MPT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4abaf1d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: yfinance in ./opt/anaconda3/lib/python3.8/site-packages (0.1.85)\n",
      "Requirement already satisfied: lxml>=4.5.1 in ./opt/anaconda3/lib/python3.8/site-packages (from yfinance) (4.6.3)\n",
      "Requirement already satisfied: appdirs>=1.4.4 in ./opt/anaconda3/lib/python3.8/site-packages (from yfinance) (1.4.4)\n",
      "Requirement already satisfied: multitasking>=0.0.7 in ./opt/anaconda3/lib/python3.8/site-packages (from yfinance) (0.0.11)\n",
      "Requirement already satisfied: pandas>=0.24.0 in ./opt/anaconda3/lib/python3.8/site-packages (from yfinance) (1.2.4)\n",
      "Requirement already satisfied: numpy>=1.15 in ./opt/anaconda3/lib/python3.8/site-packages (from yfinance) (1.19.5)\n",
      "Requirement already satisfied: requests>=2.26 in ./opt/anaconda3/lib/python3.8/site-packages (from yfinance) (2.28.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in ./opt/anaconda3/lib/python3.8/site-packages (from pandas>=0.24.0->yfinance) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.3 in ./opt/anaconda3/lib/python3.8/site-packages (from pandas>=0.24.0->yfinance) (2021.1)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in ./opt/anaconda3/lib/python3.8/site-packages (from requests>=2.26->yfinance) (1.26.4)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in ./opt/anaconda3/lib/python3.8/site-packages (from requests>=2.26->yfinance) (2.1.1)\n",
      "Requirement already satisfied: idna<4,>=2.5 in ./opt/anaconda3/lib/python3.8/site-packages (from requests>=2.26->yfinance) (2.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in ./opt/anaconda3/lib/python3.8/site-packages (from requests>=2.26->yfinance) (2020.12.5)\n",
      "Requirement already satisfied: six>=1.5 in ./opt/anaconda3/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas>=0.24.0->yfinance) (1.15.0)\n",
      "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.3.1 is available.\n",
      "You should consider upgrading via the '/Users/saiyer33/opt/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install yfinance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4ccc64fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "21343ed3",
   "metadata": {},
   "outputs": [],
   "source": [
    "sheet_id = \"1P9p4f0TTpNg-wpwSySDY238Za-lpvukTFbiO9ozhYPA\"\n",
    "sheet_name = \"Holdings\"\n",
    "url = f\"https://docs.google.com/spreadsheets/d/1P9p4f0TTpNg-wpwSySDY238Za-lpvukTFbiO9ozhYPA/gviz/tq?tqx=out:csv&sheet=Holdings\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "348156cc",
   "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>Unnamed: 0</th>\n",
       "      <th>Holdings</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Avg Purchase Price</th>\n",
       "      <th>Market Value</th>\n",
       "      <th>Cost Basis</th>\n",
       "      <th>P/L</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AZRE</td>\n",
       "      <td>14.0</td>\n",
       "      <td>$27.54</td>\n",
       "      <td>$83.44</td>\n",
       "      <td>$385.56</td>\n",
       "      <td>-$302.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CRSR</td>\n",
       "      <td>34.0</td>\n",
       "      <td>$29.32</td>\n",
       "      <td>$593.64</td>\n",
       "      <td>$996.88</td>\n",
       "      <td>-$403.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>BATRA</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$29.86</td>\n",
       "      <td>$577.26</td>\n",
       "      <td>$537.48</td>\n",
       "      <td>$39.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>LOVE</td>\n",
       "      <td>10.0</td>\n",
       "      <td>$67.61</td>\n",
       "      <td>$261.40</td>\n",
       "      <td>$676.14</td>\n",
       "      <td>-$414.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>PD</td>\n",
       "      <td>20.0</td>\n",
       "      <td>$46.45</td>\n",
       "      <td>$456.00</td>\n",
       "      <td>$929.00</td>\n",
       "      <td>-$473.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>PSQ</td>\n",
       "      <td>2.0</td>\n",
       "      <td>$10.97</td>\n",
       "      <td>$27.86</td>\n",
       "      <td>$21.93</td>\n",
       "      <td>$5.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>SARK</td>\n",
       "      <td>3.0</td>\n",
       "      <td>$36.25</td>\n",
       "      <td>$177.27</td>\n",
       "      <td>$108.75</td>\n",
       "      <td>$68.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>SH</td>\n",
       "      <td>2.0</td>\n",
       "      <td>$13.93</td>\n",
       "      <td>$31.12</td>\n",
       "      <td>$27.86</td>\n",
       "      <td>$3.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>SQQQ</td>\n",
       "      <td>3.0</td>\n",
       "      <td>$33.55</td>\n",
       "      <td>$141.24</td>\n",
       "      <td>$100.65</td>\n",
       "      <td>$40.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>SPXU</td>\n",
       "      <td>1.0</td>\n",
       "      <td>$13.20</td>\n",
       "      <td>$15.71</td>\n",
       "      <td>$13.20</td>\n",
       "      <td>$2.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>PLTR</td>\n",
       "      <td>48.0</td>\n",
       "      <td>$10.69</td>\n",
       "      <td>$366.72</td>\n",
       "      <td>$513.12</td>\n",
       "      <td>-$146.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>MTNOY</td>\n",
       "      <td>20.0</td>\n",
       "      <td>$13.27</td>\n",
       "      <td>$158.00</td>\n",
       "      <td>$265.40</td>\n",
       "      <td>-$107.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>ALK</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$56.53</td>\n",
       "      <td>$187.76</td>\n",
       "      <td>$226.12</td>\n",
       "      <td>-$38.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>INMD</td>\n",
       "      <td>12.0</td>\n",
       "      <td>$25.00</td>\n",
       "      <td>$414.00</td>\n",
       "      <td>$300.00</td>\n",
       "      <td>$114.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.98115</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>USD/EUR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0 Holdings  Quantity Avg Purchase Price  Market Value Cost Basis  \\\n",
       "0          NaN     AZRE      14.0              $27.54       $83.44    $385.56   \n",
       "1          NaN     CRSR      34.0              $29.32      $593.64    $996.88   \n",
       "2          NaN    BATRA      18.0              $29.86      $577.26    $537.48   \n",
       "3          NaN     LOVE      10.0              $67.61      $261.40    $676.14   \n",
       "4          NaN       PD      20.0              $46.45      $456.00    $929.00   \n",
       "5          NaN      PSQ       2.0              $10.97       $27.86     $21.93   \n",
       "6          NaN     SARK       3.0              $36.25      $177.27    $108.75   \n",
       "7          NaN       SH       2.0              $13.93       $31.12     $27.86   \n",
       "8          NaN     SQQQ       3.0              $33.55      $141.24    $100.65   \n",
       "9          NaN     SPXU       1.0              $13.20       $15.71     $13.20   \n",
       "10         NaN     PLTR      48.0              $10.69      $366.72    $513.12   \n",
       "11         NaN    MTNOY      20.0              $13.27      $158.00    $265.40   \n",
       "12         NaN      ALK       4.0              $56.53      $187.76    $226.12   \n",
       "13         NaN     INMD      12.0              $25.00      $414.00    $300.00   \n",
       "15         NaN  0.98115       NaN                 NaN          NaN        NaN   \n",
       "16         NaN  USD/EUR       NaN                 NaN          NaN        NaN   \n",
       "\n",
       "         P/L  Unnamed: 7  Unnamed: 8  Unnamed: 9  Unnamed: 10  \n",
       "0   -$302.12         NaN         NaN         NaN          NaN  \n",
       "1   -$403.24         NaN         NaN         NaN          NaN  \n",
       "2     $39.78         NaN         NaN         NaN          NaN  \n",
       "3   -$414.74         NaN         NaN         NaN          NaN  \n",
       "4   -$473.00         NaN         NaN         NaN          NaN  \n",
       "5      $5.93         NaN         NaN         NaN          NaN  \n",
       "6     $68.52         NaN         NaN         NaN          NaN  \n",
       "7      $3.26         NaN         NaN         NaN          NaN  \n",
       "8     $40.59         NaN         NaN         NaN          NaN  \n",
       "9      $2.51         NaN         NaN         NaN          NaN  \n",
       "10  -$146.40         NaN         NaN         NaN          NaN  \n",
       "11  -$107.40         NaN         NaN         NaN          NaN  \n",
       "12   -$38.36         NaN         NaN         NaN          NaN  \n",
       "13   $114.00         NaN         NaN         NaN          NaN  \n",
       "15       NaN         NaN         NaN         NaN          NaN  \n",
       "16       NaN         NaN         NaN         NaN          NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(url)\n",
    "df = df.drop(labels=14, axis=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0ac75406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 14 entries, 0 to 13\n",
      "Data columns (total 6 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   Holdings            14 non-null     object \n",
      " 1   Quantity            14 non-null     float64\n",
      " 2   Avg Purchase Price  14 non-null     object \n",
      " 3   Market Value        14 non-null     object \n",
      " 4   Cost Basis          14 non-null     object \n",
      " 5   P/L                 14 non-null     object \n",
      "dtypes: float64(1), object(5)\n",
      "memory usage: 784.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df = df.drop(columns=['Unnamed: 0', 'Unnamed: 7', 'Unnamed: 8','Unnamed: 9','Unnamed: 10'])\n",
    "df = df.rename(columns={'Holdings ': 'Ticker'})\n",
    "df = df.rename(columns={'Avg Purchase Price ': 'Avg Purchase Price'})\n",
    "df = df.drop([15,16])\n",
    "df.rename(columns = {'old_col1':'new_col1', 'old_col2':'new_col2'}, inplace = True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6c37f370",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Given array of equities, returning dataframe with historical data dating back \n",
    "def get_closing_price_historical_data(equities, period, interval):\n",
    "    data = {}\n",
    "    for x in equities:\n",
    "        ticker = yf.Ticker(x)\n",
    "        history = ticker.history(period= period, interval= interval)['Close']\n",
    "        data[x] = history\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0ec596b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Holdings', 'Quantity', 'Avg Purchase Price', 'Market Value', 'Cost Basis', 'P/L']\n"
     ]
    }
   ],
   "source": [
    "print(df.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6058b8b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Market Value'] = df.apply(lambda x: x['Market Value'].replace('$', ''), axis = 1)\n",
    "df['Market Value'] = df.apply(lambda x: x['Market Value'].replace('€', ''), axis = 1)\n",
    "df['Cost Basis'] = df.apply(lambda x: x['Cost Basis'].replace('$', ''), axis = 1)\n",
    "df['Cost Basis'] = df.apply(lambda x: x['Cost Basis'].replace('€', ''), axis = 1)\n",
    "df['Avg Purchase Price'] = df.apply(lambda x: x['Avg Purchase Price'].replace('$', ''), axis = 1)\n",
    "df['Avg Purchase Price'] = df.apply(lambda x: x['Avg Purchase Price'].replace('€', ''), axis = 1) \n",
    "df['P/L'] = df.apply(lambda x: x['P/L'].replace('$', ''), axis = 1)\n",
    "df['P/L'] = df.apply(lambda x: x['P/L'].replace('€', ''), axis = 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e398f14",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\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>Holdings</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Avg Purchase Price</th>\n",
