portfolio_optimizer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8d2969f3",
   "metadata": {},
   "source": [
    "## Accessing the Stock Price Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d25321db",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas-datareader\n",
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      "Installing collected packages: pandas-datareader\n",
      "Successfully installed pandas-datareader-0.10.0\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas-datareader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "76399e5b",
   "metadata": {
    "collapsed": true
   },
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      "Installing collected packages: pytz, multitasking, lxml, frozendict, beautifulsoup4, yfinance\n",
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      "    Found existing installation: pytz 2021.3\n",
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      "Successfully installed beautifulsoup4-4.12.0 frozendict-2.3.6 lxml-4.9.2 multitasking-0.0.11 pytz-2023.2 yfinance-0.2.14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "conda-repo-cli 1.0.4 requires pathlib, which is not installed.\n"
     ]
    }
   ],
   "source": [
    "!pip install yfinance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "79720421",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas_datareader import data as pdr\n",
    "import yfinance as yf\n",
    "import datetime\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "9dc9a26e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "8ff18429",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = datetime.datetime(2019,1,1)\n",
    "end = datetime.datetime(2022,12,31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "0da0d771",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fucntion that gets the stock data\n",
    "def get_stock(ticker):\n",
    "    yf.pdr_override()\n",
    "    data = pdr.get_data_yahoo(f\"{ticker}\",start,end)\n",
    "    data[f'{ticker}'] = data[\"Close\"]\n",
    "    data = data[[f'{ticker}']] \n",
    "    data.reset_index(inplace=True)\n",
    "    data.index = data.index.astype(int)\n",
    "    return data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "25efb6f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pfizer = get_stock(\"PFE\")\n",
    "# jnj = get_stock(\"JNJ\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fac78ac",
   "metadata": {},
   "source": [
    "## Stocks to be pulled are:\n",
    "#### Healthcare : Moderna (MRNA), Pfizer (PFE), Johnson & Johnson (JNJ)\n",
    "#### Tech : Google (GOOGL), Facebook/META (META), Apple (AAPL)\n",
    "#### Retail : Costco (COST), Walmart (WMT), Kroger Co (KR)\n",
    "#### Finance : JPMorgan Chase & Co (JPM), Bank of America (BAC), HSBC Holding (HSBC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "15f560c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import reduce\n",
    "\n",
    "def combine_stocks(tickers):\n",
    "    data_frames = []\n",
    "    for i in tickers:\n",
    "        data_frames.append(get_stock(i))\n",
    "    df_merged = reduce(lambda  left,right: pd.merge(left,right,on=['Date'], how='outer'), data_frames)\n",
    "    print(df_merged.head())\n",
    "    return df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "bcc7d40f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[*********************100%***********************]  1 of 1 completed\n",
      "        Date       MRNA        PFE         JNJ      GOOGL        META  \\\n",
      "0 2019-01-02  15.330000  41.034157  127.750000  52.734001  135.679993   \n",
      "1 2019-01-03  15.500000  39.886147  125.720001  51.273499  131.740005   \n",
      "2 2019-01-04  16.959999  40.796963  127.830002  53.903500  137.949997   \n",
      "3 2019-01-07  16.270000  41.015179  127.010002  53.796001  138.050003   \n",
      "4 2019-01-08  16.950001  41.204933  129.960007  54.268501  142.529999   \n",
      "\n",
      "        AAPL        COST        WMT         KR         JPM        BAC  \\\n",
      "0  39.480000  204.759995  93.339996  27.299999   99.309998  24.959999   \n",
      "1  35.547501  200.419998  92.860001  27.350000   97.110001  24.559999   \n",
      "2  37.064999  206.240005  93.440002  27.660000  100.690002  25.580000   \n",
      "3  36.982498  207.000000  94.540001  27.920000  100.760002  25.559999   \n",
      "4  37.687500  208.550003  95.199997  28.459999  100.570000  25.510000   \n",
      "\n",
      "        HSBC  \n",
      "0  40.880001  \n",
      "1  40.430000  \n",
