portfolio_planner.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime, timedelta\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Portfolio Planner\n",
    "\n",
    "In this activity, you will use the iexfinance api to grab historical data for a 60/40 portfolio using `SPY` to represent the stock portion and `AGG` to represent the bonds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from iexfinance.stocks import get_historical_data\n",
    "import iexfinance as iex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#http://finance.yahoo.com/q?s=AAPL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Collection\n",
    "\n",
    "In this step, you will need to use the IEX api to fetch closing prices for the `SPY` and `AGG` tickers. Save the results as a pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SPY</th>\n",
       "      <th>AGG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-28</th>\n",
       "      <td>263.76</td>\n",
       "      <td>106.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-29</th>\n",
       "      <td>263.41</td>\n",
       "      <td>106.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-30</th>\n",
       "      <td>267.58</td>\n",
       "      <td>107.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31</th>\n",
       "      <td>269.93</td>\n",
       "      <td>107.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-01</th>\n",
       "      <td>270.06</td>\n",
       "      <td>106.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SPY     AGG\n",
       "date                      \n",
       "2019-01-28  263.76  106.62\n",
       "2019-01-29  263.41  106.90\n",
       "2019-01-30  267.58  107.14\n",
       "2019-01-31  269.93  107.46\n",
       "2019-02-01  270.06  106.97"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_tickers = [\"SPY\", \"AGG\"]\n",
    "\n",
    "# Set start and end datetimes of 1 year, between now and 365 days ago.\n",
    "end_date = datetime.now()\n",
    "start_date = end_date + timedelta(-365)\n",
    "\n",
    "# Get 1 year's worth of historical data for SPY\n",
    "spy_df = get_historical_data(list_of_tickers[0], start_date, end_date, close_only=True, output_format='pandas')\n",
    "spy_df.drop(['volume'],axis='columns',inplace=True)\n",
    "\n",
    "# Get 1 year's worth of historical data for AGG\n",
    "agg_df = get_historical_data(list_of_tickers[1], start_date, end_date, close_only=True, output_format='pandas')\n",
    "agg_df.drop(['volume'],axis='columns',inplace=True)\n",
    "\n",
    "close_df = pd.concat([spy_df,agg_df], join='inner', axis='columns')\n",
    "\n",
    "close_df.columns = ['SPY','AGG']\n",
    "\n",
    "close_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monte Carlo Simulation\n",
    "\n",
    "In this step, you will run Monte Carlo Simulations for your portfolio to model portfolio performance at different retirement ages. \n",
    "\n",
    "Complete the following steps:\n",
    "1. Calculate the daily returns for the SPY and AGG closing prices.\n",
    "2. Calculate volatility for both the SPY and AGG closing prices.\n",
    "3. Find the last day's closing price for both stocks and save those as variables.\n",
    "4. Run a Monte Carlo Simulation of at least 500 iterations and generate at least 30 years of closing prices\n",
    "\n",
    "### HINTS:\n",
    "There are 252 trading days per year, so the number of records to generate for each Monte Carlo run will be 252 days * 30 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00027375476127896605"
      ]
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the daily roi for the stocks\n",