       "      <th>Market Value</th>\n",
       "      <th>Cost Basis</th>\n",
       "      <th>P/L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AZRE</td>\n",
       "      <td>14.0</td>\n",
       "      <td>27.54</td>\n",
       "      <td>83.44</td>\n",
       "      <td>385.56</td>\n",
       "      <td>-302.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CRSR</td>\n",
       "      <td>34.0</td>\n",
       "      <td>29.32</td>\n",
       "      <td>593.64</td>\n",
       "      <td>996.88</td>\n",
       "      <td>-403.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BATRA</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.86</td>\n",
       "      <td>577.26</td>\n",
       "      <td>537.48</td>\n",
       "      <td>39.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LOVE</td>\n",
       "      <td>10.0</td>\n",
       "      <td>67.61</td>\n",
       "      <td>261.40</td>\n",
       "      <td>676.14</td>\n",
       "      <td>-414.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PD</td>\n",
       "      <td>20.0</td>\n",
       "      <td>46.45</td>\n",
       "      <td>456.00</td>\n",
       "      <td>929.00</td>\n",
       "      <td>-473.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PSQ</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.97</td>\n",
       "      <td>27.86</td>\n",
       "      <td>21.93</td>\n",
       "      <td>5.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SARK</td>\n",
       "      <td>3.0</td>\n",
       "      <td>36.25</td>\n",
       "      <td>177.27</td>\n",
       "      <td>108.75</td>\n",
       "      <td>68.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SH</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.93</td>\n",
       "      <td>31.12</td>\n",
       "      <td>27.86</td>\n",
       "      <td>3.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SQQQ</td>\n",
       "      <td>3.0</td>\n",
       "      <td>33.55</td>\n",
       "      <td>141.24</td>\n",
       "      <td>100.65</td>\n",
       "      <td>40.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SPXU</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.20</td>\n",
       "      <td>15.71</td>\n",
       "      <td>13.20</td>\n",
       "      <td>2.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PLTR</td>\n",
       "      <td>48.0</td>\n",
       "      <td>10.69</td>\n",
       "      <td>366.72</td>\n",
       "      <td>513.12</td>\n",
       "      <td>-146.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MTNOY</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.27</td>\n",
       "      <td>158.00</td>\n",
       "      <td>265.40</td>\n",
       "      <td>-107.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ALK</td>\n",
       "      <td>4.0</td>\n",
       "      <td>56.53</td>\n",
       "      <td>187.76</td>\n",
       "      <td>226.12</td>\n",
       "      <td>-38.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>INMD</td>\n",
       "      <td>12.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>414.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>114.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Holdings  Quantity Avg Purchase Price Market Value Cost Basis      P/L\n",
       "0      AZRE      14.0              27.54        83.44     385.56  -302.12\n",
       "1      CRSR      34.0              29.32       593.64     996.88  -403.24\n",
       "2     BATRA      18.0              29.86       577.26     537.48    39.78\n",
       "3      LOVE      10.0              67.61       261.40     676.14  -414.74\n",
       "4        PD      20.0              46.45       456.00     929.00  -473.00\n",
       "5       PSQ       2.0              10.97        27.86      21.93     5.93\n",
       "6      SARK       3.0              36.25       177.27     108.75    68.52\n",
       "7        SH       2.0              13.93        31.12      27.86     3.26\n",
       "8      SQQQ       3.0              33.55       141.24     100.65    40.59\n",
       "9      SPXU       1.0              13.20        15.71      13.20     2.51\n",
       "10     PLTR      48.0              10.69       366.72     513.12  -146.40\n",
       "11    MTNOY      20.0              13.27       158.00     265.40  -107.40\n",
       "12      ALK       4.0              56.53       187.76     226.12   -38.36\n",
       "13     INMD      12.0              25.00       414.00     300.00   114.00"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3aa6493e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\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>Holdings</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Avg Purchase Price</th>\n",
       "      <th>Market Value</th>\n",
       "      <th>Cost Basis</th>\n",
       "      <th>P/L</th>\n",
       "      <th>Currency Denomination</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AZRE</td>\n",
       "      <td>14.0</td>\n",
       "      <td>27.54</td>\n",
       "      <td>83.44</td>\n",
       "      <td>385.56</td>\n",
       "      <td>-302.12</td>\n",
       "      <td>Dollars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CRSR</td>\n",
       "      <td>34.0</td>\n",
       "      <td>29.32</td>\n",
       "      <td>593.64</td>\n",
       "      <td>996.88</td>\n",
       "      <td>-403.24</td>\n",
       "      <td>Dollars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BATRA</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.86</td>\n",
       "      <td>577.26</td>\n",
       "      <td>537.48</td>\n",
       "      <td>39.78</td>\n",
       "      <td>Dollars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LOVE</td>\n",
       "      <td>10.0</td>\n",
       "      <td>67.61</td>\n",
       "      <td>261.40</td>\n",
       "      <td>676.14</td>\n",
       "      <td>-414.74</td>\n",
       "      <td>Dollars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PD</td>\n",
       "      <td>20.0</td>\n",
       "      <td>46.45</td>\n",
       "      <td>456.00</td>\n",
       "      <td>929.00</td>\n",
       "      <td>-473.00</td>\n",
       "      <td>Dollars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PSQ</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.97</td>\n",
       "      <td>27.86</td>\n",
       "      <td>21.93</td>\n",
       "      <td>5.93</td>\n",
       "      <td>Dollars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SARK</td>\n",
       "      <td>3.0</td>\n",
       "      <td>36.25</td>\n",
       "      <td>177.27</td>\n",
       "      <td>108.75</td>\n",
       "      <td>68.52</td>\n",
       "      <td>Dollars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SH</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.93</td>\n",
       "      <td>31.12</td>\n",
       "      <td>27.86</td>\n",
       "      <td>3.26</td>\n",
       "      <td>Dollars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SQQQ</td>\n",
       "      <td>3.0</td>\n",
       "      <td>33.55</td>\n",
       "      <td>141.24</td>\n",
       "      <td>100.65</td>\n",
       "      <td>40.59</td>\n",
       "      <td>Dollars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SPXU</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.20</td>\n",
       "      <td>15.71</td>\n",
       "      <td>13.20</td>\n",
       "      <td>2.51</td>\n",
       "      <td>Dollars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PLTR</td>\n",
       "      <td>48.0</td>\n",
       "      <td>10.69</td>\n",
       "      <td>366.72</td>\n",
       "      <td>513.12</td>\n",
       "      <td>-146.40</td>\n",
       "      <td>Dollars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MTNOY</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.27</td>\n",
       "      <td>158.00</td>\n",
       "      <td>265.40</td>\n",
       "      <td>-107.40</td>\n",
       "      <td>Dollars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ALK</td>\n",
       "      <td>4.0</td>\n",
       "      <td>56.53</td>\n",
       "      <td>187.76</td>\n",
       "      <td>226.12</td>\n",
       "      <td>-38.36</td>\n",
       "      <td>Dollars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>INMD</td>\n",
       "      <td>12.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>414.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>114.00</td>\n",
       "      <td>Dollars</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Holdings  Quantity Avg Purchase Price Market Value Cost Basis      P/L  \\\n",
       "0      AZRE      14.0              27.54        83.44     385.56  -302.12   \n",
       "1      CRSR      34.0              29.32       593.64     996.88  -403.24   \n",
       "2     BATRA      18.0              29.86       577.26     537.48    39.78   \n",
       "3      LOVE      10.0              67.61       261.40     676.14  -414.74   \n",
       "4        PD      20.0              46.45       456.00     929.00  -473.00   \n",
       "5       PSQ       2.0              10.97        27.86      21.93     5.93   \n",
       "6      SARK       3.0              36.25       177.27     108.75    68.52   \n",
       "7        SH       2.0              13.93        31.12      27.86     3.26   \n",
       "8      SQQQ       3.0              33.55       141.24     100.65    40.59   \n",
       "9      SPXU       1.0              13.20        15.71      13.20     2.51   \n",
       "10     PLTR      48.0              10.69       366.72     513.12  -146.40   \n",
       "11    MTNOY      20.0              13.27       158.00     265.40  -107.40   \n",
       "12      ALK       4.0              56.53       187.76     226.12   -38.36   \n",
       "13     INMD      12.0              25.00       414.00     300.00   114.00   \n",
       "\n",
       "   Currency Denomination  \n",
       "0                Dollars  \n",
       "1                Dollars  \n",
       "2                Dollars  \n",
       "3                Dollars  \n",
       "4                Dollars  \n",
       "5                Dollars  \n",
       "6                Dollars  \n",
       "7                Dollars  \n",
       "8                Dollars  \n",
       "9                Dollars  \n",
       "10               Dollars  \n",
       "11               Dollars  \n",
       "12               Dollars  \n",
       "13               Dollars  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('mode.chained_assignment', None)\n",
    "df['Currency Denomination'] = \"Dollars\"\n",
    "df['Currency Denomination'][14] = \"Euros\"\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5cc8fa7f",
   "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>Holdings</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Avg Purchase Price</th>\n",
       "      <th>Market Value</th>\n",
       "      <th>Cost Basis</th>\n",
       "      <th>P/L</th>\n",
       "      <th>Currency Denomination</th>\n",
       "      <th>Exposure</th>\n",
       "      <th>Allocation %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AZRE</td>\n",
       "      <td>14.0</td>\n",
       "      <td>27.54</td>\n",
       "      <td>83.44</td>\n",
       "      <td>385.56</td>\n",
       "      <td>-302.12</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>385.56</td>\n",
       "      <td>7.556948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CRSR</td>\n",
       "      <td>34.0</td>\n",
       "      <td>29.32</td>\n",
       "      <td>593.64</td>\n",
       "      <td>996.88</td>\n",
       "      <td>-403.24</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>996.88</td>\n",
       "      <td>19.538775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BATRA</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.86</td>\n",
       "      <td>577.26</td>\n",