      "2  41.610001  \n",
      "3  41.049999  \n",
      "4  41.160000  \n"
     ]
    }
   ],
   "source": [
    "stocks = [\"MRNA\", \"PFE\", \"JNJ\", \"GOOGL\", \n",
    "          \"META\", \"AAPL\", \"COST\", \"WMT\", \"KR\", \"JPM\", \n",
    "          \"BAC\", \"HSBC\"]\n",
    "portfolio = combine_stocks(stocks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "d67d80a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio.to_csv(\"portfolio.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "a502ecd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio = pd.read_csv(\"portfolio.csv\", parse_dates=['Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26e88c06",
   "metadata": {},
   "source": [
    "# Mean Variance Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "277ec586",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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     "output_type": "stream",
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      "Collecting PyPortfolioOpt\n",
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      "Installing collected packages: numpy, qdldl, setuptools, scs, osqp, ecos, cvxpy, PyPortfolioOpt\n",
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      "    Found existing installation: numpy 1.19.2\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "spyder 5.1.5 requires pyqt5<5.13, which is not installed.\n",
      "spyder 5.1.5 requires pyqtwebengine<5.13, which is not installed.\n",
      "daal4py 2021.4.0 requires daal==2021.3.0, which is not installed.\n",
      "conda-repo-cli 1.0.4 requires pathlib, which is not installed.\n",
      "anaconda-project 0.10.2 requires ruamel-yaml, which is not installed.\n",
      "tensorflow 2.3.0 requires gast==0.3.3, but you have gast 0.4.0 which is incompatible.\n",
      "tensorflow 2.3.0 requires numpy<1.19.0,>=1.16.0, but you have numpy 1.22.4 which is incompatible.\n",
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      "astropy 4.3.post1 requires pyerfa>=1.7.3, but you have pyerfa 1.7.2 which is incompatible.\n"
     ]
    }
   ],
   "source": [
    "!pip install PyPortfolioOpt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "53584fab",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pypfopt.expected_returns import mean_historical_return\n",
    "from pypfopt.risk_models import CovarianceShrinkage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "c06a2632",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pypfopt import plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "fdf58ca9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# portfolio[['MRNA', 'PFE', 'JNJ', 'GOOGL', 'META', 'APPL', 'COST', 'WMT', 'KR', 'JPM', 'BAC', 'HSBC']] = portfolio[['MRNA', 'PFE', 'JNJ', 'GOOGL', 'META', 'AAPL', 'COST', 'WMT', 'KR', 'JPM', 'BAC', 'HSBC']].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "23ceb3b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "mu = mean_historical_return(portfolio[['MRNA', 'PFE', 'JNJ', 'GOOGL', 'META', 'AAPL', 'COST', 'WMT', 'KR', 'JPM', 'BAC', 'HSBC']])\n",
    "S = CovarianceShrinkage(portfolio[['MRNA', 'PFE', 'JNJ', 'GOOGL', 'META', 'AAPL', 'COST', 'WMT', 'KR', 'JPM', 'BAC', 'HSBC']]).ledoit_wolf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "363dc8b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'MRNA': 0.25786, 'PFE': 0.0, 'JNJ': 0.0, 'GOOGL': 0.0, 'META': 0.0, 'AAPL': 0.41782, 'COST': 0.25427, 'WMT': 0.0, 'KR': 0.07006, 'JPM': 0.0, 'BAC': 0.0, 'HSBC': 0.0}\n",
      "-----------------------------------\n",
      "Expected annual return: 43.0%\n",
      "Annual volatility: 30.7%\n",
      "Sharpe Ratio: 1.34\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.4302485476813618, 0.30677106892250045, 1.3373117260448144)"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pypfopt.efficient_frontier import EfficientFrontier\n",
    "\n",
    "ef = EfficientFrontier(mu,S)\n",
    "weights = ef.max_sharpe()\n",
    "\n",
    "cleaned_weights = ef.clean_weights()\n",
    "print(dict(cleaned_weights))\n",
    "print(\"-----------------------------------\")\n",
    "ef.portfolio_performance(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "3bf0c5ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting.plot_weights(weights, title=\"Portfolio Allocation\")\n",
    "\n",
    "weights_dict = dict(cleaned_weights)\n",
    "\n",
    "# Create a figure and axis for the pie chart\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "# Create the pie chart\n",
    "ax.pie(weights_dict.values(), labels=weights_dict.keys())\n",