    "returns_df = close_df.pct_change().dropna()\n",
    "\n",
    "avg_daily_return_spy = returns_df.mean()['SPY']\n",
    "avg_daily_return_agg = returns_df.mean()['AGG']\n",
    "\n",
    "\n",
    "avg_daily_return_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.002094524695851093"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate volatility\n",
    "std_dev_daily_return_spy = returns_df.std()['SPY']\n",
    "std_dev_daily_return_agg = returns_df.std()['AGG']\n",
    "\n",
    "std_dev_daily_return_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the last day's closing price\n",
    "spy_last_price = returns_df['SPY'][-1]\n",
    "agg_last_price = returns_df['AGG'][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the Monte Carlo Parameters\n",
    "number_simulations = 500\n",
    "number_records = 252 * 30\n",
    "monte_carlo = pd.DataFrame()\n",
    "portfolio_cumulative_returns = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "      <th>499</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.005439</td>\n",
       "      <td>1.006493</td>\n",
       "      <td>1.008858</td>\n",
       "      <td>0.996775</td>\n",
       "      <td>1.001805</td>\n",
       "      <td>0.999272</td>\n",
       "      <td>1.002716</td>\n",
       "      <td>1.000636</td>\n",
       "      <td>1.007361</td>\n",
       "      <td>0.999478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.997900</td>\n",
       "      <td>1.003015</td>\n",
       "      <td>0.996560</td>\n",
       "      <td>0.994421</td>\n",
       "      <td>1.002971</td>\n",
       "      <td>0.997557</td>\n",
       "      <td>1.001414</td>\n",
       "      <td>1.000414</td>\n",
       "      <td>0.996477</td>\n",
       "      <td>0.999914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.008586</td>\n",
       "      <td>0.998097</td>\n",
       "      <td>1.003741</td>\n",
       "      <td>0.986887</td>\n",
       "      <td>1.001149</td>\n",
       "      <td>1.007256</td>\n",
       "      <td>0.999342</td>\n",
       "      <td>1.000480</td>\n",
       "      <td>0.997385</td>\n",
       "      <td>0.997184</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999344</td>\n",
       "      <td>1.000054</td>\n",
       "      <td>0.996212</td>\n",
       "      <td>0.994942</td>\n",
       "      <td>1.002983</td>\n",
       "      <td>1.001230</td>\n",
       "      <td>0.998795</td>\n",
       "      <td>0.993666</td>\n",
       "      <td>0.994692</td>\n",
       "      <td>0.998378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.008070</td>\n",
       "      <td>1.001031</td>\n",
       "      <td>1.007529</td>\n",
       "      <td>0.981325</td>\n",
       "      <td>1.001058</td>\n",
       "      <td>1.015133</td>\n",
       "      <td>1.007026</td>\n",
       "      <td>0.998348</td>\n",
       "      <td>0.998540</td>\n",
       "      <td>0.996012</td>\n",
       "      <td>...</td>\n",
       "      <td>0.993240</td>\n",
       "      <td>0.999027</td>\n",
       "      <td>0.999873</td>\n",
       "      <td>1.000264</td>\n",
       "      <td>1.006284</td>\n",
       "      <td>1.000288</td>\n",
       "      <td>0.997092</td>\n",
       "      <td>0.992947</td>\n",
       "      <td>0.994163</td>\n",
       "      <td>0.996727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.009652</td>\n",
       "      <td>0.996491</td>\n",
       "      <td>1.006538</td>\n",
       "      <td>0.978788</td>\n",
       "      <td>1.008599</td>\n",
       "      <td>1.005794</td>\n",
       "      <td>1.015724</td>\n",
       "      <td>0.997709</td>\n",
       "      <td>1.007217</td>\n",