       "      <td>537.48</td>\n",
       "      <td>39.78</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>537.48</td>\n",
       "      <td>10.534568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LOVE</td>\n",
       "      <td>10.0</td>\n",
       "      <td>67.61</td>\n",
       "      <td>261.40</td>\n",
       "      <td>676.14</td>\n",
       "      <td>-414.74</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>676.10</td>\n",
       "      <td>13.251510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PD</td>\n",
       "      <td>20.0</td>\n",
       "      <td>46.45</td>\n",
       "      <td>456.00</td>\n",
       "      <td>929.00</td>\n",
       "      <td>-473.00</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>929.00</td>\n",
       "      <td>18.208332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PSQ</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.97</td>\n",
       "      <td>27.86</td>\n",
       "      <td>21.93</td>\n",
       "      <td>5.93</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>21.94</td>\n",
       "      <td>0.430022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SARK</td>\n",
       "      <td>3.0</td>\n",
       "      <td>36.25</td>\n",
       "      <td>177.27</td>\n",
       "      <td>108.75</td>\n",
       "      <td>68.52</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>108.75</td>\n",
       "      <td>2.131492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SH</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.93</td>\n",
       "      <td>31.12</td>\n",
       "      <td>27.86</td>\n",
       "      <td>3.26</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>27.86</td>\n",
       "      <td>0.546054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SQQQ</td>\n",
       "      <td>3.0</td>\n",
       "      <td>33.55</td>\n",
       "      <td>141.24</td>\n",
       "      <td>100.65</td>\n",
       "      <td>40.59</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>100.65</td>\n",
       "      <td>1.972733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SPXU</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.20</td>\n",
       "      <td>15.71</td>\n",
       "      <td>13.20</td>\n",
       "      <td>2.51</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>13.20</td>\n",
       "      <td>0.258719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PLTR</td>\n",
       "      <td>48.0</td>\n",
       "      <td>10.69</td>\n",
       "      <td>366.72</td>\n",
       "      <td>513.12</td>\n",
       "      <td>-146.40</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>513.12</td>\n",
       "      <td>10.057114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MTNOY</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.27</td>\n",
       "      <td>158.00</td>\n",
       "      <td>265.40</td>\n",
       "      <td>-107.40</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>265.40</td>\n",
       "      <td>5.201820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ALK</td>\n",
       "      <td>4.0</td>\n",
       "      <td>56.53</td>\n",
       "      <td>187.76</td>\n",
       "      <td>226.12</td>\n",
       "      <td>-38.36</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>226.12</td>\n",
       "      <td>4.431935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>INMD</td>\n",
       "      <td>12.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>414.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>114.00</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>300.00</td>\n",
       "      <td>5.879978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Holdings  Quantity  Avg Purchase Price Market Value Cost Basis      P/L  \\\n",
       "0      AZRE      14.0               27.54        83.44     385.56  -302.12   \n",
       "1      CRSR      34.0               29.32       593.64     996.88  -403.24   \n",
       "2     BATRA      18.0               29.86       577.26     537.48    39.78   \n",
       "3      LOVE      10.0               67.61       261.40     676.14  -414.74   \n",
       "4        PD      20.0               46.45       456.00     929.00  -473.00   \n",
       "5       PSQ       2.0               10.97        27.86      21.93     5.93   \n",
       "6      SARK       3.0               36.25       177.27     108.75    68.52   \n",
       "7        SH       2.0               13.93        31.12      27.86     3.26   \n",
       "8      SQQQ       3.0               33.55       141.24     100.65    40.59   \n",
       "9      SPXU       1.0               13.20        15.71      13.20     2.51   \n",
       "10     PLTR      48.0               10.69       366.72     513.12  -146.40   \n",
       "11    MTNOY      20.0               13.27       158.00     265.40  -107.40   \n",
       "12      ALK       4.0               56.53       187.76     226.12   -38.36   \n",
       "13     INMD      12.0               25.00       414.00     300.00   114.00   \n",
       "\n",
       "   Currency Denomination  Exposure  Allocation %  \n",
       "0                Dollars    385.56      7.556948  \n",
       "1                Dollars    996.88     19.538775  \n",
       "2                Dollars    537.48     10.534568  \n",
       "3                Dollars    676.10     13.251510  \n",
       "4                Dollars    929.00     18.208332  \n",
       "5                Dollars     21.94      0.430022  \n",
       "6                Dollars    108.75      2.131492  \n",
       "7                Dollars     27.86      0.546054  \n",
       "8                Dollars    100.65      1.972733  \n",
       "9                Dollars     13.20      0.258719  \n",
       "10               Dollars    513.12     10.057114  \n",
       "11               Dollars    265.40      5.201820  \n",
       "12               Dollars    226.12      4.431935  \n",
       "13               Dollars    300.00      5.879978  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Quantity'] = df['Quantity'].astype(float)\n",
    "df['Avg Purchase Price'] = df['Avg Purchase Price'].astype(float)\n",
    "df['Exposure'] = df['Quantity'] * df['Avg Purchase Price']\n",
    "sum = df['Exposure'].sum()\n",
    "df['Allocation %'] = (df['Exposure']/sum)* 100\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "31586ff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_data = get_closing_price_historical_data(df['Holdings'], '179d', '1d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d080bf50",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>AZRE</th>\n",
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       "      <th>Date</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>2022-02-28 00:00:00-05:00</th>\n",
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       "      <td>42.689999</td>\n",
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       "    <tr>\n",
       "      <th>2022-03-01 00:00:00-05:00</th>\n",
       "      <td>18.080000</td>\n",
       "      <td>22.580000</td>\n",
       "      <td>25.629999</td>\n",
       "      <td>41.099998</td>\n",
       "      <td>33.009998</td>\n",
       "      <td>12.41</td>\n",
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       "      <td>14.920000</td>\n",
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       "      <td>12.449298</td>\n",
       "      <td>52.340000</td>\n",
       "      <td>41.930000</td>\n",
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       "    <tr>\n",
       "      <th>2022-03-02 00:00:00-05:00</th>\n",
       "      <td>17.180000</td>\n",
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       "      <td>25.850000</td>\n",
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       "      <td>43.009998</td>\n",
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       "    <tr>\n",
       "      <th>2022-03-03 00:00:00-05:00</th>\n",
       "      <td>17.170000</td>\n",
       "      <td>23.350000</td>\n",
       "      <td>25.870001</td>\n",
       "      <td>41.900002</td>\n",
       "      <td>31.440001</td>\n",
       "      <td>12.38</td>\n",
       "      <td>50.959999</td>\n",
       "      <td>14.710000</td>\n",
       "      <td>42.270000</td>\n",
       "      <td>15.420000</td>\n",
       "      <td>11.34</td>\n",
       "      <td>13.178134</td>\n",
       "      <td>51.419998</td>\n",
       "      <td>41.110001</td>\n",
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       "    <tr>\n",
       "      <th>2022-03-04 00:00:00-05:00</th>\n",
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       "      <td>21.730000</td>\n",
       "      <td>25.540001</td>\n",
       "      <td>40.619999</td>\n",
       "      <td>29.660000</td>\n",
       "      <td>12.56</td>\n",
       "      <td>53.459999</td>\n",
       "      <td>14.830000</td>\n",
       "      <td>44.110001</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>10.96</td>\n",
       "      <td>12.488695</td>\n",
       "      <td>49.139999</td>\n",
       "      <td>38.480000</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2022-11-04 00:00:00-04:00</th>\n",
       "      <td>5.530000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>30.790001</td>\n",
       "      <td>23.379999</td>\n",
       "      <td>20.730000</td>\n",
       "      <td>14.94</td>\n",
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       "      <td>7.520000</td>\n",
       "      <td>44.860001</td>\n",
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       "    <tr>\n",
       "      <th>2022-11-07 00:00:00-05:00</th>\n",
       "      <td>5.510000</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>31.360001</td>\n",
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       "      <td>14.79</td>\n",
       "      <td>64.680000</td>\n",
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       "      <td>17.990000</td>\n",
       "      <td>7.02</td>\n",
       "      <td>7.570000</td>\n",
       "      <td>45.040001</td>\n",
       "      <td>32.740002</td>\n",
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       "    <tr>\n",
       "      <th>2022-11-08 00:00:00-05:00</th>\n",
       "      <td>5.990000</td>\n",
       "      <td>16.719999</td>\n",
       "      <td>31.250000</td>\n",
       "      <td>24.610001</td>\n",
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       "      <td>14.69</td>\n",
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       "      <td>16.120001</td>\n",
       "      <td>56.400002</td>\n",
       "      <td>17.709999</td>\n",
       "      <td>7.21</td>\n",
       "      <td>7.650000</td>\n",
       "      <td>45.119999</td>\n",
       "      <td>33.520000</td>\n",
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       "    <tr>\n",
       "      <th>2022-11-09 00:00:00-05:00</th>\n",
       "      <td>5.370000</td>\n",
       "      <td>15.840000</td>\n",
       "      <td>31.080000</td>\n",
       "      <td>23.309999</td>\n",
       "      <td>19.580000</td>\n",
       "      <td>15.04</td>\n",
       "      <td>68.959999</td>\n",
       "      <td>16.469999</td>\n",
       "      <td>60.369999</td>\n",
       "      <td>18.799999</td>\n",
       "      <td>6.98</td>\n",
       "      <td>7.570000</td>\n",
       "      <td>44.240002</td>\n",
       "      <td>31.320000</td>\n",
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       "    <tr>\n",
       "      <th>2022-11-10 00:00:00-05:00</th>\n",
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       "      <td>7.64</td>\n",
       "      <td>7.901000</td>\n",
       "      <td>46.939999</td>\n",
       "      <td>34.500000</td>\n",
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       "<p>179 rows × 14 columns</p>\n",
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      ],
      "text/plain": [
       "                                AZRE       CRSR      BATRA       LOVE  \\\n",