    "\n",
    "# Set the title of the chart\n",
    "ax.set_title('Portfolio Allocation Using Mean Variance Optimization')\n",
    "\n",
    "# Display the chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa4c77eb",
   "metadata": {},
   "source": [
    "#### Considering the investment amount as $100,000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "068d6474",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discrete allocation: {'AAPL': 321, 'MRNA': 143, 'COST': 56, 'KR': 157}\n",
      "Funds remaining: $43.75\n"
     ]
    }
   ],
   "source": [
    "from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices\n",
    "\n",
    "latest_prices = get_latest_prices(portfolio).dropna()\n",
    "latest_prices = pd.to_numeric(latest_prices, errors='coerce')\n",
    "latest_prices = latest_prices.iloc[1:]  # drop the first row\n",
    "# print(latest_prices)\n",
    "amount = 100000\n",
    "da = DiscreteAllocation(weights, latest_prices, total_portfolio_value=amount)\n",
    "\n",
    "allocation, leftover = da.greedy_portfolio()\n",
    "print(\"Discrete allocation:\", allocation)\n",
    "print(\"Funds remaining: ${:.2f}\".format(leftover))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdca59d6",
   "metadata": {},
   "source": [
    "## Conclusion For Mean Variance Optimization\n",
    "\n",
    "We see that our portfolio performs with an expected annual return of 43 percent. The Sharpe ratio value of 1.34 indicates that the portfolio optimization algorithm performs well with our current data. Of course, this return is inflated and is not likely to hold up in the future. \n",
    "\n",
    "Mean variance optimization doesn’t perform very well since it makes many simplifying assumptions, such as returns being normally distributed and the need for an invertible covariance matrix. Fortunately, methods like HRP and mCVAR address these limitations. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd9dcfac",
   "metadata": {},
   "source": [
    "# Hierarchical Risk Parity (HRP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "1c106c5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pypfopt import HRPOpt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "813e505a",
   "metadata": {},
   "outputs": [],
   "source": [
    "returns = portfolio[['MRNA', 'PFE', 'JNJ', 'GOOGL', 'META', 'APPL', 'COST', 'WMT', 'KR', 'JPM', 'BAC', 'HSBC']] = portfolio[['MRNA', 'PFE', 'JNJ', 'GOOGL', 'META', 'AAPL', 'COST', 'WMT', 'KR', 'JPM', 'BAC', 'HSBC']].pct_change().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "cffd7a02",
   "metadata": {},
   "outputs": [],
   "source": [
    "hrp = HRPOpt(returns)\n",
    "hrp_weights = hrp.optimize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "77d3eada",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'AAPL': 0.07578508276058257, 'BAC': 0.061285362879451764, 'COST': 0.09043492065023764, 'GOOGL': 0.051059553492855965, 'HSBC': 0.059511423787638025, 'JNJ': 0.2146142053950624, 'JPM': 0.04755542704459071, 'KR': 0.11038355390831939, 'META': 0.026764887036446836, 'MRNA': 0.019619010874009676, 'PFE': 0.1420019409662181, 'WMT': 0.1009846312045869}\n",
      "-----------------------------------\n",
      "Expected annual return: 15.9%\n",
      "Annual volatility: 18.5%\n",
      "Sharpe Ratio: 0.75\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.15888304757454455, 0.1846398191886778, 0.7521836198974188)"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(dict(hrp_weights))\n",
    "print(\"-----------------------------------\")\n",
    "hrp.portfolio_performance(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "0619b1b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting.plot_weights(hrp_weights, title=\"Portfolio Allocation\")\n",
    "\n",
    "weights_dict = dict(hrp_weights)\n",
    "\n",
    "# Create a figure and axis for the pie chart\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "# Create the pie chart\n",
    "ax.pie(weights_dict.values(), labels=weights_dict.keys())\n",
    "\n",
    "# Set the title of the chart\n",
    "ax.set_title('Portfolio Allocation using Hierarchical Risk Parity (HRP)')\n",
    "\n",
    "# Display the chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "ad6636a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discrete allocation (HRP): {'JNJ': 121, 'PFE': 277, 'KR': 247, 'WMT': 71, 'COST': 20, 'AAPL': 58, 'BAC': 185, 'HSBC': 191, 'GOOGL': 58, 'JPM': 36, 'META': 22, 'MRNA': 11}\n",
      "Funds remaining (HRP): $40.58\n"
     ]
    }
   ],
   "source": [
