Portfolio Optimization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "from datetime import datetime, timedelta\n",
    "import pathlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.optimize as sci_opt\n",
    "\n",
    "from pprint import pprint\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# Set some display options for Pandas.\n",
    "%config InlineBackend.figure_format ='retina'\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "pd.set_option('expand_frame_repr', False)\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Fetching S&P 500 Companies:\n",
    "sp500_companies = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')[0]\n",
    "sp500_companies['Symbol'] = sp500_companies['Symbol'].replace({'BRK.B': 'BRK-B', 'BF.B': 'BF-B'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Security</th>\n",
       "      <th>GICS Sector</th>\n",
       "      <th>GICS Sub-Industry</th>\n",
       "      <th>Headquarters Location</th>\n",
       "      <th>Date added</th>\n",
       "      <th>CIK</th>\n",
       "      <th>Founded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MMM</td>\n",
       "      <td>3M</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Industrial Conglomerates</td>\n",
       "      <td>Saint Paul, Minnesota</td>\n",
       "      <td>1957-03-04</td>\n",
       "      <td>66740</td>\n",
       "      <td>1902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AOS</td>\n",
       "      <td>A. O. Smith</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Building Products</td>\n",
       "      <td>Milwaukee, Wisconsin</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>91142</td>\n",
       "      <td>1916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABT</td>\n",
       "      <td>Abbott</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Health Care Equipment</td>\n",
       "      <td>North Chicago, Illinois</td>\n",
       "      <td>1957-03-04</td>\n",
       "      <td>1800</td>\n",
       "      <td>1888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABBV</td>\n",
       "      <td>AbbVie</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>North Chicago, Illinois</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1551152</td>\n",
       "      <td>2013 (1888)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACN</td>\n",
       "      <td>Accenture</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>IT Consulting &amp; Other Services</td>\n",
       "      <td>Dublin, Ireland</td>\n",
       "      <td>2011-07-06</td>\n",
       "      <td>1467373</td>\n",
       "      <td>1989</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>YUM</td>\n",
       "      <td>Yum! Brands</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>Restaurants</td>\n",
       "      <td>Louisville, Kentucky</td>\n",
       "      <td>1997-10-06</td>\n",
       "      <td>1041061</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>ZBRA</td>\n",
       "      <td>Zebra Technologies</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Electronic Equipment &amp; Instruments</td>\n",
       "      <td>Lincolnshire, Illinois</td>\n",
       "      <td>2019-12-23</td>\n",
       "      <td>877212</td>\n",
       "      <td>1969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>ZBH</td>\n",
       "      <td>Zimmer Biomet</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Health Care Equipment</td>\n",
       "      <td>Warsaw, Indiana</td>\n",
       "      <td>2001-08-07</td>\n",
       "      <td>1136869</td>\n",
       "      <td>1927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>ZION</td>\n",
       "      <td>Zions Bancorporation</td>\n",
       "      <td>Financials</td>\n",
       "      <td>Regional Banks</td>\n",
       "      <td>Salt Lake City, Utah</td>\n",
       "      <td>2001-06-22</td>\n",
       "      <td>109380</td>\n",
       "      <td>1873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>ZTS</td>\n",
       "      <td>Zoetis</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Pharmaceuticals</td>\n",
       "      <td>Parsippany, New Jersey</td>\n",
       "      <td>2013-06-21</td>\n",
       "      <td>1555280</td>\n",
       "      <td>1952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>503 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Symbol              Security             GICS Sector                   GICS Sub-Industry    Headquarters Location  Date added      CIK      Founded\n",