       "Date                                                                    \n",
       "2022-02-28 00:00:00-05:00  16.549999  23.080000  25.629999  42.490002   \n",
       "2022-03-01 00:00:00-05:00  18.080000  22.580000  25.629999  41.099998   \n",
       "2022-03-02 00:00:00-05:00  17.180000  23.190001  25.850000  42.560001   \n",
       "2022-03-03 00:00:00-05:00  17.170000  23.350000  25.870001  41.900002   \n",
       "2022-03-04 00:00:00-05:00  16.080000  21.730000  25.540001  40.619999   \n",
       "...                              ...        ...        ...        ...   \n",
       "2022-11-04 00:00:00-04:00   5.530000  15.000000  30.790001  23.379999   \n",
       "2022-11-07 00:00:00-05:00   5.510000  16.500000  31.360001  24.209999   \n",
       "2022-11-08 00:00:00-05:00   5.990000  16.719999  31.250000  24.610001   \n",
       "2022-11-09 00:00:00-05:00   5.370000  15.840000  31.080000  23.309999   \n",
       "2022-11-10 00:00:00-05:00   5.960000  17.459999  32.070000  26.139999   \n",
       "\n",
       "                                  PD    PSQ       SARK         SH       SQQQ  \\\n",
       "Date                                                                           \n",
       "2022-02-28 00:00:00-05:00  33.820000  12.21  45.959999  14.690000  40.730000   \n",
       "2022-03-01 00:00:00-05:00  33.009998  12.41  47.459999  14.920000  42.650002   \n",
       "2022-03-02 00:00:00-05:00  33.290001  12.19  47.869999  14.640000  40.580002   \n",
       "2022-03-03 00:00:00-05:00  31.440001  12.38  50.959999  14.710000  42.270000   \n",
       "2022-03-04 00:00:00-05:00  29.660000  12.56  53.459999  14.830000  44.110001   \n",
       "...                              ...    ...        ...        ...        ...   \n",
       "2022-11-04 00:00:00-04:00  20.730000  14.94  64.129997  16.370001  59.549999   \n",
       "2022-11-07 00:00:00-05:00  20.190001  14.79  64.680000  16.200001  57.660000   \n",
       "2022-11-08 00:00:00-05:00  20.389999  14.69  64.910004  16.120001  56.400002   \n",
       "2022-11-09 00:00:00-05:00  19.580000  15.04  68.959999  16.469999  60.369999   \n",
       "2022-11-10 00:00:00-05:00  22.799999  13.93  59.090000  15.560000  47.080002   \n",
       "\n",
       "                                SPXU   PLTR      MTNOY        ALK       INMD  \n",
       "Date                                                                          \n",
       "2022-02-28 00:00:00-05:00  15.350000  11.85  12.340958  56.139999  42.689999  \n",
       "2022-03-01 00:00:00-05:00  16.049999  12.18  12.449298  52.340000  41.930000  \n",
       "2022-03-02 00:00:00-05:00  15.200000  12.06  13.355418  52.619999  43.009998  \n",
       "2022-03-03 00:00:00-05:00  15.420000  11.34  13.178134  51.419998  41.110001  \n",
       "2022-03-04 00:00:00-05:00  15.780000  10.96  12.488695  49.139999  38.480000  \n",
       "...                              ...    ...        ...        ...        ...  \n",
       "2022-11-04 00:00:00-04:00  18.520000   7.93   7.520000  44.860001  33.349998  \n",
       "2022-11-07 00:00:00-05:00  17.990000   7.02   7.570000  45.040001  32.740002  \n",
       "2022-11-08 00:00:00-05:00  17.709999   7.21   7.650000  45.119999  33.520000  \n",
       "2022-11-09 00:00:00-05:00  18.799999   6.98   7.570000  44.240002  31.320000  \n",
       "2022-11-10 00:00:00-05:00  15.710000   7.64   7.901000  46.939999  34.500000  \n",
       "\n",
       "[179 rows x 14 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio = pd.DataFrame(historical_data)\n",
    "portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "285cb775",
   "metadata": {},
   "outputs": [
    {
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       "      <td>143.609997</td>\n",
       "      <td>29.280001</td>\n",
       "      <td>121.740005</td>\n",
       "      <td>15.200000</td>\n",
       "      <td>578.880020</td>\n",
       "      <td>267.108364</td>\n",
       "      <td>210.479996</td>\n",
       "      <td>516.119980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-03 00:00:00-05:00</th>\n",
       "      <td>240.380001</td>\n",
       "      <td>793.900013</td>\n",
       "      <td>465.660015</td>\n",
       "      <td>419.000015</td>\n",
       "      <td>628.800011</td>\n",
       "      <td>24.760000</td>\n",
       "      <td>152.879997</td>\n",
       "      <td>29.420000</td>\n",
       "      <td>126.810001</td>\n",
       "      <td>15.420000</td>\n",
       "      <td>544.320007</td>\n",
       "      <td>263.562679</td>\n",
       "      <td>205.679993</td>\n",
       "      <td>493.320007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-04 00:00:00-05:00</th>\n",
       "      <td>225.119999</td>\n",
       "      <td>738.819984</td>\n",
       "      <td>459.720016</td>\n",
       "      <td>406.199989</td>\n",
       "      <td>593.199997</td>\n",
       "      <td>25.120001</td>\n",
       "      <td>160.379997</td>\n",
       "      <td>29.660000</td>\n",
       "      <td>132.330002</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>526.080002</td>\n",
       "      <td>249.773903</td>\n",
       "      <td>196.559998</td>\n",
       "      <td>461.759995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-04 00:00:00-04:00</th>\n",
       "      <td>77.420003</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>554.220016</td>\n",
       "      <td>233.799992</td>\n",
       "      <td>414.599991</td>\n",
       "      <td>29.879999</td>\n",
       "      <td>192.389992</td>\n",
       "      <td>32.740002</td>\n",
       "      <td>178.649998</td>\n",
       "      <td>18.520000</td>\n",
       "      <td>380.639992</td>\n",
       "      <td>150.400000</td>\n",
       "      <td>179.440002</td>\n",
       "      <td>400.199982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-07 00:00:00-05:00</th>\n",
       "      <td>77.140003</td>\n",
       "      <td>561.000000</td>\n",
       "      <td>564.480011</td>\n",
       "      <td>242.099991</td>\n",
       "      <td>403.800011</td>\n",
       "      <td>29.580000</td>\n",
       "      <td>194.040001</td>\n",
       "      <td>32.400002</td>\n",
       "      <td>172.980000</td>\n",
       "      <td>17.990000</td>\n",
       "      <td>336.959999</td>\n",
       "      <td>151.400003</td>\n",
       "      <td>180.160004</td>\n",
       "      <td>392.880020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-08 00:00:00-05:00</th>\n",
       "      <td>83.859997</td>\n",
       "      <td>568.479977</td>\n",
       "      <td>562.500000</td>\n",
       "      <td>246.100006</td>\n",
       "      <td>407.799988</td>\n",
       "      <td>29.379999</td>\n",
       "      <td>194.730011</td>\n",
       "      <td>32.240002</td>\n",
       "      <td>169.200005</td>\n",
       "      <td>17.709999</td>\n",
       "      <td>346.080002</td>\n",
       "      <td>153.000002</td>\n",
       "      <td>180.479996</td>\n",
       "      <td>402.240005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-09 00:00:00-05:00</th>\n",
       "      <td>75.179998</td>\n",
       "      <td>538.560005</td>\n",
       "      <td>559.439999</td>\n",
       "      <td>233.099995</td>\n",
       "      <td>391.599998</td>\n",
       "      <td>30.080000</td>\n",
       "      <td>206.879997</td>\n",
       "      <td>32.939999</td>\n",
       "      <td>181.109997</td>\n",
       "      <td>18.799999</td>\n",
       "      <td>335.040001</td>\n",
       "      <td>151.400003</td>\n",
       "      <td>176.960007</td>\n",
       "      <td>375.839996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-10 00:00:00-05:00</th>\n",
       "      <td>83.440001</td>\n",
       "      <td>593.639969</td>\n",
       "      <td>577.259995</td>\n",
       "      <td>261.399994</td>\n",
       "      <td>455.999985</td>\n",
       "      <td>27.860001</td>\n",
       "      <td>177.270000</td>\n",
       "      <td>31.120001</td>\n",
       "      <td>141.240005</td>\n",
       "      <td>15.710000</td>\n",
       "      <td>366.719994</td>\n",
       "      <td>158.020000</td>\n",
       "      <td>187.759995</td>\n",
       "      <td>414.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 AZRE        CRSR       BATRA        LOVE  \\\n",
       "Date                                                                        \n",
       "2022-02-28 00:00:00-05:00  231.699989  784.719997  461.339985  424.900017   \n",
       "2022-03-01 00:00:00-05:00  253.119999  767.719997  461.339985  410.999985   \n",
       "2022-03-02 00:00:00-05:00  240.520004  788.460018  465.300007  425.600014   \n",
       "2022-03-03 00:00:00-05:00  240.380001  793.900013  465.660015  419.000015   \n",
       "2022-03-04 00:00:00-05:00  225.119999  738.819984  459.720016  406.199989   \n",
       "...                               ...         ...         ...         ...   \n",
       "2022-11-04 00:00:00-04:00   77.420003  510.000000  554.220016  233.799992   \n",
       "2022-11-07 00:00:00-05:00   77.140003  561.000000  564.480011  242.099991   \n",
       "2022-11-08 00:00:00-05:00   83.859997  568.479977  562.500000  246.100006   \n",
       "2022-11-09 00:00:00-05:00   75.179998  538.560005  559.439999  233.099995   \n",
       "2022-11-10 00:00:00-05:00   83.440001  593.639969  577.259995  261.399994   \n",
       "\n",
       "                                   PD        PSQ        SARK         SH  \\\n",
       "Date                                                                      \n",
       "2022-02-28 00:00:00-05:00  676.399994  24.420000  137.879997  29.379999   \n",
       "2022-03-01 00:00:00-05:00  660.199966  24.820000  142.379997  29.840000   \n",
       "2022-03-02 00:00:00-05:00  665.800018  24.379999  143.609997  29.280001   \n",
       "2022-03-03 00:00:00-05:00  628.800011  24.760000  152.879997  29.420000   \n",
       "2022-03-04 00:00:00-05:00  593.199997  25.120001  160.379997  29.660000   \n",
       "...                               ...        ...         ...        ...   \n",
       "2022-11-04 00:00:00-04:00  414.599991  29.879999  192.389992  32.740002   \n",
       "2022-11-07 00:00:00-05:00  403.800011  29.580000  194.040001  32.400002   \n",
       "2022-11-08 00:00:00-05:00  407.799988  29.379999  194.730011  32.240002   \n",
       "2022-11-09 00:00:00-05:00  391.599998  30.080000  206.879997  32.939999   \n",
       "2022-11-10 00:00:00-05:00  455.999985  27.860001  177.270000  31.120001   \n",
       "\n",
       "                                 SQQQ       SPXU        PLTR       MTNOY  \\\n",
       "Date                                                                       \n",
       "2022-02-28 00:00:00-05:00  122.189999  15.350000  568.800018  246.819153   \n",
       "2022-03-01 00:00:00-05:00  127.950005  16.049999  584.640015  248.985958   \n",
       "2022-03-02 00:00:00-05:00  121.740005  15.200000  578.880020  267.108364   \n",
       "2022-03-03 00:00:00-05:00  126.810001  15.420000  544.320007  263.562679   \n",
       "2022-03-04 00:00:00-05:00  132.330002  15.780000  526.080002  249.773903   \n",
       "...                               ...        ...         ...         ...   \n",
       "2022-11-04 00:00:00-04:00  178.649998  18.520000  380.639992  150.400000   \n",
       "2022-11-07 00:00:00-05:00  172.980000  17.990000  336.959999  151.400003   \n",
       "2022-11-08 00:00:00-05:00  169.200005  17.709999  346.080002  153.000002   \n",
       "2022-11-09 00:00:00-05:00  181.109997  18.799999  335.040001  151.400003   \n",
       "2022-11-10 00:00:00-05:00  141.240005  15.710000  366.719994  158.020000   \n",
       "\n",
       "                                  ALK        INMD  \n",
       "Date                                               \n",
       "2022-02-28 00:00:00-05:00  224.559998  512.279984  \n",
       "2022-03-01 00:00:00-05:00  209.360001  503.160004  \n",
