awesome_portfolio_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7dc65526",
   "metadata": {},
   "source": [
    "![alt text](awesome_logo.png \"Awesome Retirement Portfolio Projector Tool\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ff0949c",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fc163016",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "# For data management\n",
    "import sqlalchemy as sql\n",
    "import pandas as pd\n",
    "import os\n",
    "import datetime\n",
    "import alpaca_trade_api as tradeapi\n",
    "from dotenv import load_dotenv\n",
    "from sqlalchemy import inspect\n",
    "\n",
    "# For Portfolo Optimization and visualizations\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import hvplot.pandas  \n",
    "from scipy.optimize import minimize, Bounds\n",
    "import matplotlib.pyplot as plt\n",
    "from bokeh.models.formatters import NumeralTickFormatter\n",
    "\n",
    "# For Simulation\n",
    "from MCForecastTools import MCSimulation\n",
    "\n",
    "# Project functions\n",
    "from awesome_metrics import get_portfolio_allocation_and_valuation, get_annual_expected_return_volatility_and_sharpe_ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ff3e2c9",
   "metadata": {},
   "source": [
    "# My Current Portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "313f79d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    There is not portfolio already available. Please run `python awesome_tool.py` from the command line to enter your portfolio. \n",
      "    Detailed instructions on usage are available in the Usage section of the README file.\n",
      "    Thanks for running the program.\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "# Get porfolio and report data from user\n",
    "connection_string = 'sqlite:///awesome_app.db'\n",
    "engine = sql.create_engine(connection_string, echo = False)\n",
    "inspect(engine).get_table_names()\n",
    "\n",
    "try:\n",
    "    portfolio_df = pd.read_sql_query(\"SELECT * FROM portfolio\", engine)\n",
    "    # handle exception\n",
    "except Exception as ex:\n",
    "    print(f\"\"\"\n",
    "    There is not portfolio already available. Please run `python awesome_tool.py` from the command line to enter your portfolio. \n",
    "    Detailed instructions on usage are available in the Usage section of the README file, including the use of .env file to access ALPACA.\n",
    "    Thanks for running the program.\n",
    "    \"\"\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c47ffd00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create empty portfolio of diversification options\n",
    "diversification_options = {\n",
    "    \"SPY\": \"Equity\",\n",
    "    \"AGG\": \"Fixed Income\",\n",
    "}\n",
    "\n",
    "portfolio_additions = []\n",
    "for symbol, asset_type in diversification_options.items():\n",
    "    # skip if symbol included in portfolio already\n",
    "    if symbol not in portfolio_df.index:\n",
    "        portfolio_additions.append({\n",
    "            \"symbol\": symbol,\n",
    "            \"quantity\": 0,\n",
    "            \"type\": asset_type\n",
    "        })\n",
    "    \n",
    "etf_addition_for_diversification = pd.DataFrame(portfolio_additions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9be37be8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Portfolio extended for recommendation based on risk profile\n",
    "portfolio_extended_df = pd.concat([portfolio_df, etf_addition_for_diversification], axis=0).sort_values(\"symbol\",ignore_index=True)\n",
    "\n",
    "# Setting format for asset type in columns for future optimization\n",
    "portfolio_extended_df[\"equity\"] = portfolio_extended_df[\"type\"] == \"Equity\"\n",
    "portfolio_extended_df[\"fixed income\"] = portfolio_extended_df[\"type\"] == \"Fixed Income\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b5e060be",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Python-dotenv could not parse statement starting at line 1\n"
     ]
    }
   ],
   "source": [
    "# Get Alpaca data\n",
    "load_dotenv()\n",
    "\n",
    "# Set Alpaca API key and secret\n",
    "alpaca_api_key = os.getenv(\"ALPACA_API_KEY\")\n",
    "alpaca_secret_key = os.getenv(\"ALPACA_SECRET_KEY\")\n",
    "\n",
    "# Create the Alpaca API object\n",
    "alpaca = tradeapi.REST(\n",
    "    alpaca_api_key,\n",
    "    alpaca_secret_key,\n",
    "    api_version=\"v2\"\n",
    ")\n",
    "\n",
    "# Set the tickers\n",
    "tickers = portfolio_extended_df[\"symbol\"]\n",
    "\n",
    "# Set timeframe to one day ('1D') for the Alpaca API\n",
    "timeframe = \"1D\"\n",
    "# Set the number of years of data to fetch\n",
    "num_years = 30\n",
    "\n",
    "# Fetch batches of data from alpaca api to get as much historical data as possible (up to num_years)\n",
    "batch_size = 1000\n",
    "num_days_data_needed = num_years * 365\n",
    "\n",
    "\n",
    "first_date = datetime.datetime.now()\n",
    "last_date = first_date - datetime.timedelta(days=num_days_data_needed)\n",
    "\n",
    "def format_date_iso(date):\n",
    "    return pd.Timestamp(date, tz=\"America/New_York\").isoformat()\n",
    "\n",
    "# empty DF to hold alpaca data\n",
    "portfolio_close_prices_df = pd.DataFrame()\n",
    "\n",
    "\n",
    "next_batch_date = first_date\n",
    "while num_days_data_needed > 0:\n",
    "    end_date = str(next_batch_date).split()[0]\n",
    "    \n",
    "    next_batch_date -= datetime.timedelta(days=batch_size)\n",
    "    if next_batch_date < last_date:\n",
    "        next_batch_date = last_date\n",
    "\n",
    "    start_date = str(next_batch_date).split()[0]\n",
    "    \n",
    "    results_batch_df = alpaca.get_barset(\n",
    "        tickers,\n",
    "        timeframe,\n",
    "        start = format_date_iso(start_date),\n",
    "        end = format_date_iso(end_date),\n",
    "        limit = batch_size\n",
    "    ).df\n",
    "    \n",
    "    results_batch_df.drop(['open', 'high', 'low', 'volume'], level=1, axis=1, inplace=True)\n",
    "\n",
    "    # add batch to portfolio_close_prices_df\n",
    "    portfolio_close_prices_df = pd.concat([portfolio_close_prices_df, results_batch_df])\n",
    "    num_days_data_needed -= batch_size\n",
    "\n",
    "portfolio_close_prices_df.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1e3d6e45",
   "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>symbol</th>\n",
       "      <th>quantity</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>total</th>\n",
       "      <th>Allocation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Equity</td>\n",
       "      <td>2768.58</td>\n",