       "0      MMM                    3M             Industrials            Industrial Conglomerates    Saint Paul, Minnesota  1957-03-04    66740         1902\n",
       "1      AOS           A. O. Smith             Industrials                   Building Products     Milwaukee, Wisconsin  2017-07-26    91142         1916\n",
       "2      ABT                Abbott             Health Care               Health Care Equipment  North Chicago, Illinois  1957-03-04     1800         1888\n",
       "3     ABBV                AbbVie             Health Care                       Biotechnology  North Chicago, Illinois  2012-12-31  1551152  2013 (1888)\n",
       "4      ACN             Accenture  Information Technology      IT Consulting & Other Services          Dublin, Ireland  2011-07-06  1467373         1989\n",
       "..     ...                   ...                     ...                                 ...                      ...         ...      ...          ...\n",
       "498    YUM           Yum! Brands  Consumer Discretionary                         Restaurants     Louisville, Kentucky  1997-10-06  1041061         1997\n",
       "499   ZBRA    Zebra Technologies  Information Technology  Electronic Equipment & Instruments   Lincolnshire, Illinois  2019-12-23   877212         1969\n",
       "500    ZBH         Zimmer Biomet             Health Care               Health Care Equipment          Warsaw, Indiana  2001-08-07  1136869         1927\n",
       "501   ZION  Zions Bancorporation              Financials                      Regional Banks     Salt Lake City, Utah  2001-06-22   109380         1873\n",
       "502    ZTS                Zoetis             Health Care                     Pharmaceuticals   Parsippany, New Jersey  2013-06-21  1555280         1952\n",
       "\n",
       "[503 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500_companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         0\n",
      "0              Industrials\n",
      "1              Health Care\n",
      "2   Information Technology\n",
      "3                Utilities\n",
      "4               Financials\n",
      "5                Materials\n",
      "6   Consumer Discretionary\n",
      "7              Real Estate\n",
      "8   Communication Services\n",
      "9         Consumer Staples\n",
      "10                  Energy\n"
     ]
    }
   ],
   "source": [
    "# Dropping NaN values\n",
    "sp500_companies.dropna(inplace=True)\n",
    "## Getting Unique Sectors and Dates:\n",
    "unique_sectors = sp500_companies['GICS Sector'].unique()\n",
    "end_date = datetime.today().strftime('%Y-%m-%d')\n",
    "start_date = (datetime.today() - timedelta(days=90)).strftime('%Y-%m-%d')\n",
    "unique_sectors_df = pd.DataFrame(unique_sectors)\n",
    "print(unique_sectors_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_of_sectors = len(unique_sectors)\n",
    "len(unique_sectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  78 of 78 completed\n",
      "[*********************100%%**********************]  64 of 64 completed\n",
      "[*********************100%%**********************]  64 of 64 completed\n",
      "[*********************100%%**********************]  30 of 30 completed\n",
      "[*********************100%%**********************]  72 of 72 completed\n",
      "[*********************100%%**********************]  28 of 28 completed\n",
      "[*********************100%%**********************]  53 of 53 completed\n",
      "[*********************100%%**********************]  31 of 31 completed\n",
      "[*********************100%%**********************]  22 of 22 completed\n",
      "[*********************100%%**********************]  38 of 38 completed\n",
      "[*********************100%%**********************]  23 of 23 completed\n",
      "           Date   Avg_Close            Sector\n",
      "659  2024-02-01   86.093043            Energy\n",
      "658  2024-01-31   86.108696            Energy\n",
      "657  2024-01-30   87.736087            Energy\n",
      "656  2024-01-29   86.452325            Energy\n",
      "655  2024-01-26   86.725390            Energy\n",
      "..          ...         ...               ...\n",
      "564  2023-12-11  101.674804  Consumer Staples\n",
      "563  2023-12-08  100.676050  Consumer Staples\n",
      "562  2023-12-07  101.477259  Consumer Staples\n",
      "561  2023-12-06  100.919538  Consumer Staples\n",
      "560  2023-12-05  100.918330  Consumer Staples\n",
      "\n",