       "      <td>0.990798</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999157</td>\n",
       "      <td>0.996321</td>\n",
       "      <td>0.994686</td>\n",
       "      <td>1.001074</td>\n",
       "      <td>1.010651</td>\n",
       "      <td>1.003701</td>\n",
       "      <td>0.999675</td>\n",
       "      <td>0.993066</td>\n",
       "      <td>0.999363</td>\n",
       "      <td>1.002971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 500 columns</p>\n",
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      "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  1.005439  1.006493  1.008858  0.996775  1.001805  0.999272  1.002716   \n",
       "2  1.008586  0.998097  1.003741  0.986887  1.001149  1.007256  0.999342   \n",
       "3  1.008070  1.001031  1.007529  0.981325  1.001058  1.015133  1.007026   \n",
       "4  1.009652  0.996491  1.006538  0.978788  1.008599  1.005794  1.015724   \n",
       "\n",
       "        7         8         9    ...       490       491       492       493  \\\n",
       "0  1.000000  1.000000  1.000000  ...  1.000000  1.000000  1.000000  1.000000   \n",
       "1  1.000636  1.007361  0.999478  ...  0.997900  1.003015  0.996560  0.994421   \n",
       "2  1.000480  0.997385  0.997184  ...  0.999344  1.000054  0.996212  0.994942   \n",
       "3  0.998348  0.998540  0.996012  ...  0.993240  0.999027  0.999873  1.000264   \n",
       "4  0.997709  1.007217  0.990798  ...  0.999157  0.996321  0.994686  1.001074   \n",
       "\n",
       "        494       495       496       497       498       499  \n",
       "0  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  \n",
       "1  1.002971  0.997557  1.001414  1.000414  0.996477  0.999914  \n",
       "2  1.002983  1.001230  0.998795  0.993666  0.994692  0.998378  \n",
       "3  1.006284  1.000288  0.997092  0.992947  0.994163  0.996727  \n",
       "4  1.010651  1.003701  0.999675  0.993066  0.999363  1.002971  \n",
       "\n",
       "[5 rows x 500 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run the Monte Carlo Simulation\n",
    "for x in range(number_simulations):\n",
    "    \n",
    "    # Initialize the simulated prices list with the last closing price of `SPY` and `AGG`\n",
    "    simulated_spy_prices = [spy_last_price]\n",
    "    simulated_agg_prices = [agg_last_price]\n",
    "    \n",
    "    # Simulate the returns for 252 * 30 days\n",
    "    for i in range(number_records):\n",
    "        \n",
    "        # Calculate the simulated price using the last price within the list\n",
    "        simulated_spy_price = simulated_spy_prices[-1] * (1 + np.random.normal(avg_daily_return_spy, std_dev_daily_return_spy))\n",
    "        simulated_agg_price = simulated_agg_prices[-1] * (1 + np.random.normal(avg_daily_return_agg, std_dev_daily_return_agg))\n",
    "        \n",
    "        # Append the simulated price to the list\n",
    "        simulated_spy_prices.append(simulated_spy_price)\n",
    "        simulated_agg_prices.append(simulated_agg_price)\n",
    "    \n",
    "    # Append a simulated price of each simulation to the Monte Carlo DataFrame\n",
    "    monte_carlo[\"SPY prices\"] = pd.Series(simulated_spy_prices)\n",
    "    monte_carlo[\"AGG prices\"] = pd.Series(simulated_agg_prices)\n",
    "    \n",
    "    # Calculate the daily returns of simulated prices\n",
    "    simulated_daily_returns = monte_carlo.pct_change()\n",
    "    \n",
    "    # Set the portfolio weights (60% SPY; 40% AGG)\n",
    "    weights = [0.60, 0.40]\n",
    "\n",
    "    # Multiply weights with each column's simulated daily returns\n",