       "      <td>27685.8</td>\n",
       "      <td>0.126500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CMG</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Equity</td>\n",
       "      <td>1891.95</td>\n",
       "      <td>37839.0</td>\n",
       "      <td>0.172892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IBM</td>\n",
       "      <td>500.0</td>\n",
       "      <td>Equity</td>\n",
       "      <td>139.11</td>\n",
       "      <td>69555.0</td>\n",
       "      <td>0.317806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XLB</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Equity</td>\n",
       "      <td>83.78</td>\n",
       "      <td>83780.0</td>\n",
       "      <td>0.382802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol  quantity    type    price    total  Allocation\n",
       "0   GOOG      10.0  Equity  2768.58  27685.8    0.126500\n",
       "1    CMG      20.0  Equity  1891.95  37839.0    0.172892\n",
       "2    IBM     500.0  Equity   139.11  69555.0    0.317806\n",
       "3    XLB    1000.0  Equity    83.78  83780.0    0.382802"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1m My total portfolio value as of 2021-08-20 is $218,859.80\n"
     ]
    }
   ],
   "source": [
    "# Original portfolio addition of allocation\n",
    "[portfolio_df, original_valuation] = get_portfolio_allocation_and_valuation(\n",
    "    portfolio_df,\n",
    "    portfolio_close_prices_df[portfolio_df[\"symbol\"]]\n",
    ")\n",
    "\n",
    "display(portfolio_df)\n",
    "latest_pricing_date = str(portfolio_close_prices_df.index[-1]).split()[0]\n",
    "\n",
    "print()\n",
    "print(f\"\\033[1m My total portfolio value as of {latest_pricing_date} is ${original_valuation:,.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "092ad285",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print()\n",
    "portfolio_df.set_index(\"symbol\").plot.pie(\n",
    "    figsize = [7, 7],\n",
    "    y = \"Allocation\",\n",
    "    ylabel = \"Amount\",\n",
    "    title = \"Original Portfolio Allocation\"\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "69055581",
   "metadata": {},
   "outputs": [],
   "source": [
    "# INDIVIDUAL SECURITY ANALYSIS\n",
    "#daily_returns_securities_df=portfolio_close_prices_df.pct_change()\n",
    "\n",
    "#RETURN DISPERSION BOXPLOT\n",
    "#daily_returns_securities_df.plot(\n",
    "#    kind='box', \n",
    "#    figsize=(25,10), \n",
    "#    title= 'Daily Returns Extended Portfolio (Original and Diversifying ETFs'\n",
    "#);\n",
    "\n",
    "# SUMMARY STATISTICS\n",
    "#display(daily_returns_securities_df.describe())\n",
    "\n",
    "# CORRELATION MATRIX\n",
    "# Calculates correlation of securities in the client_portfolio_close_prices_df\n",
    "#print(f\"\\033[1m Table of Correlations between Assets\")\n",
    "#round(daily_returns_securities_df.corr(),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e85a71d7",
   "metadata": {},
   "source": [
    "# Optimized Portfolio Recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f729678f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m My risk level is high.\n",
      "\u001b[1m I plan on retiring in 20.0 years.\n"
     ]
    }
   ],
   "source": [
    "report_df = pd.read_sql_query(\"SELECT * FROM report\", engine)\n",
    "[risk_tolerance, years_until_retirement] = report_df.iloc[0]\n",
    "\n",
    "print(f\"\\033[1m My risk level is {risk_tolerance}.\")\n",
    "print(f\"\\033[1m I plan on retiring in {years_until_retirement} years.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "223779c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optimization works with a minimization function, so instead of maximizing Sharpe Ratio, we will minimize the negative sharpe.\n",
    "def negative_sharpe_ratio(portfolio_weights, portfolio_daily_returns):\n",
    "    return get_annual_expected_return_volatility_and_sharpe_ratio(\n",
    "        portfolio_weights,\n",
    "        portfolio_daily_returns\n",
    "    )[2] * -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "360b58b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Contraints: weight sum must be equal to 1 \n",
    "def check_sum_of_portfolio_weights_is_one(portfolio_weights):\n",
    "    '''\n",
    "    Returns 0 if sum of portfolio_weights is 1.0\n",
    "    '''\n",
    "    return np.sum(portfolio_weights) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "47d9ce7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constraint: equity allocation is less than maximum limit (inequality constraint, upper limit - equity allocation >0)\n",
    "def check_sum_of_portfolio_equity_weights_is_less_than_upper_bound(portfolio_weights, asset_class_securities, equity_upper_limit):\n",
    "    \"\"\"\n",
    "    Makes sure allocation in equity is less than the maximum allowed\n",
    "    \"\"\"\n",
    "    portfolio_equity_allocation = sum(portfolio_weights * asset_class_securities['Equity'])\n",
    "    return equity_upper_limit - portfolio_equity_allocation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3f66338f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constraint: equity allocation is more than minimum limit (inequality constraint, equity allocation - lower_limit >0)\n",
    "def check_sum_of_portfolio_equity_weights_is_more_than_lower_bound(portfolio_weights, asset_class_securities, equity_lower_limit):\n",
    "    \"\"\"\n",
    "    Makes sure allocation in equity is more than the minimum allowed\n",
    "    \"\"\"\n",
    "    portfolio_equity_allocation = sum(portfolio_weights * asset_class_securities['Equity'])\n",
    "    return portfolio_equity_allocation - equity_lower_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "978d4db1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determination of the limits for total equity allocation given level of risk\n",
    "# Here we defined the required arguments for optimization constraints\n",
    "if risk_tolerance == 'high':\n",
    "    [equity_lower_limit, equity_upper_limit] = [0.75, 0.90]\n",
    "\n",
    "elif risk_tolerance == 'moderate':\n",
    "    [equity_lower_limit, equity_upper_limit] = [0.4, 0.6]\n",
    "\n",
    "elif risk_tolerance == 'low':\n",
    "    [equity_lower_limit, equity_upper_limit] = [0.05, 0.2]\n",
    "\n",
    "else: [equity_lower_limit, equity_upper_limit] = [0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ab59f4bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get df of security asset classes\n",
    "asset_class_securities = portfolio_extended_df[['symbol', 'equity', 'fixed income']].set_index('symbol').sort_index()\n",