      "[100 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "## Fetching Historical Prices and Calculating Averages:\n",
    "## The code fetches historical closing prices for each company in each sector and calculates the average closing price for the sector.\n",
    "# Loop through each sector and download closing prices\n",
    "all_avg_prices = []\n",
    "all_dates = []\n",
    "all_sectors = []\n",
    "for sector in unique_sectors:\n",
    "    companies = sp500_companies[sp500_companies['GICS Sector'] == sector]['Symbol'].tolist()\n",
    "    prices = yf.download(companies, start=start_date, end=end_date)['Adj Close']\n",
    "    avg_prices = prices.mean(axis=1)\n",
    "    # Append the data\n",
    "    all_avg_prices.extend(avg_prices.tolist())\n",
    "    all_dates.extend([date.date() for date in prices.index])\n",
    "    all_sectors.extend([sector] * len(avg_prices))\n",
    "\n",
    "# Create a DataFrame\n",
    "sp500_sectors_avg_prices = pd.DataFrame({\n",
    "    'Date': all_dates,\n",
    "    'Avg_Close': all_avg_prices,\n",
    "    'Sector': all_sectors\n",
    "})\n",
    "\n",
    "#sp500_sectors_avg_prices = sp500_sectors_avg_prices.pivot(index='Date', columns='Sector', values='Avg_Close')\n",
    "# Reverse the order of the DataFrame by dates\n",
    "sp500_sectors_avg_prices = sp500_sectors_avg_prices[::-1]\n",
    "\n",
    "print(sp500_sectors_avg_prices.head(100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sector      Communication Services  Consumer Discretionary  Consumer Staples     Energy  Financials  Health Care  Industrials  Information Technology   Materials  Real Estate  Utilities\n",
      "Date                                                                                                                                                                                     \n",
      "2023-11-06              107.379353              384.055126         96.841980  86.775185  143.068032   212.388183   176.073544              224.918651  124.745339   112.064622  63.856103\n",
      "2023-11-07              107.619845              388.411453         97.102875  84.800603  142.851109   212.798488   176.089518              226.842666  122.782611   111.098683  63.352058\n",
      "2023-11-08              107.981790              386.795342         96.718323  83.600292  143.246391   211.858423   176.464457              228.190706  123.598667   111.901380  62.954094\n",
      "2023-11-09              107.596075              384.655803         96.295157  83.279244  143.067818   208.173712   176.818573              227.108806  122.903849   110.392083  62.447700\n",
      "2023-11-10              109.280326              392.102928         96.963614  84.334537  144.614433   210.069886   179.474525              233.550783  124.219691   111.598370  62.663849\n"
     ]
    }
   ],
   "source": [
    "sp500_sectors_avg_prices = sp500_sectors_avg_prices.pivot(\n",
    "    index='Date',\n",
    "    columns='Sector',\n",
    "    values='Avg_Close'\n",
    ")\n",
    "print(sp500_sectors_avg_prices.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sector\n",
      "Communication Services    0\n",
      "Consumer Discretionary    0\n",
      "Consumer Staples          0\n",
      "Energy                    0\n",
      "Financials                0\n",
      "Health Care               0\n",
      "Industrials               0\n",
      "Information Technology    0\n",
      "Materials                 0\n",
      "Real Estate               0\n",
      "Utilities                 0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "sp500_sectors_avg_prices.dropna(inplace=True)\n",
    "print(sp500_sectors_avg_prices.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log Returns:\n",
      "Sector      Communication Services  Consumer Discretionary  Consumer Staples    Energy  Financials  Health Care  Industrials  Information Technology  Materials  Real Estate  Utilities\n",
      "Date                                                                                                                                                                                   \n",
      "2023-12-26                0.006612                0.001971          0.005255  0.010995    0.003017     0.004256     0.007121                0.007045   0.004890     0.007888   0.006605\n",
      "2023-12-21                0.009171                0.010540          0.006759  0.004001    0.011064     0.015268     0.012647                0.014892   0.010197     0.010102   0.002624\n",