       "2022-03-02 00:00:00-05:00  210.479996  516.119980  \n",
       "2022-03-03 00:00:00-05:00  205.679993  493.320007  \n",
       "2022-03-04 00:00:00-05:00  196.559998  461.759995  \n",
       "...                               ...         ...  \n",
       "2022-11-04 00:00:00-04:00  179.440002  400.199982  \n",
       "2022-11-07 00:00:00-05:00  180.160004  392.880020  \n",
       "2022-11-08 00:00:00-05:00  180.479996  402.240005  \n",
       "2022-11-09 00:00:00-05:00  176.960007  375.839996  \n",
       "2022-11-10 00:00:00-05:00  187.759995  414.000000  \n",
       "\n",
       "[179 rows x 14 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for index, x in df.iterrows():\n",
    "    portfolio[x.Holdings]*=x.Quantity\n",
    "portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dc90f59d",
   "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>AZRE</th>\n",
       "      <th>CRSR</th>\n",
       "      <th>BATRA</th>\n",
       "      <th>LOVE</th>\n",
       "      <th>PD</th>\n",
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       "      <th>PortValue</th>\n",
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       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-02-28 00:00:00-05:00</th>\n",
       "      <td>231.699989</td>\n",
       "      <td>784.719997</td>\n",
       "      <td>461.339985</td>\n",
       "      <td>424.900017</td>\n",
       "      <td>676.399994</td>\n",
       "      <td>24.420000</td>\n",
       "      <td>137.879997</td>\n",
       "      <td>29.379999</td>\n",
       "      <td>122.189999</td>\n",
       "      <td>15.350000</td>\n",
       "      <td>568.800018</td>\n",
       "      <td>246.819153</td>\n",
       "      <td>224.559998</td>\n",
       "      <td>512.279984</td>\n",
       "      <td>4460.739130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-01 00:00:00-05:00</th>\n",
       "      <td>253.119999</td>\n",
       "      <td>767.719997</td>\n",
       "      <td>461.339985</td>\n",
       "      <td>410.999985</td>\n",
       "      <td>660.199966</td>\n",
       "      <td>24.820000</td>\n",
       "      <td>142.379997</td>\n",
       "      <td>29.840000</td>\n",
       "      <td>127.950005</td>\n",
       "      <td>16.049999</td>\n",
       "      <td>584.640015</td>\n",
       "      <td>248.985958</td>\n",
       "      <td>209.360001</td>\n",
       "      <td>503.160004</td>\n",
       "      <td>4440.565910</td>\n",
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       "    <tr>\n",
       "      <th>2022-03-02 00:00:00-05:00</th>\n",
       "      <td>240.520004</td>\n",
       "      <td>788.460018</td>\n",
       "      <td>465.300007</td>\n",
       "      <td>425.600014</td>\n",
       "      <td>665.800018</td>\n",
       "      <td>24.379999</td>\n",
       "      <td>143.609997</td>\n",
       "      <td>29.280001</td>\n",
       "      <td>121.740005</td>\n",
       "      <td>15.200000</td>\n",
       "      <td>578.880020</td>\n",
       "      <td>267.108364</td>\n",
       "      <td>210.479996</td>\n",
       "      <td>516.119980</td>\n",
       "      <td>4492.478423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-03 00:00:00-05:00</th>\n",
       "      <td>240.380001</td>\n",
       "      <td>793.900013</td>\n",
       "      <td>465.660015</td>\n",
       "      <td>419.000015</td>\n",
       "      <td>628.800011</td>\n",
       "      <td>24.760000</td>\n",
       "      <td>152.879997</td>\n",
       "      <td>29.420000</td>\n",
       "      <td>126.810001</td>\n",
       "      <td>15.420000</td>\n",
       "      <td>544.320007</td>\n",
       "      <td>263.562679</td>\n",
       "      <td>205.679993</td>\n",
       "      <td>493.320007</td>\n",
       "      <td>4403.912741</td>\n",
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       "    <tr>\n",
       "      <th>2022-03-04 00:00:00-05:00</th>\n",
       "      <td>225.119999</td>\n",
       "      <td>738.819984</td>\n",
       "      <td>459.720016</td>\n",
       "      <td>406.199989</td>\n",
       "      <td>593.199997</td>\n",
       "      <td>25.120001</td>\n",
       "      <td>160.379997</td>\n",
       "      <td>29.660000</td>\n",
       "      <td>132.330002</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>526.080002</td>\n",
       "      <td>249.773903</td>\n",
       "      <td>196.559998</td>\n",
       "      <td>461.759995</td>\n",
       "      <td>4220.503882</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-04 00:00:00-04:00</th>\n",
       "      <td>77.420003</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>554.220016</td>\n",
       "      <td>233.799992</td>\n",
       "      <td>414.599991</td>\n",
       "      <td>29.879999</td>\n",
       "      <td>192.389992</td>\n",
       "      <td>32.740002</td>\n",
       "      <td>178.649998</td>\n",
       "      <td>18.520000</td>\n",
       "      <td>380.639992</td>\n",
       "      <td>150.400000</td>\n",
       "      <td>179.440002</td>\n",
       "      <td>400.199982</td>\n",
       "      <td>3352.899968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-07 00:00:00-05:00</th>\n",
       "      <td>77.140003</td>\n",
       "      <td>561.000000</td>\n",
       "      <td>564.480011</td>\n",
       "      <td>242.099991</td>\n",
       "      <td>403.800011</td>\n",
       "      <td>29.580000</td>\n",
       "      <td>194.040001</td>\n",
       "      <td>32.400002</td>\n",
       "      <td>172.980000</td>\n",
       "      <td>17.990000</td>\n",
       "      <td>336.959999</td>\n",
       "      <td>151.400003</td>\n",
       "      <td>180.160004</td>\n",
       "      <td>392.880020</td>\n",
       "      <td>3356.910044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-08 00:00:00-05:00</th>\n",
       "      <td>83.859997</td>\n",
       "      <td>568.479977</td>\n",
       "      <td>562.500000</td>\n",
       "      <td>246.100006</td>\n",
       "      <td>407.799988</td>\n",
       "      <td>29.379999</td>\n",
       "      <td>194.730011</td>\n",
       "      <td>32.240002</td>\n",
       "      <td>169.200005</td>\n",
       "      <td>17.709999</td>\n",
       "      <td>346.080002</td>\n",
       "      <td>153.000002</td>\n",
       "      <td>180.479996</td>\n",
       "      <td>402.240005</td>\n",
       "      <td>3393.799988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-09 00:00:00-05:00</th>\n",
       "      <td>75.179998</td>\n",
       "      <td>538.560005</td>\n",
       "      <td>559.439999</td>\n",
       "      <td>233.099995</td>\n",
       "      <td>391.599998</td>\n",
       "      <td>30.080000</td>\n",
       "      <td>206.879997</td>\n",
       "      <td>32.939999</td>\n",
       "      <td>181.109997</td>\n",
       "      <td>18.799999</td>\n",
       "      <td>335.040001</td>\n",
       "      <td>151.400003</td>\n",
       "      <td>176.960007</td>\n",
       "      <td>375.839996</td>\n",
       "      <td>3306.929995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-10 00:00:00-05:00</th>\n",
       "      <td>83.440001</td>\n",
       "      <td>593.639969</td>\n",
       "      <td>577.259995</td>\n",
       "      <td>261.399994</td>\n",
       "      <td>455.999985</td>\n",
       "      <td>27.860001</td>\n",
       "      <td>177.270000</td>\n",
       "      <td>31.120001</td>\n",
       "      <td>141.240005</td>\n",
       "      <td>15.710000</td>\n",
       "      <td>366.719994</td>\n",
       "      <td>158.020000</td>\n",
       "      <td>187.759995</td>\n",
       "      <td>414.000000</td>\n",
       "      <td>3491.439939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 AZRE        CRSR       BATRA        LOVE  \\\n",
       "Date                                                                        \n",
       "2022-02-28 00:00:00-05:00  231.699989  784.719997  461.339985  424.900017   \n",
       "2022-03-01 00:00:00-05:00  253.119999  767.719997  461.339985  410.999985   \n",
       "2022-03-02 00:00:00-05:00  240.520004  788.460018  465.300007  425.600014   \n",
       "2022-03-03 00:00:00-05:00  240.380001  793.900013  465.660015  419.000015   \n",
       "2022-03-04 00:00:00-05:00  225.119999  738.819984  459.720016  406.199989   \n",
       "...                               ...         ...         ...         ...   \n",
       "2022-11-04 00:00:00-04:00   77.420003  510.000000  554.220016  233.799992   \n",
       "2022-11-07 00:00:00-05:00   77.140003  561.000000  564.480011  242.099991   \n",
       "2022-11-08 00:00:00-05:00   83.859997  568.479977  562.500000  246.100006   \n",
       "2022-11-09 00:00:00-05:00   75.179998  538.560005  559.439999  233.099995   \n",
       "2022-11-10 00:00:00-05:00   83.440001  593.639969  577.259995  261.399994   \n",
       "\n",
       "                                   PD        PSQ        SARK         SH  \\\n",
       "Date                                                                      \n",
       "2022-02-28 00:00:00-05:00  676.399994  24.420000  137.879997  29.379999   \n",
       "2022-03-01 00:00:00-05:00  660.199966  24.820000  142.379997  29.840000   \n",
       "2022-03-02 00:00:00-05:00  665.800018  24.379999  143.609997  29.280001   \n",
       "2022-03-03 00:00:00-05:00  628.800011  24.760000  152.879997  29.420000   \n",
       "2022-03-04 00:00:00-05:00  593.199997  25.120001  160.379997  29.660000   \n",
       "...                               ...        ...         ...        ...   \n",
       "2022-11-04 00:00:00-04:00  414.599991  29.879999  192.389992  32.740002   \n",
       "2022-11-07 00:00:00-05:00  403.800011  29.580000  194.040001  32.400002   \n",
       "2022-11-08 00:00:00-05:00  407.799988  29.379999  194.730011  32.240002   \n",
       "2022-11-09 00:00:00-05:00  391.599998  30.080000  206.879997  32.939999   \n",
       "2022-11-10 00:00:00-05:00  455.999985  27.860001  177.270000  31.120001   \n",
       "\n",
       "                                 SQQQ       SPXU        PLTR       MTNOY  \\\n",
       "Date                                                                       \n",
       "2022-02-28 00:00:00-05:00  122.189999  15.350000  568.800018  246.819153   \n",
       "2022-03-01 00:00:00-05:00  127.950005  16.049999  584.640015  248.985958   \n",
       "2022-03-02 00:00:00-05:00  121.740005  15.200000  578.880020  267.108364   \n",
       "2022-03-03 00:00:00-05:00  126.810001  15.420000  544.320007  263.562679   \n",
       "2022-03-04 00:00:00-05:00  132.330002  15.780000  526.080002  249.773903   \n",
       "...                               ...        ...         ...         ...   \n",
       "2022-11-04 00:00:00-04:00  178.649998  18.520000  380.639992  150.400000   \n",
       "2022-11-07 00:00:00-05:00  172.980000  17.990000  336.959999  151.400003   \n",
       "2022-11-08 00:00:00-05:00  169.200005  17.709999  346.080002  153.000002   \n",
       "2022-11-09 00:00:00-05:00  181.109997  18.799999  335.040001  151.400003   \n",
       "2022-11-10 00:00:00-05:00  141.240005  15.710000  366.719994  158.020000   \n",
       "\n",
       "                                  ALK        INMD    PortValue  \n",
       "Date                                                            \n",
       "2022-02-28 00:00:00-05:00  224.559998  512.279984  4460.739130  \n",
       "2022-03-01 00:00:00-05:00  209.360001  503.160004  4440.565910  \n",
       "2022-03-02 00:00:00-05:00  210.479996  516.119980  4492.478423  \n",
       "2022-03-03 00:00:00-05:00  205.679993  493.320007  4403.912741  \n",
       "2022-03-04 00:00:00-05:00  196.559998  461.759995  4220.503882  \n",
       "...                               ...         ...          ...  \n",
       "2022-11-04 00:00:00-04:00  179.440002  400.199982  3352.899968  \n",
       "2022-11-07 00:00:00-05:00  180.160004  392.880020  3356.910044  \n",
       "2022-11-08 00:00:00-05:00  180.479996  402.240005  3393.799988  \n",