    "asset_class_securities.columns = ['Equity', 'Fixed Income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a0e11040",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "DEFINITION OF CONSTRAINTS\n",
    "By convention of minimize function it should be a function that returns zero for equality constraints, and a positive number for inequality constraints. \n",
    "Constraint type: 'eq' for equality, 'ineq' for inequality. \n",
    "'fun': The function defining the constraint:      \n",
    "                              sum(portfolio_weights)=1  ==> sum(portfolio_weights) - 1 = 0\n",
    "       portfolio_equity_allocation < equity_upper_limit ==> equity_upper_limit - portfolio_equity_allocation >0\n",
    "       portfolio_equity allocation > equity_lower_limit ==> portfolio_equity_allocation - lower_limit >0\n",
    "\"\"\" \n",
    "constraints = [\n",
    "    {\n",
    "        'type': 'eq',\n",
    "        'fun': check_sum_of_portfolio_weights_is_one\n",
    "    },{\n",
    "        'type': 'ineq',\n",
    "        'fun': check_sum_of_portfolio_equity_weights_is_less_than_upper_bound,\n",
    "        'args': (asset_class_securities, equity_upper_limit)\n",
    "    },{\n",
    "        'type': 'ineq',\n",
    "        'fun': check_sum_of_portfolio_equity_weights_is_more_than_lower_bound,\n",
    "        'args': (asset_class_securities, equity_lower_limit)\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0c60a78b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting bounds for the weights of each security\n",
    "# Only long positions are allowed\n",
    "num_securities = len(portfolio_close_prices_df.columns)\n",
    "\n",
    "lb=[0] * num_securities\n",
    "ub=[1] * num_securities\n",
    "        \n",
    "bounds = Bounds(lb, ub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6029ac36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial Guess (equal distribution)\n",
    "# n: number of stocks in the portfolio to optimize\n",
    "initial_guess = [1 / num_securities] * num_securities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fedf269b",
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_daily_returns = portfolio_close_prices_df.pct_change(1).dropna()\n",
    "portfolio_daily_returns.columns = portfolio_daily_returns.columns.droplevel(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cca01926",
   "metadata": {},
   "outputs": [],
   "source": [
    "# OPTIMIZATION \n",
    "# Sequential Least SQuares Programming (SLSQP) method is used, which is similar to Least Square optimization, but using less resources.\n",
    "optimization_results = minimize(\n",
    "    negative_sharpe_ratio,\n",
    "    initial_guess,\n",
    "    args = portfolio_daily_returns,\n",
    "    method = 'SLSQP',\n",
    "    bounds = bounds,\n",
    "    constraints = constraints\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4c0b417f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# help(minimize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "592c571f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully\n",
      "Optimized Sharpe ratio: 0.81\n"
     ]
    }
   ],
   "source": [
    "# Optimization success\n",
    "print(optimization_results.message)\n",
    "print(f\"Optimized Sharpe ratio: {-optimization_results.fun:.2f}\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "089271f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optimal allocation (weights)\n",
    "optimal_portfolio_weights = optimization_results.x\n",
    "\n",
    "#Formatting results\n",
    "portfolio_position_names  = pd.Series(portfolio_daily_returns.columns)\n",
    "optimal_portfolio_weights = pd.Series(optimal_portfolio_weights)\n",
    "optimal_portfolio_weights = pd.concat([portfolio_position_names, optimal_portfolio_weights], axis=1)\n",
    "optimal_portfolio_weights.columns = [\"symbol\", \"Optimal Allocation\"]\n",
    "optimal_portfolio_weights = optimal_portfolio_weights.set_index(\"symbol\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "385b6413",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Portfolio extended addition of allocation\n",
    "portfolio_extended_df = get_portfolio_allocation_and_valuation(portfolio_extended_df, portfolio_close_prices_df)[0]\n",
    "portfolio_extended_df = portfolio_extended_df.rename(columns={'Allocation': 'Original Allocation'}).set_index(\"symbol\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "08f4decf",
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_allocation_comparison = pd.concat([\n",
    "    portfolio_extended_df[\"type\"],\n",
    "    round(portfolio_extended_df[\"Original Allocation\"] * 100, 2),\n",
    "    round(optimal_portfolio_weights * 100, 2)\n",
    "], axis=1)\n",
    "portfolio_allocation_comparison.columns = ['Asset Type', 'Original Allocation %', 'Optimal Allocation %']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e33a756b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "portfolio_allocation_comparison.plot.pie(\n",
    "    figsize = [7, 7],\n",
    "    y = \"Optimal Allocation %\",\n",
    "    ylabel = \"Amount\",\n",
    "    title = \"Optimized Portfolio Allocation\"\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "aa917a9f",
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
       "<div id='1002'>\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
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      "text/plain": [
       ":Bars   [symbol,Variable]   (Allocation (%))"
      ]
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   "source": [
    "portfolio_allocation_comparison.hvplot.bar(\n",
    "    title = \"Allocation Comparison between Original and Optimized Portfolio (%)\",\n",
    "    ylabel = \"Security Allocation (%)\",\n",
    "    xlabel = \"Security Symbol\",\n",
    "    rot = 90,\n",
    "    value_label = \"Allocation (%)\",\n",
    "    height = 500,\n",
    "    responsive = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "97482c02",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return of results \n",
    "# ORIGINAL PORTFOLIO Performance Metrics\n",
    "[original_expected_return, original_volatility, original_Sharpe_ratio] = get_annual_expected_return_volatility_and_sharpe_ratio(\n",
    "    np.array(portfolio_extended_df['Original Allocation']),\n",
    "    portfolio_daily_returns\n",
    ")\n",
    "\n",
    "# print( f\"Original Annual Expected Return:  {100*original_expected_return:.2f} %\"  )\n",
    "# print( f\"Original Annual Volatility:       {100*original_volatility:.2f} % \") \n",
    "# print( f\"Original Annual Sharpe ratio:     {original_Sharpe_ratio:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d9e83220",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optimal values (Max Sharpe ratio point)\n",