      "2023-12-19                0.010264                0.010588          0.005005  0.011440    0.005798     0.008706     0.006324                0.002996   0.008073     0.005542   0.005890\n",
      "2023-12-13                0.017362                0.016282          0.021183  0.014509    0.015064     0.028291     0.012848                0.010128   0.016772     0.033097   0.034392\n",
      "2023-12-11                0.002404                0.010005          0.009872  0.002508    0.009847     0.009959     0.009944                0.021433   0.006881     0.000194   0.007153\n"
     ]
    }
   ],
   "source": [
    "sp500_sectors_avg_prices.to_excel('sp500_sectors_avg_prices.xlsx', index=True)\n",
    "# Calculate the Log of returns.\n",
    "log_return = np.log(1 +sp500_sectors_avg_prices.pct_change().iloc[::-1])\n",
    "\n",
    "# Drop rows with negative values\n",
    "log_return = log_return[(log_return > 0).all(axis=1)]\n",
    "\n",
    "\n",
    "# Generate Random Weights.\n",
    "random_weights = np.array(np.random.random(number_of_sectors))\n",
    "\n",
    "# Generate the Rebalance Weights, these should equal 1.\n",
    "rebalance_weights = random_weights / np.sum(random_weights)\n",
    "print('Log Returns:')\n",
    "print(log_return.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sector\n",
      "Communication Services    0\n",
      "Consumer Discretionary    0\n",
      "Consumer Staples          0\n",
      "Energy                    0\n",
      "Financials                0\n",
      "Health Care               0\n",
      "Industrials               0\n",
      "Information Technology    0\n",
      "Materials                 0\n",
      "Real Estate               0\n",
      "Utilities                 0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print((sp500_sectors_avg_prices == 0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of negative log returns: 0.00%\n"
     ]
    }
   ],
   "source": [
    "log_return.to_excel('log_return.xlsx', index=True)\n",
    "# Calculate the percentage of negative log returns\n",
    "percentage_negative_returns = (log_return < 0).mean().mean() * 100\n",
    "\n",
    "print(f\"Percentage of negative log returns: {percentage_negative_returns:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================\n",
      "PORTFOLIO WEIGHTS:\n",
      "--------------------------------------------------------------------------------\n",
      "    random_weights  rebalance_weights\n",
      "0         0.560410           0.120796\n",
      "1         0.725787           0.156443\n",
      "2         0.173865           0.037476\n",
      "3         0.624587           0.134629\n",
      "4         0.262699           0.056625\n",
      "5         0.219882           0.047395\n",
      "6         0.267079           0.057569\n",
      "7         0.610351           0.131561\n",
      "8         0.235035           0.050662\n",
      "9         0.571922           0.123277\n",
      "10        0.387694           0.083567\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "================================================================================\n",
      "PORTFOLIO METRICS:\n",
      "--------------------------------------------------------------------------------\n",
      "   Expected Portfolio Returns  Expected Portfolio Volatility  Portfolio Sharpe Ratio\n",
      "0                    0.760516                       0.114729                6.628787\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Calculate the Expected Returns, annualize it by multiplying it by `252`.\n",
    "risk_free_rate = .01\n",
    "exp_ret = np.sum(((log_return.mean()-risk_free_rate) * rebalance_weights) * 252)\n",
    "\n",
    "# Calculate the Expected Volatility, annualize it by multiplying it by `252`.\n",
    "exp_vol = np.sqrt(\n",
    "np.dot(\n",
    "    rebalance_weights.T,\n",
    "    np.dot(\n",
    "        log_return.cov() * 252,\n",
    "        rebalance_weights\n",
    "    )\n",
    ")\n",
    ")\n",
    "\n",
    "# Calculate the Sharpe Ratio.\n",
    "sharpe_ratio = exp_ret / exp_vol\n",
    "\n",
    "# Put the weights into a data frame to see them better.\n",
    "weights_df = pd.DataFrame(data={\n",
    "'random_weights': random_weights,\n",
    "'rebalance_weights': rebalance_weights\n",
    "})\n",
    "print('')\n",
    "print('='*80)\n",
    "print('PORTFOLIO WEIGHTS:')\n",
    "print('-'*80)\n",