    "    portfolio_daily_returns = simulated_daily_returns.dot(weights)\n",
    "    \n",
    "    # Calculate the normalized cumulative return series\n",
    "    portfolio_cumulative_returns[x] = (1 + portfolio_daily_returns.fillna(0)).cumprod()\n",
    "\n",
    "# Print records\n",
    "portfolio_cumulative_returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the Simulation\n",
    "portfolio_cumulative_returns.plot(legend=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    118.235745\n",
       "1     96.921071\n",
       "2    112.441703\n",
       "3    174.933398\n",
       "4    121.966198\n",
       "Name: 7560, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the last row for the cumulative returns (cumulative returns at 30 years)\n",
    "ending_cumulative_returns = portfolio_cumulative_returns.iloc[-1, :]\n",
    "ending_cumulative_returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    25.164168\n",
       "1    20.009425\n",
       "2    20.260119\n",
       "3    51.631232\n",
       "4    20.316466\n",
       "Name: 5042, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the last row for the cumulative returns (cumulative returns at 20 years)\n",
    "ending_cumulative_returns_20 = portfolio_cumulative_returns.iloc[-2519, :]\n",
    "ending_cumulative_returns_20.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5    93.582147\n",
       "Name: 7560, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the 90% confidence interval for the ending returns\n",
    "confidence_interval = ending_cumulative_returns.quantile(q=[0.05, 0.95])\n",
    "confidence_interval\n",
    "\n",
    "# 50% confidence interval\n",
    "confidence_interval_50 = ending_cumulative_returns.quantile(q=[.5])\n",
    "confidence_interval_50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x12d708c90>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zIUU8UVY/jph7phrQV0v5OU0GEhER/WOq5OIO5YiIiI6meiz285IeorqDeUopU7Zt+5RGo4uIiDlp0uRie95MBRIREf3jWN7nEtG3Ok0a2H/F2TMcSUR/6HbhyoiIiK41mlwkrZG0V9KopI1t9i+UdG3Zv1PSYMu+i0v9Xkmva6k/VdJ2Sd+UdJeklzfZh4iIOHaNJZey4OVVwFnACuCNklZMaHYh8IDtM4ArgU3l2BVUrzx+IbAG+Eg5H8DvA//b9guAnwfuaqoPERExPU2OuawCRm3vA5C0DVgL7Glpsxa4tJS3Ax+WpFK/zfajwN2SRoFVkvYArwL+LYDtI8CRBvsw6+QLhRExFzT5WGwJcE/L9oFS17aN7XHgQWDxJMcuA8aAj0v6qqSP5a2YERGzz1wb0J9PtRzNR22/lGqF5p8aywGQtF7SiKSRsbGxmYwxIuKE12RyOQic3rK9tNS1bSNpPrAIODTJsQeAA7Z3lvrtlLXPJrK92faQ7aGBgYHj7EpERByLJpPLLmC5pGWSFlAN0A9PaDMMXFDK5wI32XapX1dmky0DlgO32v474B5Jzy/HrOaJYzgRETELNDagb3tc0gbgBmAesMX2bkmXASO2h4Grga1lwP5+qgREaXcdVeIYBy4qb8QE+E/Ap0vC2ge8pak+RETE9DT6DX3bO4AdE+ouaSkfBs7rcOzlwOVt6u8AhuqNNCIi6jTXBvQjImIOSHKJiIjaJblERETtklwiIqJ2SS4REVG7JJeIiKhdkktERNQuySUiImqX5BIREbVLcomIiNoluURERO2SXCIionaNLlwZMdd1eq30/ivOnuFIIuaW3LlERETtklwiIqJ2SS4REVG7RpOLpDWS9koalbSxzf6Fkq4t+3dKGmzZd3Gp3yvpdROOmyfpq5L+V5PxR0TE9DQ2oC9pHnAVcCZwANgladh26zvvLwQesH2GpHXAJuB8SSuoXnn8QuBZwI2SntfyquO3A3cBpzQV/0w5OmC8bd8hANaV7QwYN6PTAH3T58/1jBNNk3cuq4BR2/tsHwG2AWsntFkLXFPK24HVklTqt9l+1PbdwGg5H5KWAmcDH2sw9oiIOA5NJpclwD0t2wdKXds2tseBB4HFUxz7QeC/Aj+e7JdLWi9pRNLI2NjYdPsQERHTMKcG9CX9MnCf7dumamt7s+0h20MDAwMzEF1ERBzVZHI5CJzesr201LVtI2k+sAg4NMmxrwDeIGk/1WO210j6VBPBR0TE9DWZXHYByyUtk7SAaoB+eEKbYeCCUj4XuMm2S/26MptsGbAcuNX2xbaX2h4s57vJ9psa7ENERExDY7PFbI9L2gDcAMwDttjeLekyYMT2MHA1sFXSKHA/VcKgtLsO2AOMAxe1zBSLiIhZrtG1xWzvAHZMqLukpXwYOK/DsZcDl09y7i8BX6ojzoiIqNecGtCPiIi5Iasiz5Cmv7wXMyvXM2JyuXOJiIjaJblERETtklwiIqJ2SS4REVG7DOjPUhkwjoi5LHcuERFRuySXiIioXZJLRETULsklIiJqlwH9iFnoWCd05DXKMdvkziUiImqX5BIREbVLcomIiNo1mlwkrZG0V9KopI1t9i+UdG3Zv1PSYMu+i0v9XkmvK3WnS7pZ0h5JuyW9vcn4IyJiehob0Jc0D7gKOBM4AOySNGx7T0uzC4EHbJ8haR2wCThf0gqqt1K+EHgWcKOk51G9lfK/2L5d0tOA2yR9ccI5eyrfrI/Z5Oifx237DgGwruXPZyYBRJOavHNZBYza3mf7CLANWDuhzVrgmlLeDqyWpFK/zfajtu8GRoFVtu+1fTuA7e8DdwFLGuxDRERMQ5PJZQlwT8v2AX46ETzexvY48CCwuJtjyyO0lwI7a4w5IiJqMCcH9CU9Ffgs8A7bD3Vos17SiKSRsbGxmQ0wIuIE1+SXKA8Cp7dsLy117dockDQfWAQcmuxYSU+mSiyftv25Tr/c9mZgM8DQ0JCPqydtZGwljkWnPy8Z94h+1eSdyy5guaRlkhZQDdAPT2gzDFxQyucCN9l2qV9XZpMtA5YDt5bxmKuBu2x/oMHYIyLiODR252J7XNIG4AZgHrDF9m5JlwEjtoepEsVWSaPA/VQJiNLuOmAP1Qyxi2w/JumVwL8BviHpjvKr3mV7R1P9iIiIY9fo2mLlH/0dE+ouaSkfBs7rcOzlwOUT6m4BVH+kERFRpzk5oB8REbNbVkWO6AO9nGBS12SFTHroL7lziYiI2iW5RERE7ZJcIiKidkkuERFRuwzoTyHfxI8mzcY/XxlYjzrkziUiImqX5BIREbVLcomIiNplzCUiutKr8aF8SXNuyp1LRETULsklIiJql+QSERG1S3KJiIjaZUA/ImZUXRMDZuMXUHtlNk5WaPTORdIaSXsljUra2Gb/QknXlv07JQ227Lu41O+V9LpuzxkREb3XWHKRNA+4CjgLWAG8UdKKCc0uBB6wfQZwJbCpHLuC6pXHLwTWAB+RNK/Lc0ZERI81eeeyChi1vc/2EWAbsHZCm7XANaW8HVgtSaV+m+1Hbd8NjJbzdXPOiIjosSaTyxLgnpbtA6WubRvb48CDwOJJju3mnBER0WN9O6AvaT2wvmw+LGlvL+OZysuPFjb9MsBpwHd7FkxNJvSpnb7oZxd63s9210Kbav81T+hnA+dvxDTi7Pn17NZxXoPTgJ+b7sFNJpeDwOkt20tLXbs2ByTNBxYBh6Y4dqpzAmB7M7B5usH3kqQR20O9jqNp6Wd/ST/7S+nn4HSPb/Kx2C5guaRlkhZQDdAPT2gzDFxQyucCN9l2qV9XZpMtA5YDt3Z5zoiI6LHG7lxsj0vaANwAzAO22N4t6TJgxPYwcDWwVdIocD9VsqC0uw7YA4wDF9l+DKDdOZvqQ0RETI+qG4WYTSStL4/1+lr62V/Sz/5yvP1McomIiNplbbGIiKhdksssIGm/pG9IukPSSKl7uqQvSvp2+fmzvY7zWEnaIuk+SXe21LXtlyofKsv6fF3Syt5Ffmw69PNSSQfLNb1D0utb9rVd2mi2k3S6pJsl7ZG0W9LbS31fXdNJ+tlX11TSSZJulfS10s//VuqXleW4RsvyXAtKfcflutqynU+PP8B+4LQJdf8T2FjKG4FNvY5zGv16FbASuHOqfgGvB/4SEPAyYGev4z/Ofl4KvLNN2xXA14CFwDLg/wHzet2HLvv5TGBlKT8N+FbpT19d00n62VfXtFyXp5byk4Gd5TpdB6wr9X8I/EYpvw34w1JeB1w72flz5zJ7tS6Ncw1wTg9jmRbb/4dqFmCrTv1aC3zSla8Ap0p65sxEenw69LOTTksbzXq277V9eyl/H7iLaoWMvrqmk/Szkzl5Tct1ebhsPrl8DLyGajku+Onr2W65rraSXGYHA1+QdFtZWQDgGbbvLeW/A57Rm9Bq16lf/bi0z4byOGhLy2PNvuhneSTyUqr/7fbtNZ3QT+iza6pqQeA7gPuAL1LddX3P1XJc8MS+dFquq60kl9nhlbZXUq32fJGkV7XudHUf2nfT+vq1X8VHgecCLwHuBd7f23DqI+mpwGeBd9h+qHVfP13TNv3su2tq+zHbL6Fa7WQV8IK6zp3kMgvYPlh+3gf8KdVF/vujjxDKz/t6F2GtOvWrm+WC5gzbf1/+4v4Y+GN+8phkTvdT0pOp/sH9tO3Pleq+u6bt+tmv1xTA9veAm6mWoTtV1XJc8MS+PN5PPXG5rraSXHpM0s9IetrRMvBa4E6euDTOBcCf9ybC2nXq1zDw5jLD6GXAgy2PWuacCWMLv0p1TaHz0kazXnm+fjVwl+0PtOzqq2vaqZ/9dk0lDUg6tZSfApxJNb50M9VyXPDT17Pdcl3t9XrGwon+AZ5DNdPka8Bu4N2lfjHwV8C3gRuBp/c61mn07TNUjw9+RPXs9sJO/aKauXIV1TPfbwBDvY7/OPu5tfTj6+Uv5TNb2r+79HMvcFav4z+Gfr6S6pHX14E7yuf1/XZNJ+lnX11T4MXAV0t/7gQuKfXPoUqOo8D1wMJSf1LZHi37nzPZ+fMN/YiIqF0ei0VERO2SXCIionZJLhERUbskl4iIqF2SS0RE1C7JJSIiapfkEhERtUtyiYiI2v1/YMmam/v+cbcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the distribution of the ending returns\n",
    "plt.figure();\n",
    "ending_cumulative_returns.plot(kind='hist', density=True, bins=50)\n",
    "plt.axvline(confidence_interval.iloc[0], color='r')\n",
    "plt.axvline(confidence_interval.iloc[1], color='r')\n",
    "plt.axvline(confidence_interval_50.iloc[0], color='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Retirement Analysis\n",
    "\n",
    "In this section, you will use the monte carlo model to answer the following retirement planning questions:\n",
    "\n",
    "1. What are the expected cumulative returns at 30 years for the 10th, 50th, and 90th percentiles?\n",
    "2. Given an initial investment of `$20,000`, what is the expected portfolio return in dollars at the 10th, 50th, and 90th percentiles?\n",
    "3. Given the current projected annual income from the Plaid analysis, will a 4% withdraw rate from the retirement portfolio meet or exceed that value at the 10th percentile?\n",
    "4. How would a 50% increase in the initial investment amount affect the 4% retirement withdrawal?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What are the expected cumulative returns at 30 years for the 10th, 50th, and 90th percentiles?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At the 10th percentile, the expected cumulative return is ~49%\n",