    "[optimal_expected_return, optimal_volatility, maximum_Sharpe_ratio] = get_annual_expected_return_volatility_and_sharpe_ratio(\n",
    "    optimization_results.x,\n",
    "    portfolio_daily_returns\n",
    ")\n",
    "\n",
    "# print( f\"Optimal Annual Expected Return:   {100*optimal_expected_return:.2f}%\"  )\n",
    "# print( f\"Optimal Annual Volatility:        {100*optimal_volatility:.2f}%\") \n",
    "# print( f\"Maximum Annual Sharpe ratio:      {maximum_Sharpe_ratio:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "36673ff4",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Expected Annual Return (%)</th>\n",
       "      <th>Volatility (%)</th>\n",
       "      <th>Sharpe Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Original Portfolio</th>\n",
       "      <td>12.40</td>\n",
       "      <td>22.44</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal Portfolio</th>\n",
       "      <td>17.71</td>\n",
       "      <td>21.87</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Expected Annual Return (%)  Volatility (%)  Sharpe Ratio\n",
       "Original Portfolio                       12.40           22.44          0.55\n",
       "Optimal Portfolio                        17.71           21.87          0.81"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Portfolios metrics original and optimized - data preparation\n",
    "results_df=pd.DataFrame({\"Original Portfolio\":[original_expected_return*100, original_volatility*100, original_Sharpe_ratio],\n",
    "                        \"Optimal Portfolio\":[optimal_expected_return*100, optimal_volatility*100, maximum_Sharpe_ratio]})\n",
    "results_df=results_df.T\n",
    "results_df.columns=[\"Expected Annual Return (%)\", \"Volatility (%)\", \"Sharpe Ratio\"]\n",
    "results_df\n",
    "round(results_df,2)"
   ]
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       "})(window);</script>"
      ],
      "text/plain": [
       ":Scatter   [Volatility (%)]   (Expected Annual Return (%),index,_size)"
      ]
     },
     "execution_count": 29,
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       "id": "1123"
      }
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     "output_type": "execute_result"
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   "source": [
    "# Plot of results\n",
    "results_df_for_plot=results_df[[\"Expected Annual Return (%)\",\"Volatility (%)\" ]]\n",
    "results_df_for_plot[\"Sharpe Ratio\"]=results_df[\"Sharpe Ratio\"]*5000\n",
    "\n",
    "results_df_for_plot=results_df_for_plot.reset_index()\n",
    "\n",
    "# Setting axis limits \n",
    "max_vol=   max(results_df_for_plot[\"Volatility (%)\"])\n",
    "min_vol=   min(results_df_for_plot[\"Volatility (%)\"])\n",
    "max_return=max(results_df_for_plot[\"Expected Annual Return (%)\"])\n",
    "\n",
    "\n",
    "results_df_for_plot.hvplot(\n",
    "    kind = \"scatter\",\n",
    "    x = 'Volatility (%)',\n",
    "    y = 'Expected Annual Return (%)',\n",
    "    c = 'index',\n",
    "    s = results_df_for_plot['Sharpe Ratio'],\n",
    "    xlim = (min_vol-1, max_vol+1),\n",
    "    ylim = (0, max_return+5),\n",
    "    title = \"Metrics Comparison: Original vs Optimized Portfolio\",\n",
    "    height = 350,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3df2f025",
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   "outputs": [
    {
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     "output_type": "display_data"
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       "  }\n",
       "  if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
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       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":Bars   [index]   (Sharpe Ratio)"
      ]
     },
     "execution_count": 30,
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       "id": "1259"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df[\"Sharpe Ratio\"].hvplot.bar(title=\"Sharpe Ratio comparison between Original and Optimal Portfolios\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82a69595",
   "metadata": {},
   "source": [
    "# Portfolio Projections\n",
    "\n",
    "Montecarlo Simultations projecting expected returns by retirement age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1aac718a",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_simulation = 100\n",
    "num_trading_days = 252 * int(years_until_retirement)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c6060ab2",
   "metadata": {},
   "outputs": [],
   "source": [
    "MC_n_years_original = MCSimulation(\n",
    "    portfolio_data = portfolio_close_prices_df,\n",
    "    weights = list(portfolio_extended_df['Original Allocation']),\n",
    "    num_simulation = num_simulation,\n",
    "    num_trading_days = num_trading_days\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "63e0f6a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.014850</td>\n",
       "      <td>0.999153</td>\n",
       "      <td>1.002920</td>\n",
       "      <td>1.018083</td>\n",
       "      <td>0.994602</td>\n",
       "      <td>0.998708</td>\n",
       "      <td>1.023674</td>\n",
       "      <td>0.987713</td>\n",
       "      <td>1.019392</td>\n",
       "      <td>1.012413</td>\n",
       "      <td>...</td>\n",
       "      <td>0.990295</td>\n",
       "      <td>0.997963</td>\n",
       "      <td>0.984641</td>\n",
       "      <td>1.002167</td>\n",
       "      <td>0.998387</td>\n",
       "      <td>0.998024</td>\n",
       "      <td>1.000660</td>\n",
       "      <td>0.985601</td>\n",
       "      <td>0.998808</td>\n",
       "      <td>1.009172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.018303</td>\n",
       "      <td>0.993675</td>\n",
       "      <td>1.008719</td>\n",
       "      <td>1.042915</td>\n",
       "      <td>0.993282</td>\n",
       "      <td>0.976791</td>\n",
       "      <td>1.042256</td>\n",
       "      <td>0.996525</td>\n",
       "      <td>1.034342</td>\n",
       "      <td>1.008829</td>\n",
       "      <td>...</td>\n",
       "      <td>1.001529</td>\n",
       "      <td>0.986829</td>\n",
       "      <td>1.015044</td>\n",
       "      <td>1.007318</td>\n",
       "      <td>1.003182</td>\n",
       "      <td>0.981386</td>\n",
       "      <td>1.005282</td>\n",
       "      <td>0.992406</td>\n",
       "      <td>0.971517</td>\n",
       "      <td>1.014074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.994549</td>\n",
       "      <td>0.991317</td>\n",
       "      <td>1.016497</td>\n",
       "      <td>1.064495</td>\n",
       "      <td>1.001620</td>\n",