       "2022-11-09 00:00:00-05:00  176.960007  375.839996  3306.929995  \n",
       "2022-11-10 00:00:00-05:00  187.759995  414.000000  3491.439939  \n",
       "\n",
       "[179 rows x 15 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio[\"PortValue\"] = portfolio.sum(axis=1)\n",
    "portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3847dc79",
   "metadata": {},
   "outputs": [],
   "source": [
    "pct_change = []\n",
    "for i in range(1, len(portfolio[\"PortValue\"])):\n",
    "    curr = portfolio[\"PortValue\"][i]\n",
    "    yester = portfolio[\"PortValue\"][i-1]\n",
    "    delta = curr-yester\n",
    "    pct_change.append(delta/curr * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aa943512",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.1625241793203852\n"
     ]
    }
   ],
   "source": [
    "avg_pct_change = statistics.mean(pct_change)\n",
    "print(avg_pct_change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "671177dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2346884643281424\n"
     ]
    }
   ],
   "source": [
    "stdev_pct_change = statistics.stdev(pct_change)\n",
    "print(stdev_pct_change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7b18a1c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.07921649131240646\n"
     ]
    }
   ],
   "source": [
    "print((avg_pct_change-.0145)/stdev_pct_change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "75b9a6da",
   "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>AZRE</th>\n",
       "      <th>CRSR</th>\n",
       "      <th>BATRA</th>\n",
       "      <th>LOVE</th>\n",
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       "      <th>2022-03-01 00:00:00-05:00</th>\n",
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       "      <td>-0.055921</td>\n",
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       "      <td>0.067847</td>\n",
       "      <td>0.005321</td>\n",
       "      <td>0.025110</td>\n",
       "      <td>0.011555</td>\n",
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       "    <tr>\n",
       "      <th>2022-03-03 00:00:00-05:00</th>\n",
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       "      <td>0.000773</td>\n",
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       "      <th>2022-11-07 00:00:00-05:00</th>\n",
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       "      <td>-0.018632</td>\n",
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       "    <tr>\n",
       "      <th>2022-11-08 00:00:00-05:00</th>\n",
       "      <td>0.080133</td>\n",
       "      <td>0.013158</td>\n",
       "      <td>-0.003520</td>\n",
       "      <td>0.016254</td>\n",
       "      <td>0.009809</td>\n",
       "      <td>-0.006807</td>\n",
       "      <td>0.003543</td>\n",
       "      <td>-0.004963</td>\n",
       "      <td>-0.022340</td>\n",
       "      <td>-0.015810</td>\n",
       "      <td>0.026352</td>\n",
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       "      <td>0.001773</td>\n",
       "      <td>0.023270</td>\n",
       "      <td>0.010870</td>\n",
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       "    <tr>\n",
       "      <th>2022-11-09 00:00:00-05:00</th>\n",
       "      <td>-0.115456</td>\n",
       "      <td>-0.055556</td>\n",
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       "    <tr>\n",
       "      <th>2022-11-10 00:00:00-05:00</th>\n",
       "      <td>0.098993</td>\n",
       "      <td>0.092783</td>\n",
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       "      <td>-0.282285</td>\n",
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       "      <td>0.041893</td>\n",
       "      <td>0.057520</td>\n",
       "      <td>0.092174</td>\n",
       "      <td>0.052846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               AZRE      CRSR     BATRA      LOVE        PD  \\\n",
       "Date                                                                          \n",
       "2022-02-28 00:00:00-05:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2022-03-01 00:00:00-05:00  0.084624 -0.022143  0.000000 -0.033820 -0.024538   \n",
       "2022-03-02 00:00:00-05:00 -0.052386  0.026304  0.008511  0.034305  0.008411   \n",
       "2022-03-03 00:00:00-05:00 -0.000582  0.006852  0.000773 -0.015752 -0.058842   \n",
       "2022-03-04 00:00:00-05:00 -0.067786 -0.074551 -0.012921 -0.031512 -0.060014   \n",
       "...                             ...       ...       ...       ...       ...   \n",
       "2022-11-04 00:00:00-04:00 -0.025316  0.158000 -0.011042  0.024808 -0.068982   \n",
       "2022-11-07 00:00:00-05:00 -0.003630  0.090909  0.018176  0.034283 -0.026746   \n",
       "2022-11-08 00:00:00-05:00  0.080133  0.013158 -0.003520  0.016254  0.009809   \n",
       "2022-11-09 00:00:00-05:00 -0.115456 -0.055556 -0.005470 -0.055770 -0.041369   \n",
       "2022-11-10 00:00:00-05:00  0.098993  0.092783  0.030870  0.108263  0.141228   \n",
       "\n",
       "                                PSQ      SARK        SH      SQQQ      SPXU  \\\n",
       "Date                                                                          \n",
       "2022-02-28 00:00:00-05:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2022-03-01 00:00:00-05:00  0.016116  0.031606  0.015416  0.045018  0.043614   \n",
       "2022-03-02 00:00:00-05:00 -0.018048  0.008565 -0.019126 -0.051010 -0.055921   \n",
       "2022-03-03 00:00:00-05:00  0.015347  0.060636  0.004759  0.039981  0.014267   \n",
       "2022-03-04 00:00:00-05:00  0.014331  0.046764  0.008092  0.041714  0.022814   \n",
       "...                             ...       ...       ...       ...       ...   \n",
       "2022-11-04 00:00:00-04:00 -0.017403  0.026197 -0.014050 -0.048699 -0.043197   \n",
       "2022-11-07 00:00:00-05:00 -0.010142  0.008503 -0.010494 -0.032778 -0.029461   \n",
       "2022-11-08 00:00:00-05:00 -0.006807  0.003543 -0.004963 -0.022340 -0.015810   \n",
       "2022-11-09 00:00:00-05:00  0.023271  0.058730  0.021251  0.065761  0.057979   \n",
       "2022-11-10 00:00:00-05:00 -0.079684 -0.167033 -0.058483 -0.282285 -0.196690   \n",
       "\n",
       "                               PLTR     MTNOY       ALK      INMD  PortValue  \n",
       "Date                                                                          \n",
       "2022-02-28 00:00:00-05:00  0.000000  0.000000  0.000000  0.000000   0.000000  \n",
       "2022-03-01 00:00:00-05:00  0.027094  0.008703 -0.072602 -0.018125  -0.004543  \n",
       "2022-03-02 00:00:00-05:00 -0.009950  0.067847  0.005321  0.025110   0.011555  \n",
       "2022-03-03 00:00:00-05:00 -0.063492 -0.013453 -0.023337 -0.046217  -0.020111  \n",
       "2022-03-04 00:00:00-05:00 -0.034672 -0.055205 -0.046398 -0.068347  -0.043457  \n",
       "...                             ...       ...       ...       ...        ...  \n",
       "2022-11-04 00:00:00-04:00 -0.018916  0.041223  0.022737 -0.005697   0.013439  \n",
       "2022-11-07 00:00:00-05:00 -0.129630  0.006605  0.003996 -0.018632   0.001195  \n",
       "2022-11-08 00:00:00-05:00  0.026352  0.010458  0.001773  0.023270   0.010870  \n",
       "2022-11-09 00:00:00-05:00 -0.032951 -0.010568 -0.019891 -0.070243  -0.026269  \n",
       "2022-11-10 00:00:00-05:00  0.086387  0.041893  0.057520  0.092174   0.052846  \n",
       "\n",
       "[179 rows x 15 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for sym in portfolio.columns:\n",
    "    data = list(portfolio[sym])\n",
    "    pct_change = []\n",
    "    for i in range(1, len(data)):\n",
    "        curr = data[i]\n",
    "        yester = data[i-1]\n",
    "        delta = curr-yester\n",
    "        pct_change.append(delta/curr)\n",
    "    pct_change.insert(0, 0)\n",
    "    portfolio[sym] = pct_change\n",
    "\n",
    "portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4128bdd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>AZRE</th>\n",
       "      <th>CRSR</th>\n",
       "      <th>BATRA</th>\n",
       "      <th>LOVE</th>\n",
       "      <th>PD</th>\n",
       "      <th>PSQ</th>\n",
       "      <th>SARK</th>\n",
       "      <th>SH</th>\n",
       "      <th>SQQQ</th>\n",
       "      <th>SPXU</th>\n",
       "      <th>PLTR</th>\n",
       "      <th>MTNOY</th>\n",
       "      <th>ALK</th>\n",
       "      <th>INMD</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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2022-02-28 00:00:00-05:00</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2022-03-01 00:00:00-05:00</th>\n",
       "      <td>0.084624</td>\n",
       "      <td>-0.022143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.033820</td>\n",
       "      <td>-0.024538</td>\n",
       "      <td>0.016116</td>\n",
       "      <td>0.031606</td>\n",
       "      <td>0.015416</td>\n",
       "      <td>0.045018</td>\n",
       "      <td>0.043614</td>\n",
       "      <td>0.027094</td>\n",
       "      <td>0.008703</td>\n",
       "      <td>-0.072602</td>\n",
       "      <td>-0.018125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-02 00:00:00-05:00</th>\n",
       "      <td>-0.052386</td>\n",
       "      <td>0.026304</td>\n",
       "      <td>0.008511</td>\n",
       "      <td>0.034305</td>\n",
       "      <td>0.008411</td>\n",
       "      <td>-0.018048</td>\n",
       "      <td>0.008565</td>\n",
       "      <td>-0.019126</td>\n",
       "      <td>-0.051010</td>\n",
       "      <td>-0.055921</td>\n",
       "      <td>-0.009950</td>\n",
       "      <td>0.067847</td>\n",
       "      <td>0.005321</td>\n",
       "      <td>0.025110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-03 00:00:00-05:00</th>\n",
       "      <td>-0.000582</td>\n",
       "      <td>0.006852</td>\n",
       "      <td>0.000773</td>\n",
       "      <td>-0.015752</td>\n",
       "      <td>-0.058842</td>\n",
       "      <td>0.015347</td>\n",
       "      <td>0.060636</td>\n",
       "      <td>0.004759</td>\n",
       "      <td>0.039981</td>\n",
       "      <td>0.014267</td>\n",
       "      <td>-0.063492</td>\n",
       "      <td>-0.013453</td>\n",
       "      <td>-0.023337</td>\n",
       "      <td>-0.046217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-04 00:00:00-05:00</th>\n",
       "      <td>-0.067786</td>\n",
       "      <td>-0.074551</td>\n",
       "      <td>-0.012921</td>\n",
       "      <td>-0.031512</td>\n",
       "      <td>-0.060014</td>\n",
       "      <td>0.014331</td>\n",
       "      <td>0.046764</td>\n",
       "      <td>0.008092</td>\n",
       "      <td>0.041714</td>\n",
       "      <td>0.022814</td>\n",
       "      <td>-0.034672</td>\n",
       "      <td>-0.055205</td>\n",
       "      <td>-0.046398</td>\n",
       "      <td>-0.068347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2022-11-04 00:00:00-04:00</th>\n",
       "      <td>-0.025316</td>\n",
       "      <td>0.158000</td>\n",
       "      <td>-0.011042</td>\n",
       "      <td>0.024808</td>\n",
       "      <td>-0.068982</td>\n",
       "      <td>-0.017403</td>\n",
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       "      <td>-0.014050</td>\n",
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       "      <td>-0.043197</td>\n",
       "      <td>-0.018916</td>\n",
       "      <td>0.041223</td>\n",
       "      <td>0.022737</td>\n",
       "      <td>-0.005697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-07 00:00:00-05:00</th>\n",
       "      <td>-0.003630</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.018176</td>\n",
       "      <td>0.034283</td>\n",
       "      <td>-0.026746</td>\n",
       "      <td>-0.010142</td>\n",
       "      <td>0.008503</td>\n",
       "      <td>-0.010494</td>\n",
       "      <td>-0.032778</td>\n",
       "      <td>-0.029461</td>\n",
       "      <td>-0.129630</td>\n",
       "      <td>0.006605</td>\n",
