtosis of the supplied Series or DataFrame\n",
    "    Returns a float or a Series\n",
    "    \"\"\"\n",
    "    demeaned_r = r - r.mean()\n",
    "    # use the population standard deviation, so set dof=0\n",
    "    sigma_r = r.std(ddof=0)\n",
    "    exp = (demeaned_r**4).mean()\n",
    "    return exp/sigma_r**4\n",
    "\n",
    "from scipy.stats import norm\n",
    "def var_gaussian(r, level=5, modified=False):\n",
    "    \"\"\"\n",
    "    Returns the Parametric Gauusian VaR of a Series or DataFrame\n",
    "    If \"modified\" is True, then the modified VaR is returned,\n",
    "    using the Cornish-Fisher modification\n",
    "    \"\"\"\n",
    "    # compute the Z score assuming it was Gaussian\n",
    "    z = norm.ppf(level/100)\n",
    "    if modified:\n",
    "        # modify the Z score based on observed skewness and kurtosis\n",
    "        s = skewness(r)\n",
    "        k = kurtosis(r)\n",
    "        z = (z +\n",
    "                (z**2 - 1)*s/6 +\n",
    "                (z**3 -3*z)*(k-3)/24 -\n",
    "                (2*z**3 - 5*z)*(s**2)/36\n",
    "            )\n",
    "    return -(r.mean() + z*r.std(ddof=0))\n",
    "\n",
    "def cvar_historic(r, level=5):\n",
    "    \"\"\"\n",
    "    Computes the Conditional VaR of Series or DataFrame\n",
    "    \"\"\"\n",
    "    if isinstance(r, pd.Series):\n",
    "        is_beyond = r <= var_historic(r, level=level)\n",
    "        return -r[is_beyond].mean()\n",
    "    elif isinstance(r, pd.DataFrame):\n",
    "        return r.aggregate(cvar_historic, level=level)\n",
    "    else:\n",
    "        raise TypeError(\"Expected r to be a Series or DataFrame\")\n",
    "        \n",
    "def var_historic(r, level=5):\n",
    "    \"\"\"\n",
    "    Returns the historic Value at Risk at a specified level\n",
    "    i.e. returns the number such that \"level\" percent of the returns\n",
    "    fall below that number, and the (100-level) percent are above\n",
    "    \"\"\"\n",
    "    if isinstance(r, pd.DataFrame):\n",
    "        return r.aggregate(var_historic, level=level)\n",
    "    elif isinstance(r, pd.Series):\n",
    "        return -np.percentile(r, level)\n",
    "    else:\n",
    "        raise TypeError(\"Expected r to be a Series or DataFrame\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Showing the results\n",
    "Looking at Transportation and Energy Minerals starting from 2020 <br>\n",
    "Plotting the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = price_sector[['Transportation', 'Energy Minerals']].pct_change().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = df_safe['Return']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f92a3dd9e20>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "btr = run_cppi(r['2020-01-01':], s['2020-01-01':], m=3, drawdown=0.2)\n",
    "ax = btr[0][\"Wealth\"].plot(figsize=(15,6))\n",
    "btr[0][\"Risky Wealth\"].plot(ax=ax, style=\"--\")\n",
    "btr[0]['floor'].plot(ax=ax, style=\"--\")\n",
    "ax.axvspan(35.5, 60.5, facecolor ='pink', alpha = 0.3)\n",
    "a = plt.axhline(1000*0.8, c='r', linewidth=5)\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "\n",
    "# manually define a new patch \n",
    "patch = mpatches.Patch( color='red', label='20% Floor')\n",
    "\n",
    "# handles is a list, so append manual patch\n",
    "handles.append(patch) \n",
    "plt.legend(handles=handles, loc='upper left', fontsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Allocations to the risky portfolios from the period of 03-01-2020 to 03-16-2020'}>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "btr[0]['Risk Budget']['2020-03-01':'2020-03-16'].plot(kind='bar', figsize=(15,5), title='Allocations to the risky portfolios from the period of 03-01-2020 to 03-16-2020')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transportation</th>\n",
       "      <th>Energy Minerals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>950.000000</td>\n",
       "      <td>950.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>952.622164</td>\n",
       "      <td>952.739433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>952.933110</td>\n",
       "      <td>953.001855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>952.933110</td>\n",
       "      <td>953.567880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>952.933110</td>\n",
       "      <td>953.567880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2021-01-20</th>\n",
       "      <td>1014.874044</td>\n",
       "      <td>953.567880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-21</th>\n",
       "      <td>1016.566061</td>\n",
       "      <td>953.567880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-22</th>\n",
       "      <td>1016.566061</td>\n",
       "      <td>953.567880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-25</th>\n",
       "      <td>1016.566061</td>\n",
       "      <td>953.567880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-26</th>\n",
       "      <td>1016.566061</td>\n",