       "      <td>0.987835</td>\n",
       "      <td>1.039876</td>\n",
       "      <td>0.981152</td>\n",
       "      <td>1.040213</td>\n",
       "      <td>0.997016</td>\n",
       "      <td>...</td>\n",
       "      <td>0.990397</td>\n",
       "      <td>1.009570</td>\n",
       "      <td>1.021533</td>\n",
       "      <td>1.006672</td>\n",
       "      <td>0.998034</td>\n",
       "      <td>0.997763</td>\n",
       "      <td>0.996030</td>\n",
       "      <td>1.004653</td>\n",
       "      <td>0.968817</td>\n",
       "      <td>1.010736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.005752</td>\n",
       "      <td>0.992893</td>\n",
       "      <td>1.015083</td>\n",
       "      <td>1.077127</td>\n",
       "      <td>1.006721</td>\n",
       "      <td>0.980181</td>\n",
       "      <td>1.058302</td>\n",
       "      <td>0.987291</td>\n",
       "      <td>1.045252</td>\n",
       "      <td>1.000802</td>\n",
       "      <td>...</td>\n",
       "      <td>0.988093</td>\n",
       "      <td>1.006030</td>\n",
       "      <td>1.007919</td>\n",
       "      <td>1.009520</td>\n",
       "      <td>0.990513</td>\n",
       "      <td>1.008646</td>\n",
       "      <td>0.993736</td>\n",
       "      <td>0.988816</td>\n",
       "      <td>0.967803</td>\n",
       "      <td>1.004781</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",
       "      <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>5036</th>\n",
       "      <td>12.449760</td>\n",
       "      <td>1.294308</td>\n",
       "      <td>5.157548</td>\n",
       "      <td>1.828874</td>\n",
       "      <td>6.288105</td>\n",
       "      <td>36.663797</td>\n",
       "      <td>3.288536</td>\n",
       "      <td>10.281993</td>\n",
       "      <td>9.185487</td>\n",
       "      <td>29.463653</td>\n",
       "      <td>...</td>\n",
       "      <td>3.633707</td>\n",
       "      <td>11.621957</td>\n",
       "      <td>3.008779</td>\n",
       "      <td>19.260073</td>\n",
       "      <td>5.533693</td>\n",
       "      <td>8.795299</td>\n",
       "      <td>16.752620</td>\n",
       "      <td>11.639688</td>\n",
       "      <td>19.843259</td>\n",
       "      <td>8.208732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5037</th>\n",
       "      <td>12.114879</td>\n",
       "      <td>1.296847</td>\n",
       "      <td>5.093117</td>\n",
       "      <td>1.857044</td>\n",
       "      <td>6.316281</td>\n",
       "      <td>36.616351</td>\n",
       "      <td>3.312115</td>\n",
       "      <td>10.341121</td>\n",
       "      <td>9.217302</td>\n",
       "      <td>29.336184</td>\n",
       "      <td>...</td>\n",
       "      <td>3.627041</td>\n",
       "      <td>11.758013</td>\n",
       "      <td>2.999521</td>\n",
       "      <td>18.903200</td>\n",
       "      <td>5.491625</td>\n",
       "      <td>8.840577</td>\n",
       "      <td>16.799416</td>\n",
       "      <td>11.785230</td>\n",
       "      <td>19.824605</td>\n",
       "      <td>8.136372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5038</th>\n",
       "      <td>12.213085</td>\n",
       "      <td>1.303705</td>\n",
       "      <td>5.187451</td>\n",
       "      <td>1.869187</td>\n",
       "      <td>6.347361</td>\n",
       "      <td>36.001088</td>\n",
       "      <td>3.322099</td>\n",
       "      <td>10.318730</td>\n",
       "      <td>9.177727</td>\n",
       "      <td>29.178373</td>\n",
       "      <td>...</td>\n",
       "      <td>3.591313</td>\n",
       "      <td>11.822489</td>\n",
       "      <td>3.050010</td>\n",
       "      <td>18.789970</td>\n",
       "      <td>5.490508</td>\n",
       "      <td>8.924286</td>\n",
       "      <td>16.928107</td>\n",
       "      <td>11.796961</td>\n",
       "      <td>19.987374</td>\n",
       "      <td>8.189728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5039</th>\n",
       "      <td>12.249913</td>\n",
       "      <td>1.308060</td>\n",
       "      <td>5.158965</td>\n",
       "      <td>1.836307</td>\n",
       "      <td>6.417045</td>\n",
       "      <td>36.039237</td>\n",
       "      <td>3.295975</td>\n",
       "      <td>10.352927</td>\n",
       "      <td>9.114619</td>\n",
       "      <td>28.802977</td>\n",
       "      <td>...</td>\n",
       "      <td>3.620156</td>\n",
       "      <td>11.706739</td>\n",
       "      <td>3.042973</td>\n",
       "      <td>18.896373</td>\n",
       "      <td>5.480556</td>\n",
       "      <td>8.916295</td>\n",
       "      <td>16.725957</td>\n",
       "      <td>11.794937</td>\n",
       "      <td>20.136168</td>\n",
       "      <td>8.324151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5040</th>\n",
       "      <td>12.058632</td>\n",
       "      <td>1.315315</td>\n",
       "      <td>5.181689</td>\n",
       "      <td>1.833328</td>\n",
       "      <td>6.402427</td>\n",
       "      <td>36.007155</td>\n",
       "      <td>3.375136</td>\n",
       "      <td>10.273490</td>\n",
       "      <td>9.274295</td>\n",
       "      <td>28.866991</td>\n",
       "      <td>...</td>\n",
       "      <td>3.658702</td>\n",
       "      <td>11.879752</td>\n",
       "      <td>3.050987</td>\n",
       "      <td>19.099480</td>\n",
       "      <td>5.476340</td>\n",
       "      <td>8.823855</td>\n",
       "      <td>16.857350</td>\n",
       "      <td>11.781487</td>\n",
       "      <td>20.496917</td>\n",
       "      <td>8.483398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5041 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4          5         6    \\\n",
       "0      1.000000  1.000000  1.000000  1.000000  1.000000   1.000000  1.000000   \n",
       "1      1.014850  0.999153  1.002920  1.018083  0.994602   0.998708  1.023674   \n",
       "2      1.018303  0.993675  1.008719  1.042915  0.993282   0.976791  1.042256   \n",
       "3      0.994549  0.991317  1.016497  1.064495  1.001620   0.987835  1.039876   \n",
       "4      1.005752  0.992893  1.015083  1.077127  1.006721   0.980181  1.058302   \n",
       "...         ...       ...       ...       ...       ...        ...       ...   \n",
       "5036  12.449760  1.294308  5.157548  1.828874  6.288105  36.663797  3.288536   \n",
       "5037  12.114879  1.296847  5.093117  1.857044  6.316281  36.616351  3.312115   \n",
       "5038  12.213085  1.303705  5.187451  1.869187  6.347361  36.001088  3.322099   \n",
       "5039  12.249913  1.308060  5.158965  1.836307  6.417045  36.039237  3.295975   \n",
       "5040  12.058632  1.315315  5.181689  1.833328  6.402427  36.007155  3.375136   \n",
       "\n",
       "            7         8          9    ...       490        491       492  \\\n",