       "      <td>0.003996</td>\n",
       "      <td>-0.018632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-08 00:00:00-05:00</th>\n",
       "      <td>0.080133</td>\n",
       "      <td>0.013158</td>\n",
       "      <td>-0.003520</td>\n",
       "      <td>0.016254</td>\n",
       "      <td>0.009809</td>\n",
       "      <td>-0.006807</td>\n",
       "      <td>0.003543</td>\n",
       "      <td>-0.004963</td>\n",
       "      <td>-0.022340</td>\n",
       "      <td>-0.015810</td>\n",
       "      <td>0.026352</td>\n",
       "      <td>0.010458</td>\n",
       "      <td>0.001773</td>\n",
       "      <td>0.023270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-09 00:00:00-05:00</th>\n",
       "      <td>-0.115456</td>\n",
       "      <td>-0.055556</td>\n",
       "      <td>-0.005470</td>\n",
       "      <td>-0.055770</td>\n",
       "      <td>-0.041369</td>\n",
       "      <td>0.023271</td>\n",
       "      <td>0.058730</td>\n",
       "      <td>0.021251</td>\n",
       "      <td>0.065761</td>\n",
       "      <td>0.057979</td>\n",
       "      <td>-0.032951</td>\n",
       "      <td>-0.010568</td>\n",
       "      <td>-0.019891</td>\n",
       "      <td>-0.070243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-10 00:00:00-05:00</th>\n",
       "      <td>0.098993</td>\n",
       "      <td>0.092783</td>\n",
       "      <td>0.030870</td>\n",
       "      <td>0.108263</td>\n",
       "      <td>0.141228</td>\n",
       "      <td>-0.079684</td>\n",
       "      <td>-0.167033</td>\n",
       "      <td>-0.058483</td>\n",
       "      <td>-0.282285</td>\n",
       "      <td>-0.196690</td>\n",
       "      <td>0.086387</td>\n",
       "      <td>0.041893</td>\n",
       "      <td>0.057520</td>\n",
       "      <td>0.092174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               AZRE      CRSR     BATRA      LOVE        PD  \\\n",
       "Date                                                                          \n",
       "2022-02-28 00:00:00-05:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2022-03-01 00:00:00-05:00  0.084624 -0.022143  0.000000 -0.033820 -0.024538   \n",
       "2022-03-02 00:00:00-05:00 -0.052386  0.026304  0.008511  0.034305  0.008411   \n",
       "2022-03-03 00:00:00-05:00 -0.000582  0.006852  0.000773 -0.015752 -0.058842   \n",
       "2022-03-04 00:00:00-05:00 -0.067786 -0.074551 -0.012921 -0.031512 -0.060014   \n",
       "...                             ...       ...       ...       ...       ...   \n",
       "2022-11-04 00:00:00-04:00 -0.025316  0.158000 -0.011042  0.024808 -0.068982   \n",
       "2022-11-07 00:00:00-05:00 -0.003630  0.090909  0.018176  0.034283 -0.026746   \n",
       "2022-11-08 00:00:00-05:00  0.080133  0.013158 -0.003520  0.016254  0.009809   \n",
       "2022-11-09 00:00:00-05:00 -0.115456 -0.055556 -0.005470 -0.055770 -0.041369   \n",
       "2022-11-10 00:00:00-05:00  0.098993  0.092783  0.030870  0.108263  0.141228   \n",
       "\n",
       "                                PSQ      SARK        SH      SQQQ      SPXU  \\\n",
       "Date                                                                          \n",
       "2022-02-28 00:00:00-05:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2022-03-01 00:00:00-05:00  0.016116  0.031606  0.015416  0.045018  0.043614   \n",
       "2022-03-02 00:00:00-05:00 -0.018048  0.008565 -0.019126 -0.051010 -0.055921   \n",
       "2022-03-03 00:00:00-05:00  0.015347  0.060636  0.004759  0.039981  0.014267   \n",
       "2022-03-04 00:00:00-05:00  0.014331  0.046764  0.008092  0.041714  0.022814   \n",
       "...                             ...       ...       ...       ...       ...   \n",
       "2022-11-04 00:00:00-04:00 -0.017403  0.026197 -0.014050 -0.048699 -0.043197   \n",
       "2022-11-07 00:00:00-05:00 -0.010142  0.008503 -0.010494 -0.032778 -0.029461   \n",
       "2022-11-08 00:00:00-05:00 -0.006807  0.003543 -0.004963 -0.022340 -0.015810   \n",
       "2022-11-09 00:00:00-05:00  0.023271  0.058730  0.021251  0.065761  0.057979   \n",
       "2022-11-10 00:00:00-05:00 -0.079684 -0.167033 -0.058483 -0.282285 -0.196690   \n",
       "\n",
       "                               PLTR     MTNOY       ALK      INMD  \n",
       "Date                                                               \n",
       "2022-02-28 00:00:00-05:00  0.000000  0.000000  0.000000  0.000000  \n",
       "2022-03-01 00:00:00-05:00  0.027094  0.008703 -0.072602 -0.018125  \n",
       "2022-03-02 00:00:00-05:00 -0.009950  0.067847  0.005321  0.025110  \n",
       "2022-03-03 00:00:00-05:00 -0.063492 -0.013453 -0.023337 -0.046217  \n",
       "2022-03-04 00:00:00-05:00 -0.034672 -0.055205 -0.046398 -0.068347  \n",
       "...                             ...       ...       ...       ...  \n",
       "2022-11-04 00:00:00-04:00 -0.018916  0.041223  0.022737 -0.005697  \n",
       "2022-11-07 00:00:00-05:00 -0.129630  0.006605  0.003996 -0.018632  \n",
       "2022-11-08 00:00:00-05:00  0.026352  0.010458  0.001773  0.023270  \n",
       "2022-11-09 00:00:00-05:00 -0.032951 -0.010568 -0.019891 -0.070243  \n",
       "2022-11-10 00:00:00-05:00  0.086387  0.041893  0.057520  0.092174  \n",
       "\n",
       "[179 rows x 14 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio = portfolio.drop(['PortValue'], axis=1)\n",
    "portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "90601afe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_average(closing_prices_for_multiple_equities):\n",
    "    sums = [0] * len(closing_prices_for_multiple_equities[0])\n",
    "    for row in closing_prices_for_multiple_equities:\n",
    "        for i in range(len(row)):\n",
    "            sums[i] += row[i]\n",
    "\n",
    "    avgs = [x/len(closing_prices_for_multiple_equities) for x in sums]\n",
    "    return avgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6a1f4de0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.009007518983210379, -0.002360452549971179, 0.001155554926435003, -0.004012912827622752, -0.0033522237437184346, 0.0005094565247488378, 0.0004131332451041153, 0.00018887027979365352, -0.001203581060790059, -0.0010593980907649667, -0.0035647595851330083, -0.0029463115863512667, -0.001389502737895717, -0.002161956008816806]\n"
     ]
    }
   ],
   "source": [
    "apex_port = portfolio.to_numpy()\n",
    "apex_avg_returns = compute_average(apex_port)\n",
    "print(apex_avg_returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "847216a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_stocks_demeaned(closing_prices_matrix, average_closing_prices):\n",
    "    m = len(closing_prices_matrix)\n",
    "    n = len(closing_prices_matrix[0])\n",
    "    demeaned = [[0 for i in range(n)] for j in range(m)]\n",
    "    for i in range(len(closing_prices_matrix)):\n",
    "        for j in range(len(closing_prices_matrix[i])):\n",
    "            demeaned[i][j] = closing_prices_matrix[i][j] - average_closing_prices[j]\n",
    "\n",
    "    return demeaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b9d706f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_covariance_matrix(demeaned):\n",
    "    s_minus_m = np.array(demeaned)\n",
    "    s_minus_m_t = np.transpose(demeaned)\n",
    "    return np.matmul(s_minus_m_t, s_minus_m)/len(demeaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2ff79deb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.009007518983210379,\n",
       "  0.002360452549971179,\n",
       "  -0.001155554926435003,\n",
       "  0.004012912827622752,\n",
       "  0.0033522237437184346,\n",
       "  -0.0005094565247488378,\n",
       "  -0.0004131332451041153,\n",
       "  -0.00018887027979365352,\n",
       "  0.001203581060790059,\n",
       "  0.0010593980907649667,\n",
       "  0.0035647595851330083,\n",
       "  0.0029463115863512667,\n",
       "  0.001389502737895717,\n",
       "  0.002161956008816806],\n",
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       " [0.1080008325422029,\n",
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       "  -0.08019356509327619,\n",
       "  -0.16744645371214598,\n",
       "  -0.058672088094527466,\n",
       "  -0.2810818179186083,\n",
       "  -0.19563055680487143,\n",
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       "  0.04483972384656422,\n",
       "  0.05890967800918092,\n",
       "  0.09433587789796988]]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apex_demeaned = compute_stocks_demeaned(apex_port, apex_avg_returns)\n",
    "apex_demeaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "524b2883",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.07556947585877077,\n",
       " 0.19538774534207753,\n",
       " 0.10534568390022855,\n",
       " 0.13251510174321746,\n",
       " 0.18208331536673422,\n",
       " 0.004300223831158395,\n",
       " 0.021314919855901344,\n",
       " 0.0054605394683716005,\n",
       " 0.019727325825254896,\n",
       " 0.002587190272164577,\n",
       " 0.10057114185250665,\n",
       " 0.052018204411551416,\n",
       " 0.04431935335923137,\n",
       " 0.058799778912831294]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apex_asset_weights = list(df['Allocation %']/100)\n",
    "apex_asset_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e07122d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_return = portfolio.dot(apex_asset_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "14b1f1ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AZRE</th>\n",
       "      <th>CRSR</th>\n",
       "      <th>BATRA</th>\n",
       "      <th>LOVE</th>\n",
       "      <th>PD</th>\n",
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       "      <th>SQQQ</th>\n",
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       "      <th>PLTR</th>\n",
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       "      <td>0.078863</td>\n",
       "      <td>0.660430</td>\n",
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       "      <td>-0.393857</td>\n",
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       "      <td>0.211878</td>\n",
       "      <td>0.334267</td>\n",
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       "    <tr>\n",
       "      <th>PD</th>\n",
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       "      <td>0.294247</td>\n",
       "      <td>0.079091</td>\n",
       "      <td>0.402035</td>\n",
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       "      <th>PSQ</th>\n",
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       "      <td>-0.156671</td>\n",
       "      <td>-0.042690</td>\n",
       "      <td>-0.182669</td>\n",
       "      <td>-0.198773</td>\n",
       "      <td>0.114987</td>\n",
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       "      <th>SARK</th>\n",
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       "      <td>-0.083079</td>\n",
       "      <td>-0.394048</td>\n",
       "      <td>-0.476147</td>\n",
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       "      <td>-0.131132</td>\n",
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       "      <td>-0.097998</td>\n",
       "      <td>-0.393857</td>\n",
       "      <td>-0.415375</td>\n",
       "      <td>0.256029</td>\n",
       "      <td>0.427870</td>\n",
       "      <td>0.201637</td>\n",
       "      <td>0.769805</td>\n",
       "      <td>0.607642</td>\n",
       "      <td>-0.378080</td>\n",
       "      <td>-0.165397</td>\n",
       "      <td>-0.245072</td>\n",
       "      <td>-0.365363</td>\n",
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       "      <th>PLTR</th>\n",
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       "      <td>0.251540</td>\n",
       "      <td>0.077862</td>\n",
       "      <td>0.326225</td>\n",
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       "      <td>0.168573</td>\n",