       "      <td>953.567880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>269 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Transportation  Energy Minerals\n",
       "2020-01-02      950.000000       950.000000\n",
       "2020-01-03      952.622164       952.739433\n",
       "2020-01-06      952.933110       953.001855\n",
       "2020-01-07      952.933110       953.567880\n",
       "2020-01-08      952.933110       953.567880\n",
       "...                    ...              ...\n",
       "2021-01-20     1014.874044       953.567880\n",
       "2021-01-21     1016.566061       953.567880\n",
       "2021-01-22     1016.566061       953.567880\n",
       "2021-01-25     1016.566061       953.567880\n",
       "2021-01-26     1016.566061       953.567880\n",
       "\n",
       "[269 rows x 2 columns]"
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     "execution_count": 176,
     "metadata": {},
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   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-03-18   -0.20509\n",
       "Name: Energy Minerals, dtype: float64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(btr[0]['Wealth']['Energy Minerals'].nsmallest(1))/1000 - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-03-23   -0.582278\n",
       "Name: Energy Minerals, dtype: float64"
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     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "(btr[0]['Risky Wealth']['Energy Minerals'].nsmallest(1))/1000 - 1"
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  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37718799999999997"
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     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "0.582278-0.20509"
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   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Wealth':             Transportation  Energy Minerals\n",
       " 2020-01-02     1006.423474      1006.917237\n",
       " 2020-01-03     1003.397103      1003.684089\n",
       " 2020-01-06     1000.007748      1007.209767\n",
       " 2020-01-07      999.778236      1004.067796\n",
       " 2020-01-08     1004.656849       993.609037\n",
       " ...                    ...              ...\n",
       " 2021-01-20     1128.651704       870.768938\n",
       " 2021-01-21     1116.398309       864.769293\n",
       " 2021-01-22     1112.191094       863.417525\n",
       " 2021-01-25     1114.982306       862.097257\n",
       " 2021-01-26     1103.330681       858.477429\n",
       " \n",
       " [269 rows x 2 columns],\n",
       " 'Risky Wealth':             Transportation  Energy Minerals\n",
       " 2020-01-02     1009.679742      1010.502680\n",
       " 2020-01-03     1003.651817      1004.126470\n",
       " 2020-01-06      998.205510      1010.355667\n",
       " 2020-01-07      998.586346      1005.836503\n",
       " 2020-01-08     1007.564097       988.770507\n",
       " ...                    ...              ...\n",
       " 2021-01-20     1308.049224       750.591473\n",
       " 2021-01-21     1283.277892       725.268175\n",
       " 2021-01-22     1275.568615       722.143258\n",
       " 2021-01-25     1281.033998       715.806643\n",
       " 2021-01-26     1257.129219       700.194066\n",
       " \n",
       " [269 rows x 2 columns],\n",
       " 'Risk Budget':             Transportation  Energy Minerals\n",
       " 2020-01-02        0.200000         0.200000\n",
       " 2020-01-03        0.200000         0.200000\n",
       " 2020-01-06        0.197587         0.197423\n",
       " 2020-01-07        0.194867         0.200000\n",
       " 2020-01-08        0.194683         0.197497\n",
       " ...                    ...              ...\n",
       " 2021-01-20        0.190137         0.072603\n",
       " 2021-01-21        0.197256         0.074648\n",
       " 2021-01-22        0.188445         0.068228\n",
       " 2021-01-25        0.185375         0.066769\n",
       " 2021-01-26        0.187414         0.065340\n",
       " \n",
       " [269 rows x 2 columns],\n",
       " 'Risky Allocation':             Transportation  Energy Minerals\n",
       " 2020-01-02        0.600000         0.600000\n",
       " 2020-01-03        0.600000         0.600000\n",
       " 2020-01-06        0.592761         0.592269\n",
       " 2020-01-07        0.584602         0.600000\n",
       " 2020-01-08        0.584048         0.592490\n",
       " ...                    ...              ...\n",
       " 2021-01-20        0.570412         0.217810\n",
       " 2021-01-21        0.591767         0.223944\n",
       " 2021-01-22        0.565334         0.204684\n",
       " 2021-01-25        0.556124         0.200308\n",
       " 2021-01-26        0.562242         0.196020\n",
       " \n",
       " [269 rows x 2 columns],\n",
       " 'm': 3,\n",
       " 'start': 1000,\n",
       " 'floor':             Transportation  Energy Minerals\n",
       " 2020-01-02      800.000000       800.000000\n",
       " 2020-01-03      805.138779       805.533790\n",
       " 2020-01-06      805.138779       805.533790\n",
       " 2020-01-07      805.138779       805.767814\n",