       "0      1.000000  1.000000   1.000000  ...  1.000000   1.000000  1.000000   \n",
       "1      0.987713  1.019392   1.012413  ...  0.990295   0.997963  0.984641   \n",
       "2      0.996525  1.034342   1.008829  ...  1.001529   0.986829  1.015044   \n",
       "3      0.981152  1.040213   0.997016  ...  0.990397   1.009570  1.021533   \n",
       "4      0.987291  1.045252   1.000802  ...  0.988093   1.006030  1.007919   \n",
       "...         ...       ...        ...  ...       ...        ...       ...   \n",
       "5036  10.281993  9.185487  29.463653  ...  3.633707  11.621957  3.008779   \n",
       "5037  10.341121  9.217302  29.336184  ...  3.627041  11.758013  2.999521   \n",
       "5038  10.318730  9.177727  29.178373  ...  3.591313  11.822489  3.050010   \n",
       "5039  10.352927  9.114619  28.802977  ...  3.620156  11.706739  3.042973   \n",
       "5040  10.273490  9.274295  28.866991  ...  3.658702  11.879752  3.050987   \n",
       "\n",
       "            493       494       495        496        497        498       499  \n",
       "0      1.000000  1.000000  1.000000   1.000000   1.000000   1.000000  1.000000  \n",
       "1      1.002167  0.998387  0.998024   1.000660   0.985601   0.998808  1.009172  \n",
       "2      1.007318  1.003182  0.981386   1.005282   0.992406   0.971517  1.014074  \n",
       "3      1.006672  0.998034  0.997763   0.996030   1.004653   0.968817  1.010736  \n",
       "4      1.009520  0.990513  1.008646   0.993736   0.988816   0.967803  1.004781  \n",
       "...         ...       ...       ...        ...        ...        ...       ...  \n",
       "5036  19.260073  5.533693  8.795299  16.752620  11.639688  19.843259  8.208732  \n",
       "5037  18.903200  5.491625  8.840577  16.799416  11.785230  19.824605  8.136372  \n",
       "5038  18.789970  5.490508  8.924286  16.928107  11.796961  19.987374  8.189728  \n",
       "5039  18.896373  5.480556  8.916295  16.725957  11.794937  20.136168  8.324151  \n",
       "5040  19.099480  5.476340  8.823855  16.857350  11.781487  20.496917  8.483398  \n",
       "\n",
       "[5041 rows x 500 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MC_n_years_original.calc_cumulative_return()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3cc1f345",
   "metadata": {},
   "outputs": [],
   "source": [
    "MC_n_years_optimized = MCSimulation(\n",
    "    portfolio_data = portfolio_close_prices_df,\n",
    "    weights = list(optimal_portfolio_weights[\"Optimal Allocation\"]),\n",
    "    num_simulation = num_simulation,\n",
    "    num_trading_days = num_trading_days\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "da412a69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Monte Carlo simulation number 0.\n",
      "Running Monte Carlo simulation number 10.\n",
      "Running Monte Carlo simulation number 20.\n",
      "Running Monte Carlo simulation number 30.\n",
      "Running Monte Carlo simulation number 40.\n",
      "Running Monte Carlo simulation number 50.\n",
      "Running Monte Carlo simulation number 60.\n",
      "Running Monte Carlo simulation number 70.\n",
      "Running Monte Carlo simulation number 80.\n",
      "Running Monte Carlo simulation number 90.\n",
      "Running Monte Carlo simulation number 100.\n",
      "Running Monte Carlo simulation number 110.\n",
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      "Running Monte Carlo simulation number 130.\n",
      "Running Monte Carlo simulation number 140.\n",
      "Running Monte Carlo simulation number 150.\n",
      "Running Monte Carlo simulation number 160.\n",
      "Running Monte Carlo simulation number 170.\n",
      "Running Monte Carlo simulation number 180.\n",
      "Running Monte Carlo simulation number 190.\n",
      "Running Monte Carlo simulation number 200.\n",
      "Running Monte Carlo simulation number 210.\n",
      "Running Monte Carlo simulation number 220.\n",
      "Running Monte Carlo simulation number 230.\n",
      "Running Monte Carlo simulation number 240.\n",
      "Running Monte Carlo simulation number 250.\n",
      "Running Monte Carlo simulation number 260.\n",
      "Running Monte Carlo simulation number 270.\n",
      "Running Monte Carlo simulation number 280.\n",
      "Running Monte Carlo simulation number 290.\n",
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      "Running Monte Carlo simulation number 350.\n",
      "Running Monte Carlo simulation number 360.\n",
      "Running Monte Carlo simulation number 370.\n",
      "Running Monte Carlo simulation number 380.\n",
      "Running Monte Carlo simulation number 390.\n",
      "Running Monte Carlo simulation number 400.\n",
      "Running Monte Carlo simulation number 410.\n",
      "Running Monte Carlo simulation number 420.\n",
      "Running Monte Carlo simulation number 430.\n",
      "Running Monte Carlo simulation number 440.\n",
      "Running Monte Carlo simulation number 450.\n",
      "Running Monte Carlo simulation number 460.\n",
      "Running Monte Carlo simulation number 470.\n",
      "Running Monte Carlo simulation number 480.\n",
      "Running Monte Carlo simulation number 490.\n"
     ]
    },
    {
     "data": {
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       "      <td>0.996967</td>\n",
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       "      <td>0.988228</td>\n",
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       "      <td>1.004635</td>\n",
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       "      <td>1.004085</td>\n",
       "      <td>0.983949</td>\n",
       "      <td>1.032396</td>\n",
       "      <td>1.007836</td>\n",
       "      <td>1.017010</td>\n",
       "      <td>1.002709</td>\n",
       "      <td>1.023491</td>\n",
       "      <td>1.007268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.994035</td>\n",
       "      <td>0.990335</td>\n",
       "      <td>1.014804</td>\n",
       "      <td>0.991991</td>\n",
       "      <td>1.025457</td>\n",
       "      <td>1.015555</td>\n",
       "      <td>1.014806</td>\n",
       "      <td>1.001339</td>\n",
       "      <td>0.984653</td>\n",
       "      <td>1.000203</td>\n",
       "      <td>...</td>\n",
       "      <td>1.001613</td>\n",
       "      <td>1.006325</td>\n",
       "      <td>0.999793</td>\n",
       "      <td>1.010072</td>\n",
       "      <td>1.044427</td>\n",
       "      <td>0.994015</td>\n",
       "      <td>1.026441</td>\n",
       "      <td>0.993635</td>\n",
       "      <td>1.026954</td>\n",
       "      <td>1.004142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.012174</td>\n",
       "      <td>1.004639</td>\n",
       "      <td>1.012451</td>\n",