      "At the 50th percentile, the expected cumulative return is ~95%\n",
      "At the 90th percentile, the expected cumulative return is ~180%\n"
     ]
    }
   ],
   "source": [
    "print('At the 10th percentile, the expected cumulative return is ~49%')\n",
    "print('At the 50th percentile, the expected cumulative return is ~95%')\n",
    "print('At the 90th percentile, the expected cumulative return is ~180%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Given an initial investment of `$20,000`, what is the expected portfolio return in dollars at the 10th, 50th, and 90th percentiles?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At the 10th percentile, the expected portfolio return is $9800\n",
      "At the 50th percentile, the expected portfolio return is $19000\n",
      "At the 90th percentile, the expected portfolio return is $36000\n"
     ]
    }
   ],
   "source": [
    "print(f'At the 10th percentile, the expected portfolio return is ${int(20000*.49)}')\n",
    "print(f'At the 50th percentile, the expected portfolio return is ${int(20000*.95)}')\n",
    "print(f'At the 90th percentile, the expected portfolio return is ${int(20000*1.80)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Given the current projected annual income from the Plaid analysis, will a 4% withdraw rate from the retirement portfolio meet or exceed that value at the 10th percentile?\n",
    "\n",
    "Note: This is effectively saying that 90% of the expected returns will be greater than the return at the 10th percentile, so this can help measure the uncertainty about having enough funds at retirement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total income after 30 years at 10th percentile with a $20,000 investment:\n",
      "29800.0\n",
      "Total withdrawal of 4% per year over 30 years:\n",
      "24000.0\n",
      "The retirement portfolio will exceed the value of the cumulative withdrawals.\n"
     ]
    }
   ],
   "source": [
    "print('Total income after 30 years at 10th percentile with a $20,000 investment:')\n",
    "_10th_pct_income = 20000*1.49\n",
    "print(_10th_pct_income)\n",
    "print('Total withdrawal of 4% per year over 30 years:')\n",
    "withdrawal = 20000*.04*30\n",
    "print(withdrawal)\n",
    "\n",
    "if _10th_pct_income > withdrawal:\n",
    "    print('The retirement portfolio will exceed the value of the cumulative withdrawals.')\n",
    "else:\n",
    "    print('The retirement portfolio will NOT exceed the value of the cumulative withdrawals.')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How would a 50% increase in the initial investment amount affect the 4% retirement withdrawal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total income after 30 years at 10th percentile with a $20,000 investment:\n",
      "29800.0\n",
      "Total withdrawal of 4% per year over 30 years:\n",
      "24000.0\n",
      "A 50% increase will have NO effect on the withdrawal.\n"
     ]
    }
   ],
   "source": [
    "print('Total income after 30 years at 10th percentile with a $20,000 investment:')\n",
    "_10th_pct_income_50 = 30000*1.49\n",
    "print(_10th_pct_income)\n",
    "print('Total withdrawal of 4% per year over 30 years:')\n",
    "withdrawal_50 = 30000*.04*30\n",
    "print(withdrawal)\n",
    "\n",
    "if (withdrawal_50/30000) == (withdrawal/20000):\n",
    "    print('A 50% increase will have NO effect on the withdrawal.')\n",
    "else:\n",
    "    print('A 50% increase will have an effect on the withdrawal.')"
   ]
  }
 ],
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