       " 2020-01-08      805.138779       805.767814\n",
       " ...                    ...              ...\n",
       " 2021-01-20      906.018914       805.767814\n",
       " 2021-01-21      906.018914       805.767814\n",
       " 2021-01-22      906.018914       805.767814\n",
       " 2021-01-25      906.018914       805.767814\n",
       " 2021-01-26      906.018914       805.767814\n",
       " \n",
       " [269 rows x 2 columns],\n",
       " 'risky_r':             Transportation  Energy Minerals\n",
       " 2020-01-02        0.009680         0.010503\n",
       " 2020-01-03       -0.005970        -0.006310\n",
       " 2020-01-06       -0.005426         0.006204\n",
       " 2020-01-07        0.000382        -0.004473\n",
       " 2020-01-08        0.008990        -0.016967\n",
       " ...                    ...              ...\n",
       " 2021-01-20        0.014755         0.006378\n",
       " 2021-01-21       -0.018938        -0.033738\n",
       " 2021-01-22       -0.006007        -0.004309\n",
       " 2021-01-25        0.004285        -0.008775\n",
       " 2021-01-26       -0.018661        -0.021811\n",
       " \n",
       " [269 rows x 2 columns],\n",
       " 'safe_r': begins_at\n",
       " 2020-01-02 00:00:00+00:00    0.001539\n",
       " 2020-01-03 00:00:00+00:00    0.001438\n",
       " 2020-01-06 00:00:00+00:00   -0.000396\n",
       " 2020-01-07 00:00:00+00:00   -0.001089\n",
       " 2020-01-08 00:00:00+00:00   -0.000892\n",
       "                                ...   \n",
       " 2021-01-20 00:00:00+00:00    0.001049\n",
       " 2021-01-21 00:00:00+00:00    0.000857\n",
       " 2021-01-22 00:00:00+00:00   -0.000857\n",
       " 2021-01-25 00:00:00+00:00    0.000286\n",
       " 2021-01-26 00:00:00+00:00    0.000095\n",
       " Name: Return, Length: 269, dtype: float64,\n",
       " 'drawdown': 0.2,\n",
       " 'peak':             Transportation  Energy Minerals\n",
       " 2020-01-02     1000.000000      1000.000000\n",
       " 2020-01-03     1006.423474      1006.917237\n",
       " 2020-01-06     1006.423474      1006.917237\n",
       " 2020-01-07     1006.423474      1007.209767\n",
       " 2020-01-08     1006.423474      1007.209767\n",
       " ...                    ...              ...\n",
       " 2021-01-20     1132.523643      1007.209767\n",
       " 2021-01-21     1132.523643      1007.209767\n",
       " 2021-01-22     1132.523643      1007.209767\n",
       " 2021-01-25     1132.523643      1007.209767\n",
       " 2021-01-26     1132.523643      1007.209767\n",
       " \n",
       " [269 rows x 2 columns]}"
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     "execution_count": 181,
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   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Annualized Return</th>\n",
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       "      <td>0.134835</td>\n",
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       "      <td>-0.175079</td>\n",
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       "      <th>Energy Minerals</th>\n",
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       "      <td>0.103602</td>\n",
       "      <td>-1.541758</td>\n",
       "      <td>-0.210990</td>\n",
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       "                 Annualized Return  Annualized Vol  Sharpe Ratio  Max Drawdown\n",
       "Transportation            0.087463        0.134835      0.413803     -0.175079\n",
       "Energy Minerals          -0.134504        0.103602     -1.541758     -0.210990"
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     "execution_count": 21,
     "metadata": {},
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   "source": [
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   "execution_count": 22,
   "metadata": {},
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       "      <th>Transportation</th>\n",
       "      <td>0.224433</td>\n",
       "      <td>0.362082</td>\n",
       "      <td>0.521387</td>\n",
       "      <td>-0.348918</td>\n",
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       "    <tr>\n",
       "      <th>Energy Minerals</th>\n",
       "      <td>-0.275422</td>\n",
       "      <td>0.579931</td>\n",
       "      <td>-0.511387</td>\n",
       "      <td>-0.587312</td>\n",
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       "                 Annualized Return  Annualized Vol  Sharpe Ratio  Max Drawdown\n",
       "Transportation            0.224433        0.362082      0.521387     -0.348918\n",
       "Energy Minerals          -0.275422        0.579931     -0.511387     -0.587312"
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     "execution_count": 22,
     "metadata": {},
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   "source": [
    "summary_stats(btr[0][\"Risky Wealth\"].pct_change().dropna())[[\"Annualized Return\", \"Annualized Vol\", \"Sharpe Ratio\", \"Max Drawdown\"]]"
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   "cell_type": "code",
   "execution_count": null,
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