       "      <td>1.001048</td>\n",
       "      <td>1.017074</td>\n",
       "      <td>1.039441</td>\n",
       "      <td>0.995480</td>\n",
       "      <td>1.017780</td>\n",
       "      <td>0.985959</td>\n",
       "      <td>0.989218</td>\n",
       "      <td>...</td>\n",
       "      <td>0.995462</td>\n",
       "      <td>1.038692</td>\n",
       "      <td>1.015675</td>\n",
       "      <td>1.011488</td>\n",
       "      <td>1.045346</td>\n",
       "      <td>1.003065</td>\n",
       "      <td>1.033255</td>\n",
       "      <td>1.012226</td>\n",
       "      <td>1.030498</td>\n",
       "      <td>1.007908</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",
       "      <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>5036</th>\n",
       "      <td>21.465790</td>\n",
       "      <td>84.756608</td>\n",
       "      <td>7.582315</td>\n",
       "      <td>55.588533</td>\n",
       "      <td>32.555093</td>\n",
       "      <td>30.249313</td>\n",
       "      <td>69.077675</td>\n",
       "      <td>17.510117</td>\n",
       "      <td>35.903893</td>\n",
       "      <td>12.513797</td>\n",
       "      <td>...</td>\n",
       "      <td>6.032671</td>\n",
       "      <td>7.453824</td>\n",
       "      <td>11.274684</td>\n",
       "      <td>13.942188</td>\n",
       "      <td>21.932187</td>\n",
       "      <td>12.498953</td>\n",
       "      <td>31.365904</td>\n",
       "      <td>84.865420</td>\n",
       "      <td>48.912687</td>\n",
       "      <td>39.328659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5037</th>\n",
       "      <td>21.546318</td>\n",
       "      <td>84.260468</td>\n",
       "      <td>7.623454</td>\n",
       "      <td>55.909086</td>\n",
       "      <td>32.675861</td>\n",
       "      <td>30.696764</td>\n",
       "      <td>68.844878</td>\n",
       "      <td>17.854011</td>\n",
       "      <td>35.664846</td>\n",
       "      <td>12.420399</td>\n",
       "      <td>...</td>\n",
       "      <td>5.963202</td>\n",
       "      <td>7.475336</td>\n",
       "      <td>11.315851</td>\n",
       "      <td>13.989432</td>\n",
       "      <td>21.521183</td>\n",
       "      <td>12.231691</td>\n",
       "      <td>31.003417</td>\n",
       "      <td>86.675978</td>\n",
       "      <td>49.480191</td>\n",
       "      <td>39.480251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5038</th>\n",
       "      <td>20.982753</td>\n",
       "      <td>82.680637</td>\n",
       "      <td>7.782951</td>\n",
       "      <td>55.652998</td>\n",
       "      <td>32.956168</td>\n",
       "      <td>30.866493</td>\n",
       "      <td>69.317508</td>\n",
       "      <td>18.067077</td>\n",
       "      <td>35.974236</td>\n",
       "      <td>12.383378</td>\n",
       "      <td>...</td>\n",
       "      <td>5.979570</td>\n",
       "      <td>7.587729</td>\n",
       "      <td>11.260561</td>\n",
       "      <td>13.720756</td>\n",
       "      <td>21.412498</td>\n",
       "      <td>12.202708</td>\n",
       "      <td>31.148888</td>\n",
       "      <td>86.180539</td>\n",
       "      <td>49.721001</td>\n",
       "      <td>40.036864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5039</th>\n",
       "      <td>21.208277</td>\n",
       "      <td>84.430756</td>\n",
       "      <td>7.953612</td>\n",
       "      <td>56.588626</td>\n",
       "      <td>33.141908</td>\n",
       "      <td>30.943282</td>\n",
       "      <td>68.435840</td>\n",
       "      <td>17.970193</td>\n",
       "      <td>35.530772</td>\n",
       "      <td>12.481465</td>\n",
       "      <td>...</td>\n",
       "      <td>6.117293</td>\n",
       "      <td>7.701709</td>\n",
       "      <td>11.265846</td>\n",
       "      <td>13.544568</td>\n",
       "      <td>21.457789</td>\n",
       "      <td>12.196591</td>\n",
       "      <td>31.666826</td>\n",
       "      <td>86.783646</td>\n",
       "      <td>49.082852</td>\n",
       "      <td>40.443639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5040</th>\n",
       "      <td>21.151451</td>\n",
       "      <td>82.082801</td>\n",
       "      <td>7.883152</td>\n",
       "      <td>55.993849</td>\n",
       "      <td>33.174259</td>\n",
       "      <td>31.444200</td>\n",
       "      <td>67.839311</td>\n",
       "      <td>18.272888</td>\n",
       "      <td>36.431316</td>\n",
       "      <td>12.442973</td>\n",
       "      <td>...</td>\n",
       "      <td>6.136479</td>\n",
       "      <td>7.525097</td>\n",
       "      <td>11.281110</td>\n",
       "      <td>13.847669</td>\n",
       "      <td>21.530267</td>\n",
       "      <td>12.320445</td>\n",
       "      <td>31.981295</td>\n",
       "      <td>84.410279</td>\n",
       "      <td>48.126272</td>\n",
       "      <td>40.808662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5041 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0          1         2          3          4          5    \\\n",
       "0      1.000000   1.000000  1.000000   1.000000   1.000000   1.000000   \n",
       "1      0.998020   1.000760  0.988918   0.994212   1.005737   1.010138   \n",
       "2      0.995828   0.981495  0.999448   1.006155   1.014963   1.020139   \n",
       "3      0.994035   0.990335  1.014804   0.991991   1.025457   1.015555   \n",
       "4      1.012174   1.004639  1.012451   1.001048   1.017074   1.039441   \n",
       "...         ...        ...       ...        ...        ...        ...   \n",
       "5036  21.465790  84.756608  7.582315  55.588533  32.555093  30.249313   \n",
       "5037  21.546318  84.260468  7.623454  55.909086  32.675861  30.696764   \n",
       "5038  20.982753  82.680637  7.782951  55.652998  32.956168  30.866493   \n",
       "5039  21.208277  84.430756  7.953612  56.588626  33.141908  30.943282   \n",
       "5040  21.151451  82.082801  7.883152  55.993849  33.174259  31.444200   \n",
       "\n",
       "            6          7          8          9    ...       490       491  \\\n",
       "0      1.000000   1.000000   1.000000   1.000000  ...  1.000000  1.000000   \n",
       "1      0.995837   1.002394   1.001049   0.996967  ...  1.024846  0.999723   \n",
       "2      0.999579   1.012621   0.982073   0.988228  ...  1.004635  0.989168   \n",
       "3      1.014806   1.001339   0.984653   1.000203  ...  1.001613  1.006325   \n",
       "4      0.995480   1.017780   0.985959   0.989218  ...  0.995462  1.038692   \n",
       "...         ...        ...        ...        ...  ...       ...       ...   \n",
       "5036  69.077675  17.510117  35.903893  12.513797  ...  6.032671  7.453824   \n",
       "5037  68.844878  17.854011  35.664846  12.420399  ...  5.963202  7.475336   \n",
