financial-planner.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial imports\n",
    "import os\n",
    "import requests\n",
    "import json\n",
    "import pandas as pd\n",
    "from dotenv import load_dotenv\n",
    "import alpaca_trade_api as tradeapi\n",
    "from MCForecastTools import MCSimulation\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load .env enviroment variables\n",
    "load_dotenv()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Personal Finance Planner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collecting Crypto Prices Using the `requests` Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set current amount of crypto assets\n",
    "my_btc = 1.2\n",
    "my_eth = 5.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Crypto API URLs\n",
    "btc_url = \"https://api.alternative.me/v2/ticker/Bitcoin/?convert=CAD\"\n",
    "eth_url = \"https://api.alternative.me/v2/ticker/Ethereum/?convert=CAD\"\n",
    "\n",
    "btc_url = btc_url + \"?format=json\"\n",
    "eth_url = eth_url + \"?format=json\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetch current BTC price\n",
    "btc_price = requests.get(btc_url)\n",
    "\n",
    "# Fetch current ETH price\n",
    "eth_price = requests.get(eth_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract response / Retrieve output for BTC & ETH in order to get names of rows\n",
    "btc_response = btc_price.content\n",
    "eth_response = eth_price.content\n",
    "\n",
    "# Convert output to JSON\n",
    "btc_data = btc_price.json()\n",
    "eth_data = eth_price.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current value of your 1.2 BTC is $15328.94.\n",
      "The current value of your 5.3 ETH is $2087.56.\n"
     ]
    }
   ],
   "source": [
    "# Fetch prices of both BTC and ETH in order to compute total value of crypto portfolio\n",
    "btc_price = btc_data['data']['1']['quotes']['USD']['price']\n",
    "eth_price = eth_data['data']['1027']['quotes']['USD']['price']\n",
    "\n",
    "# Compute current value of my crypto\n",
    "total_btc_value = my_btc * btc_price\n",
    "total_eth_value = my_eth * eth_price\n",
    "\n",
    "# Print current crypto wallet balance\n",
    "print(f\"The current value of your {my_btc} BTC is ${total_btc_value:0.2f}.\")\n",
    "print(f\"The current value of your {my_eth} ETH is ${total_eth_value:0.2f}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collecting Investments Data Using Alpaca: `SPY` (stocks) and `AGG` (bonds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Current amount of shares\n",
    "my_spy = 50\n",
    "my_agg = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set Alpaca API key and secret key\n",
    "\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",
    "api = tradeapi.REST(\n",
    "    alpaca_api_key,\n",
    "    alpaca_secret_key,\n",
    "    api_version=\"v2\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">AGG</th>\n",
       "      <th colspan=\"5\" halign=\"left\">SPY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-10-21 00:00:00-04:00</th>\n",
       "      <td>117.59</td>\n",
       "      <td>117.64</td>\n",
       "      <td>117.48</td>\n",
       "      <td>117.515</td>\n",
       "      <td>2910530</td>\n",
       "      <td>343.33</td>\n",
       "      <td>345.67</td>\n",
       "      <td>342.4</td>\n",
       "      <td>342.69</td>\n",
       "      <td>46745662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              AGG                                       SPY  \\\n",
       "                             open    high     low    close   volume    open   \n",
       "2020-10-21 00:00:00-04:00  117.59  117.64  117.48  117.515  2910530  343.33   \n",
       "\n",
       "                                                            \n",