    "print(weights_df)\n",
    "print('-'*80)\n",
    "\n",
    "# Do the same with the other metrics.\n",
    "metrics_df = pd.DataFrame(data={\n",
    "    'Expected Portfolio Returns': exp_ret,\n",
    "    'Expected Portfolio Volatility': exp_vol,\n",
    "    'Portfolio Sharpe Ratio': sharpe_ratio\n",
    "}, index=[0])\n",
    "\n",
    "print('')\n",
    "print('='*80)\n",
    "print('PORTFOLIO METRICS:')\n",
    "print('-'*80)\n",
    "print(metrics_df)\n",
    "print('-'*80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================\n",
      "SIMULATIONS RESULT:\n",
      "--------------------------------------------------------------------------------\n",
      "================================================================================\n",
      "PORTFOLIO WEIGHTS:\n",
      "--------------------------------------------------------------------------------\n",
      "0    [0.15590447184185768, 0.057384483665105124, 0.0781244636046349, 0.05575516447099274, 0.10186732518018163, 0.039045293106263805, 0.09102103948435449, 0.004378654832128958, 0.12108445370491619, 0.12114030864800975, 0.17429434146155467]\n",
      "1          [0.0406975440839412, 0.1287752558441318, 0.10414383001281226, 0.00898338858973777, 0.166162598037402, 0.0814400365567108, 0.11634034923300363, 0.1629342630091322, 0.13277675723275084, 0.037334695933648604, 0.020411281466729055]\n",
      "2        [0.0743085140541715, 0.16939988988021168, 0.09520914477623667, 0.02099204648845927, 0.03311527881131096, 0.1445137310238473, 0.09057225267335094, 0.04235756092519942, 0.16168062007967807, 0.10529748225493517, 0.06255347903259902]\n",
      "3     [0.09797647858772943, 0.008321053928051525, 0.05919647671061805, 0.06792984627235747, 0.09193566524685264, 0.1396634017471446, 0.059620239670493576, 0.14674988432178288, 0.14882389249512287, 0.09804686302386251, 0.08173619799598443]\n",
      "4       [0.14744501070535543, 0.06895463148812409, 0.10970252535547113, 0.023635014313103376, 0.04337838241287972, 0.18145597438415378, 0.0662200077621848, 0.14981202112489878, 0.0237763274462241, 0.1734453277532435, 0.012174777254361467]\n",
      "Name: Portfolio Weights, dtype: object\n",
      "--------------------------------------------------------------------------------\n",
      "================================================================================\n",
      "PORTFOLIO METRICS:\n",
      "--------------------------------------------------------------------------------\n",
      "    Returns  Volatility  Sharpe Ratio\n",
      "0  0.662358    0.133582      4.958441\n",
      "1  0.697880    0.102517      6.807489\n",
      "2  0.746478    0.120846      6.177125\n",
      "3  0.757165    0.115093      6.578734\n",
      "4  0.838348    0.117010      7.164756\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Initialize the components, to run a Monte Carlo Simulation.\n",
    "\n",
    "# We will run 5000 iterations.\n",
    "num_of_portfolios = 20000\n",
    "\n",
    "# Prep an array to store the weights as they are generated, 5000 iterations for each of our 4 symbols.\n",
    "all_weights = np.zeros((num_of_portfolios, number_of_sectors))\n",
    "\n",
    "# Prep an array to store the returns as they are generated, 5000 possible return values.\n",
    "ret_arr = np.zeros(num_of_portfolios)\n",
    "\n",
    "# Prep an array to store the volatilities as they are generated, 5000 possible volatility values.\n",
    "vol_arr = np.zeros(num_of_portfolios)\n",
    "\n",
    "# Prep an array to store the sharpe ratios as they are generated, 5000 possible Sharpe Ratios.\n",
    "sharpe_arr = np.zeros(num_of_portfolios)\n",
    "\n",
    "# Start the simulations.\n",
    "for ind in range(num_of_portfolios):\n",
    "\n",
    "    # First, calculate the weights.\n",
    "    weights = np.array(np.random.random(number_of_sectors))\n",
    "    weights = weights / np.sum(weights)\n",
    "\n",
    "    # Add the weights, to the `weights_arrays`.\n",
    "    all_weights[ind, :] = weights\n",
    "\n",
    "    # Calculate the expected log returns, and add them to the `returns_array`.\n",
    "    ret_arr[ind] = np.sum(((log_return.mean()-risk_free_rate) * weights) * 252)\n",
    "\n",
    "    # Calculate the volatility, and add them to the `volatility_array`.\n",
    "    vol_arr[ind] = np.sqrt(\n",
    "        np.dot(weights.T, np.dot(log_return.cov() * 252, weights)))\n",
    "\n",
    "\n",
    "    # Calculate the Sharpe Ratio and Add it to the `sharpe_ratio_array`.\n",