       "5038  69.317508  18.067077  35.974236  12.383378  ...  5.979570  7.587729   \n",
       "5039  68.435840  17.970193  35.530772  12.481465  ...  6.117293  7.701709   \n",
       "5040  67.839311  18.272888  36.431316  12.442973  ...  6.136479  7.525097   \n",
       "\n",
       "            492        493        494        495        496        497  \\\n",
       "0      1.000000   1.000000   1.000000   1.000000   1.000000   1.000000   \n",
       "1      0.998085   0.995128   1.017331   0.996831   1.003477   0.989666   \n",
       "2      1.004085   0.983949   1.032396   1.007836   1.017010   1.002709   \n",
       "3      0.999793   1.010072   1.044427   0.994015   1.026441   0.993635   \n",
       "4      1.015675   1.011488   1.045346   1.003065   1.033255   1.012226   \n",
       "...         ...        ...        ...        ...        ...        ...   \n",
       "5036  11.274684  13.942188  21.932187  12.498953  31.365904  84.865420   \n",
       "5037  11.315851  13.989432  21.521183  12.231691  31.003417  86.675978   \n",
       "5038  11.260561  13.720756  21.412498  12.202708  31.148888  86.180539   \n",
       "5039  11.265846  13.544568  21.457789  12.196591  31.666826  86.783646   \n",
       "5040  11.281110  13.847669  21.530267  12.320445  31.981295  84.410279   \n",
       "\n",
       "            498        499  \n",
       "0      1.000000   1.000000  \n",
       "1      1.025417   1.009457  \n",
       "2      1.023491   1.007268  \n",
       "3      1.026954   1.004142  \n",
       "4      1.030498   1.007908  \n",
       "...         ...        ...  \n",
       "5036  48.912687  39.328659  \n",
       "5037  49.480191  39.480251  \n",
       "5038  49.721001  40.036864  \n",
       "5039  49.082852  40.443639  \n",
       "5040  48.126272  40.808662  \n",
       "\n",
       "[5041 rows x 500 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MC_n_years_optimized.calc_cumulative_return()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "21399b6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of portfolio projections\n"
     ]
    },
    {
     "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>Original</th>\n",
       "      <th>Optimized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500.00</td>\n",
       "      <td>500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.01</td>\n",
       "      <td>32.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.70</td>\n",
       "      <td>30.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.18</td>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.57</td>\n",
       "      <td>12.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.19</td>\n",
       "      <td>22.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14.43</td>\n",
       "      <td>40.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>71.64</td>\n",
       "      <td>241.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95% CI Lower</th>\n",
       "      <td>2.27</td>\n",
       "      <td>5.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95% CI Upper</th>\n",
       "      <td>30.65</td>\n",
       "      <td>104.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Original  Optimized\n",
       "count           500.00     500.00\n",
       "mean             11.01      32.19\n",
       "std               7.70      30.86\n",
       "min               1.18       1.54\n",
       "25%               5.57      12.26\n",
       "50%               9.19      22.44\n",
       "75%              14.43      40.80\n",
       "max              71.64     241.75\n",
       "95% CI Lower      2.27       5.08\n",
       "95% CI Upper     30.65     104.79"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Summary statistics result comparison\n",
    "summary_statistics_combined = pd.concat([\n",
    "    MC_n_years_original.summarize_cumulative_return(),\n",
    "    MC_n_years_optimized.summarize_cumulative_return()\n",
    "], axis=1)\n",
    "summary_statistics_combined.columns=[\"Original\", \"Optimized\"]\n",
    "\n",
    "print(f\"Summary of portfolio projections\")\n",
    "round(summary_statistics_combined ,2)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2d62af89",
   "metadata": {},
   "outputs": [],
   "source": [
    "projection_compare = summary_statistics_combined.loc[[\"95% CI Upper\", \"mean\", \"95% CI Lower\"]]\n",
    "projection_compare = projection_compare * original_valuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ae0784b7",
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
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Portfoilio Projections at Retirement 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       "    var render_items = [{\"docid\":\"b3699647-ed49-4d94-8559-bfde2723bc34\",\"root_ids\":[\"1738\"],\"roots\":{\"1738\":\"b6e8d7a0-569f-4135-8601-2f38bfd7df07\"}}];\n",
       "    root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":BoxWhisker   [Variable]   (value)"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "1738"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formatter = NumeralTickFormatter(format=\"0,0\")\n",
    "\n",
    "projection_compare.hvplot.box(\n",
    "    rot = 45,\n",
    "    height = 500,\n",
    "    title = \"Comparing Portfoilio Projections at Retirement Age\",\n",
    "    ylabel = \"Portfolio Value\",\n",
    "    yformatter=formatter\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "33099fb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "projection_compare['Original'] = projection_compare['Original'].apply(lambda x: \"${:,.0f}k\".format(x/1000))\n",
    "projection_compare['Optimized'] = projection_compare['Optimized'].apply(lambda x: \"${:,.0f}k\".format(x/1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "82cb7ad6",
   "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>Original</th>\n",
       "      <th>Optimized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>95% CI Upper</th>\n",
       "      <td>$6,708k</td>\n",
       "      <td>$22,934k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>$2,410k</td>\n",
       "      <td>$7,045k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95% CI Lower</th>\n",
       "      <td>$496k</td>\n",
       "      <td>$1,111k</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Original Optimized\n",
       "95% CI Upper  $6,708k  $22,934k\n",
       "mean          $2,410k   $7,045k\n",
       "95% CI Lower    $496k   $1,111k"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "projection_compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef41aeeb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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