       "      <td>0.350449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MTNOY</th>\n",
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       "      <td>0.028023</td>\n",
       "      <td>0.115567</td>\n",
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       "      <td>0.229627</td>\n",
       "      <td>0.087086</td>\n",
       "      <td>0.118147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALK</th>\n",
       "      <td>0.135383</td>\n",
       "      <td>0.142012</td>\n",
       "      <td>0.056083</td>\n",
       "      <td>0.211878</td>\n",
       "      <td>0.179634</td>\n",
       "      <td>-0.105218</td>\n",
       "      <td>-0.195590</td>\n",
       "      <td>-0.081912</td>\n",
       "      <td>-0.314289</td>\n",
       "      <td>-0.245072</td>\n",
       "      <td>0.168573</td>\n",
       "      <td>0.087086</td>\n",
       "      <td>0.199274</td>\n",
       "      <td>0.182056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INMD</th>\n",
       "      <td>0.257398</td>\n",
       "      <td>0.260127</td>\n",
       "      <td>0.072171</td>\n",
       "      <td>0.334267</td>\n",
       "      <td>0.366652</td>\n",
       "      <td>-0.165395</td>\n",
       "      <td>-0.381357</td>\n",
       "      <td>-0.120943</td>\n",
       "      <td>-0.493913</td>\n",
       "      <td>-0.365363</td>\n",
       "      <td>0.350449</td>\n",
       "      <td>0.118147</td>\n",
       "      <td>0.182056</td>\n",
       "      <td>0.488537</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           AZRE      CRSR     BATRA      LOVE        PD       PSQ      SARK  \\\n",
       "AZRE   1.956830  0.229506  0.076874  0.234356  0.238146 -0.135204 -0.317783   \n",
       "CRSR   0.229506  0.402802  0.058418  0.297898  0.294247 -0.156671 -0.315945   \n",
       "BATRA  0.076874  0.058418  0.048550  0.078863  0.079091 -0.042690 -0.083079   \n",
       "LOVE   0.234356  0.297898  0.078863  0.660430  0.402035 -0.182669 -0.394048   \n",
       "PD     0.238146  0.294247  0.079091  0.402035  0.577537 -0.198773 -0.476147   \n",
       "PSQ   -0.135204 -0.156671 -0.042690 -0.182669 -0.198773  0.114987  0.202696   \n",
       "SARK  -0.317783 -0.315945 -0.083079 -0.394048 -0.476147  0.202696  0.508724   \n",
       "SH    -0.102664 -0.118858 -0.032615 -0.131132 -0.137639  0.085126  0.141164   \n",
       "SQQQ  -0.410752 -0.467522 -0.128167 -0.544778 -0.594742  0.344233  0.609638   \n",
       "SPXU  -0.311349 -0.356755 -0.097998 -0.393857 -0.415375  0.256029  0.427870   \n",
       "PLTR   0.277291  0.251540  0.077862  0.326225  0.436814 -0.180856 -0.435162   \n",
       "MTNOY  0.113939  0.104602  0.028023  0.115567  0.083988 -0.064465 -0.106518   \n",
       "ALK    0.135383  0.142012  0.056083  0.211878  0.179634 -0.105218 -0.195590   \n",
       "INMD   0.257398  0.260127  0.072171  0.334267  0.366652 -0.165395 -0.381357   \n",
       "\n",
       "             SH      SQQQ      SPXU      PLTR     MTNOY       ALK      INMD  \n",
       "AZRE  -0.102664 -0.410752 -0.311349  0.277291  0.113939  0.135383  0.257398  \n",
       "CRSR  -0.118858 -0.467522 -0.356755  0.251540  0.104602  0.142012  0.260127  \n",
       "BATRA -0.032615 -0.128167 -0.097998  0.077862  0.028023  0.056083  0.072171  \n",
       "LOVE  -0.131132 -0.544778 -0.393857  0.326225  0.115567  0.211878  0.334267  \n",
       "PD    -0.137639 -0.594742 -0.415375  0.436814  0.083988  0.179634  0.366652  \n",
       "PSQ    0.085126  0.344233  0.256029 -0.180856 -0.064465 -0.105218 -0.165395  \n",
       "SARK   0.141164  0.609638  0.427870 -0.435162 -0.106518 -0.195590 -0.381357  \n",
       "SH     0.067191  0.255048  0.201637 -0.125696 -0.055346 -0.081912 -0.120943  \n",
       "SQQQ   0.255048  1.036703  0.769805 -0.537335 -0.194522 -0.314289 -0.493913  \n",
       "SPXU   0.201637  0.769805  0.607642 -0.378080 -0.165397 -0.245072 -0.365363  \n",
       "PLTR  -0.125696 -0.537335 -0.378080  0.580728  0.082118  0.168573  0.350449  \n",
       "MTNOY -0.055346 -0.194522 -0.165397  0.082118  0.229627  0.087086  0.118147  \n",
       "ALK   -0.081912 -0.314289 -0.245072  0.168573  0.087086  0.199274  0.182056  \n",
       "INMD  -0.120943 -0.493913 -0.365363  0.350449  0.118147  0.182056  0.488537  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variance_mat = portfolio.cov()*252\n",
    "variance_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "64cd7bfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2035430001384028"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_variance = np.transpose(apex_asset_weights)@variance_mat@apex_asset_weights\n",
    "portfolio_variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "98afd57c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45115740062466314"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_volitality = np.sqrt(portfolio_variance)\n",
    "portfolio_volitality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4cf0a589",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AZRE    NaN\n",
       "CRSR    NaN\n",
       "BATRA   NaN\n",
       "LOVE    NaN\n",
       "PD      NaN\n",
       "PSQ     NaN\n",
       "SARK    NaN\n",
       "SH      NaN\n",
       "SQQQ    NaN\n",
       "SPXU    NaN\n",
       "PLTR    NaN\n",
       "MTNOY   NaN\n",
       "ALK     NaN\n",
       "INMD    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "port_returns = []\n",
    "port_volitality = []\n",
    "port_weights = []\n",
    "\n",
    "num_assets = len(portfolio.columns)\n",
    "num_portfolios = 10000\n",
    "\n",
    "\n",
    "individual_rets = portfolio.resample('Y').last().pct_change().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d63dd19a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for port in range(num_portfolios):\n",
    "    weights = np.random.random(num_assets)\n",
    "    weights = weights/np.sum(weights)\n",
    "    port_weights.append(weights)\n",
    "    returns = np.dot(weights, individual_rets)\n",
    "    port_returns.append(returns)\n",
    "    \n",
    "    \n",
    "    var = variance_mat.mul(weights, axis = 0).mul(weights, axis =1).sum().sum()\n",
    "    sd = np.sqrt(var)\n",
    "    ann_sd = sd*np.sqrt(250)\n",
    "    port_volitality.append(ann_sd) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "13147749",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'Returns':port_returns, 'Volatility':port_volitality}\n",
    "for counter, symbol in enumerate(portfolio.columns.tolist()):\n",
    "    data[symbol+' weight'] = [w[counter] for w in port_weights]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "69bc2dd5",
   "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>Returns</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>AZRE weight</th>\n",
       "      <th>CRSR weight</th>\n",
       "      <th>BATRA weight</th>\n",
       "      <th>LOVE weight</th>\n",
       "      <th>PD weight</th>\n",
       "      <th>PSQ weight</th>\n",
       "      <th>SARK weight</th>\n",
       "      <th>SH weight</th>\n",
       "      <th>SQQQ weight</th>\n",
       "      <th>SPXU weight</th>\n",
       "      <th>PLTR weight</th>\n",
       "      <th>MTNOY weight</th>\n",
       "      <th>ALK weight</th>\n",
       "      <th>INMD weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.338540</td>\n",
       "      <td>0.032047</td>\n",
       "      <td>0.055957</td>\n",
       "      <td>0.062673</td>\n",
       "      <td>0.067073</td>\n",
       "      <td>0.100697</td>\n",
       "      <td>0.080730</td>\n",
       "      <td>0.014432</td>\n",
       "      <td>0.082921</td>\n",
       "      <td>0.101062</td>\n",
       "      <td>0.103262</td>\n",
       "      <td>0.116130</td>\n",
       "      <td>0.115938</td>\n",
       "      <td>0.054247</td>\n",
       "      <td>0.012831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.551856</td>\n",
       "      <td>0.030251</td>\n",
       "      <td>0.077176</td>\n",
       "      <td>0.119967</td>\n",
       "      <td>0.054466</td>\n",
       "      <td>0.123329</td>\n",
       "      <td>0.039444</td>\n",
       "      <td>0.121962</td>\n",
       "      <td>0.010457</td>\n",
       "      <td>0.013297</td>\n",
       "      <td>0.085736</td>\n",
       "      <td>0.077761</td>\n",
       "      <td>0.124693</td>\n",
       "      <td>0.112577</td>\n",
       "      <td>0.008886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.520262</td>\n",
       "      <td>0.087229</td>\n",
       "      <td>0.138994</td>\n",
       "      <td>0.046133</td>\n",
       "      <td>0.023030</td>\n",
       "      <td>0.006608</td>\n",
       "      <td>0.081885</td>\n",
       "      <td>0.096904</td>\n",
       "      <td>0.160538</td>\n",
       "      <td>0.092635</td>\n",
       "      <td>0.022991</td>\n",
       "      <td>0.085777</td>\n",
       "      <td>0.012330</td>\n",
       "      <td>0.045838</td>\n",
       "      <td>0.099107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.646333</td>\n",
       "      <td>0.089874</td>\n",
       "      <td>0.096062</td>\n",
       "      <td>0.053644</td>\n",
       "      <td>0.063282</td>\n",
       "      <td>0.121182</td>\n",
       "      <td>0.028383</td>\n",
       "      <td>0.006164</td>\n",
       "      <td>0.101977</td>\n",
       "      <td>0.093072</td>\n",
       "      <td>0.012142</td>\n",
       "      <td>0.105821</td>\n",
       "      <td>0.038727</td>\n",
       "      <td>0.062293</td>\n",
       "      <td>0.127377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.187609</td>\n",
       "      <td>0.072913</td>\n",
       "      <td>0.103679</td>\n",
       "      <td>0.068964</td>\n",
       "      <td>0.102688</td>\n",
       "      <td>0.065555</td>\n",
       "      <td>0.113194</td>\n",
       "      <td>0.037804</td>\n",
       "      <td>0.027751</td>\n",
       "      <td>0.073513</td>\n",
       "      <td>0.057202</td>\n",
       "      <td>0.102226</td>\n",
       "      <td>0.041727</td>\n",
       "      <td>0.108523</td>\n",
       "      <td>0.024262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Returns  Volatility  AZRE weight  CRSR weight  BATRA weight  LOVE weight  \\\n",
       "0      NaN    2.338540     0.032047     0.055957      0.062673     0.067073   \n",
       "1      NaN    2.551856     0.030251     0.077176      0.119967     0.054466   \n",
       "2      NaN    2.520262     0.087229     0.138994      0.046133     0.023030   \n",
       "3      NaN    4.646333     0.089874     0.096062      0.053644     0.063282   \n",
       "4      NaN    3.187609     0.072913     0.103679      0.068964     0.102688   \n",
       "\n",
       "   PD weight  PSQ weight  SARK weight  SH weight  SQQQ weight  SPXU weight  \\\n",
       "0   0.100697    0.080730     0.014432   0.082921     0.101062     0.103262   \n",
       "1   0.123329    0.039444     0.121962   0.010457     0.013297     0.085736   \n",
       "2   0.006608    0.081885     0.096904   0.160538     0.092635     0.022991   \n",
       "3   0.121182    0.028383     0.006164   0.101977     0.093072     0.012142   \n",
       "4   0.065555    0.113194     0.037804   0.027751     0.073513     0.057202   \n",
       "\n",
       "   PLTR weight  MTNOY weight  ALK weight  INMD weight  \n",
       "0     0.116130      0.115938    0.054247     0.012831  \n",
       "1     0.077761      0.124693    0.112577     0.008886  \n",
       "2     0.085777      0.012330    0.045838     0.099107  \n",
       "3     0.105821      0.038727    0.062293     0.127377  \n",
       "4     0.102226      0.041727    0.108523     0.024262  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolios_V1 = pd.DataFrame(data)\n",
    "portfolios_V1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9467c831",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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