    "da_hrp = DiscreteAllocation(hrp_weights, latest_prices, total_portfolio_value=100000)\n",
    "\n",
    "allocation, leftover = da_hrp.greedy_portfolio()\n",
    "print(\"Discrete allocation (HRP):\", allocation)\n",
    "print(\"Funds remaining (HRP): ${:.2f}\".format(leftover))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f1d798d",
   "metadata": {},
   "source": [
    "# Conclusion Hierarchical Risk Parity (HRP)\n",
    "\n",
    "We see that we have an expected annual return of 15.9 percent, which is significantly less than the inflated 43.0 percent we achieved with mean variance optimization. We also see a diminished Sharpe ratio of 0.75.  This result is much more reasonable and more likely to hold up in the future since HRP is not as sensitive to outliers as mean variance optimization is. \n",
    "\n",
    "We see that our algorithm suggests we invest heavily into Kroger (KR), HSBC, Johnson & Johnson (JNJ) and Pfizer (PFE) and not, as the previous model did, so much into Moderna (MRNA) and Apple (AAPL). Further, while the performance decreased, we can be more confident that this model will perform just as well when we refresh our data. This is because HRP is more robust to the anomalous increase in Moderna and Apple stock prices."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ead2898",
   "metadata": {},
   "source": [
    "# Mean Conditional Value at Risk (mCVAR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "6a6ee814",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pypfopt.efficient_frontier import EfficientCVaR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "48cc994b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'MRNA': 0.0164112362579626, 'PFE': 0.0570599244725613, 'JNJ': 0.2818814967502368, 'GOOGL': 2.30506716e-08, 'META': 2.22640728e-08, 'AAPL': 0.0851246432923121, 'COST': 3.86006137e-08, 'WMT': 0.1922373482936009, 'KR': 0.1432909752939262, 'JPM': 4.07974923e-08, 'BAC': 0.2239942403932198, 'HSBC': 1.05333297e-08}\n",
      "-----------------------------------\n",
      "Expected annual return: 12.7%\n",
      "Conditional Value at Risk: -0.01%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.12695234386727605, -0.00012160499953229924)"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S = portfolio[['MRNA', 'PFE', 'JNJ', 'GOOGL', 'META', 'APPL', 'COST', 'WMT', 'KR', 'JPM', 'BAC', 'HSBC']].cov()\n",
    "ef_cvar = EfficientCVaR(mu, S)\n",
    "cvar_weights = ef_cvar.min_cvar()\n",
    "\n",
    "cleaned_weights = ef_cvar.clean_weights()\n",
    "print(dict(cvar_weights))\n",
    "print(\"-----------------------------------\")\n",
    "ef_cvar.portfolio_performance(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "77a6a0c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting.plot_weights(cleaned_weights, title=\"Portfolio Allocation\")\n",
    "\n",
    "weights_dict = dict(cleaned_weights)\n",
    "\n",
    "# Create a figure and axis for the pie chart\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "# Create the pie chart\n",
    "ax.pie(weights_dict.values(), labels=weights_dict.keys())\n",
    "\n",
    "# Set the title of the chart\n",
    "ax.set_title('Portfolio Allocation using Mean Conditional Value at Risk (mCVAR)')\n",
    "\n",
    "# Display the chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "6984b40f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discrete allocation (CVAR): {'JNJ': 160, 'BAC': 676, 'WMT': 135, 'KR': 321, 'AAPL': 66, 'PFE': 111, 'MRNA': 9}\n",
      "Funds remaining (CVAR): $15.45\n"
     ]
    }
   ],
   "source": [
    "da_cvar = DiscreteAllocation(cvar_weights, latest_prices, total_portfolio_value=100000)\n",
    "\n",
    "allocation, leftover = da_cvar.greedy_portfolio()\n",
    "print(\"Discrete allocation (CVAR):\", allocation)\n",
    "print(\"Funds remaining (CVAR): ${:.2f}\".format(leftover))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f1703af",
   "metadata": {},
   "source": [
    "# Conclusion Mean Conditional Value at Risk (mCVAR)\n",
    "\n",
    "We see that this algorithm suggests we invest heavily into Johnson & Johnson (JNJ) and Bank of America (BAC). Also it suggests to buy a single share of HSBC. Also we see that the expected return is 12.7 percent.As with HRP, this result is much more reasonable than the inflated 43 percent returns given by mean variance optimization since it is not as sensitive to the anomalous behaviour of the Moderna stock price. "
   ]
  }
 ],
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
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