    "    sharpe_arr[ind] = ret_arr[ind]/vol_arr[ind]\n",
    "\n",
    "# Let's create our \"Master Data Frame\", with the weights, the returns, the volatility, and the Sharpe Ratio\n",
    "simulations_data = [ret_arr, vol_arr, sharpe_arr, all_weights]\n",
    "\n",
    "# Create a DataFrame from it, then Transpose it so it looks like our original one.\n",
    "simulations_df = pd.DataFrame(data=simulations_data).T\n",
    "\n",
    "# Give the columns the Proper Names.\n",
    "simulations_df.columns = [\n",
    "    'Returns',\n",
    "    'Volatility',\n",
    "    'Sharpe Ratio',\n",
    "    'Portfolio Weights'\n",
    "]\n",
    "\n",
    "# Make sure the data types are correct, we don't want our floats to be strings.\n",
    "simulations_df = simulations_df.infer_objects()\n",
    "\n",
    "# Print out the results.\n",
    "print('')\n",
    "print('='*80)\n",
    "print('SIMULATIONS RESULT:')\n",
    "print('-'*80)\n",
    "\n",
    "# Print PORTFOLIO WEIGHTS\n",
    "print('='*80)\n",
    "print('PORTFOLIO WEIGHTS:')\n",
    "print('-'*80)\n",
    "print(simulations_df['Portfolio Weights'].head())\n",
    "print('-'*80)\n",
    "\n",
    "# Print PORTFOLIO METRICS\n",
    "print('='*80)\n",
    "print('PORTFOLIO METRICS:')\n",
    "print('-'*80)\n",
    "print(simulations_df[['Returns', 'Volatility', 'Sharpe Ratio']].head())\n",
    "print('-'*80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================\n",
      "MAX SHARPE RATIO:\n",
      "--------------------------------------------------------------------------------\n",
      "Returns: 0.856399995066738\n",
      "Volatility: 0.09493854008923967\n",
      "Sharpe Ratio: 9.02057261741907\n",
      "Portfolio Weights: [0.05549935 0.05417608 0.01885594 0.00298535 0.05649435 0.31488822\n",
      " 0.16272199 0.25708486 0.03096386 0.00626343 0.04006656]\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "================================================================================\n",
      "MIN VOLATILITY:\n",
      "--------------------------------------------------------------------------------\n",
      "Returns: 0.36320929008347885\n",
      "Volatility: 0.07225019288204404\n",
      "Sharpe Ratio: 5.0271047812489\n",
      "Portfolio Weights: [0.14749584 0.00655644 0.04340685 0.27679508 0.15079983 0.03663565\n",
      " 0.15501133 0.09160337 0.07666837 0.0144301  0.00059715]\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Return the Max Sharpe Ratio from the run.\n",
    "max_sharpe_ratio = simulations_df.loc[simulations_df['Sharpe Ratio'].idxmax()]\n",
    "\n",
    "# Return the Min Volatility from the run.\n",
    "min_volatility = simulations_df.loc[simulations_df['Volatility'].idxmin()]\n",
    "\n",
    "print('\\n' + '='*80)\n",
    "print('MAX SHARPE RATIO:')\n",
    "print('-'*80)\n",
    "print(f'Returns: {max_sharpe_ratio[\"Returns\"]}')\n",
    "print(f'Volatility: {max_sharpe_ratio[\"Volatility\"]}')\n",
    "print(f'Sharpe Ratio: {max_sharpe_ratio[\"Sharpe Ratio\"]}')\n",
    "print(f'Portfolio Weights: {max_sharpe_ratio[\"Portfolio Weights\"]}')\n",
    "print('-'*80)\n",
    "\n",
    "print('\\n' + '='*80)\n",
    "print('MIN VOLATILITY:')\n",
    "print('-'*80)\n",
    "print(f'Returns: {min_volatility[\"Returns\"]}')\n",
    "print(f'Volatility: {min_volatility[\"Volatility\"]}')\n",
    "print(f'Sharpe Ratio: {min_volatility[\"Sharpe Ratio\"]}')\n",
    "print(f'Portfolio Weights: {min_volatility[\"Portfolio Weights\"]}')\n",
    "print('-'*80)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DICTIONARIES WITH WEIGHTS\n",
    "\n",
    "maxsharpedict = { \"Communication Services\": 0.181, \"Consumer Discretionary\": 0.0458,\n",
    "                  \"Consumer Staples\": 0.0105, \"Energy\": 0.0437, \"Financials\": 0.0043,\n",
    "                  \"Health Care\": 0.0278, \"Industrials\": 0.0619, \"Information Technology\": 0.2273,\n",
    "                  \"Materials\": 0.1414, \"Real Estate\": 0.2024,\"Utilites\": 0.0539 }\n",
    "\n",
    "minvoldict = { \"Communication Services\": 0.2683, \"Consumer Discretionary\": 0.0288,\n",
    "               \"Consumer Staples\": 0.235, \"Energy\": 0.0597, \"Financials\": 0.007,\n",
    "               \"Health Care\": 0.0763, \"Industrials\": 0.044, \"Information Technology\": 0.0356,\n",
    "               \"Materials\": 0.0141, \"Real Estate\": 0.034, \"Utilites\": 0.1973 }"
   ]
  }
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