       "                             high    low   close    volume  \n",
       "2020-10-21 00:00:00-04:00  345.67  342.4  342.69  46745662  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format current date as ISO format\n",
    "today = pd.Timestamp(\"2020-10-21\", tz=\"America/New_York\").isoformat()\n",
    "\n",
    "# Set the tickers\n",
    "tickers = [\"AGG\", \"SPY\"]\n",
    "\n",
    "# Set timeframe to '1D' for Alpaca API\n",
    "timeframe = \"1D\"\n",
    "\n",
    "# Get current closing prices for SPY and AGG\n",
    "df_portfolio = api.get_barset(\n",
    "    tickers,\n",
    "    timeframe,\n",
    "    start = today,\n",
    "    end = today\n",
    ").df\n",
    "\n",
    "# Preview DataFrame\n",
    "df_portfolio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current AGG closing price: $117.515\n",
      "Current SPY closing price: $342.69\n"
     ]
    }
   ],
   "source": [
    "# Pick AGG and SPY close prices\n",
    "agg_close_price = df_portfolio[\"AGG\"][\"close\"].item()\n",
    "spy_close_price = df_portfolio[\"SPY\"][\"close\"].item()\n",
    "\n",
    "# Print AGG and SPY close prices\n",
    "print(f\"Current AGG closing price: ${agg_close_price}\")\n",
    "print(f\"Current SPY closing price: ${spy_close_price}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current value of your 50 SPY shares is $17134.50.\n",
      "The current value of your 200 AGG shares is $23503.00.\n"
     ]
    }
   ],
   "source": [
    "# Compute the current value of shares\n",
    "my_spy_value = my_spy * spy_close_price\n",
    "my_agg_value = my_agg * agg_close_price\n",
    "\n",
    "# Print current value of shares\n",
    "print(f\"The current value of your {my_spy} SPY shares is ${my_spy_value:0.2f}.\")\n",
    "print(f\"The current value of your {my_agg} AGG shares is ${my_agg_value:0.2f}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Savings Health Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Crypto</th>\n",
       "      <td>17416.508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shares</th>\n",
       "      <td>40637.500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Amount\n",
       "Crypto  17416.508\n",
       "Shares  40637.500"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set monthly household income\n",
    "monthly_income = 12000\n",
    "\n",
    "# Create savings DataFrame\n",
    "# First, create dictionary that holds the sum of BTC/ETH as well as SPY/AGG\n",
    "total_portfolio_dictionary = {'Amount': [total_btc_value+total_eth_value, my_spy_value+my_agg_value]}\n",
    "\n",
    "# Now, create savings DataFrame incorporating above variable\n",
    "df_savings = pd.DataFrame(total_portfolio_dictionary,index=['Crypto','Shares'])\n",
    "\n",
    "# Display savings DataFrame\n",
    "display(df_savings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x11451b3d0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+Yoinw3eYE+4MPI3bGcGz8qThpBLZ+qbtHD7zNeDntkMci9wsariHZsvbeNns0CJdKjb1bohEY9+xHaKjcq+o3f7cnv0La2lSYK2nzzay2AKv9jrLraJ2wkNwV33wjS5SN7RA3ktuojPVHiHgwQ5MQ2xd7hS1E+6EO0DjiKtxeNns0LMpm0VTfcZgPNQhqUnuFDXchTsxu++cH1g10HYGH5sVicbOtx2iPXKjqJ3wGcD1tmOkSzepLYjIzvdt5/CxX3vpNNz/Re2E83Cvo339Wb8TXKTX1ekzGPiF7RDJ8vUvesIPgCLbIdKtLPhqzszBZcm3I9HYV2yHSIa/i9oJFwI3246RCT04WHQCu3fazuFzv45EY1l/o9W/Re2EBXfRsqQXePcyEWRWaLH2LkuvfDxwGu7fonaXGT3LdohMuii4opftDDlgZiQaG287xNH4s6jdm2M/sR0j03rxSXF/Pt5tO0cOKLcd4Gj8WdQwBzjZdohMEyEwM/TnTbZz5IDSbJ4txX9F7YTDwC22Y9jyjeBfPDuU1GPuytYhmlkZ6hhFgT62Q9jSh+pRvajR+cvSrxj4pu0QR+KvonbCfYDrbMewSYTQ9ODzb9jOkSNuycbWOusCHaN/BbrZDmHbFaGXs2pVER8rAL5hO0RL/lnK1gl3Bd4F+tmOYpsxHB5V++uDNXTP+o4SPrAeKKkqL8uaQvJTSz0DLWgARMibGnx5g+0cOaIYuNB2iOb8UdROOAjcYDtGNrky+GLQdoYccqPtAM35o6jdyeJ0cvtmBsvukm4c2m87R444KxKNZU2/CL8U9bdtB8g2InS9NLhM19vKDAH+xXaIJt4vaif8BcBTM1NkyvTg8422M+SQabYDNPF+Ubv/Qur14xEMkZ2jOnP4kO0cOeLkSDSWFQOI/FDUM2wHyFYi9PhacMU62zlyyHTbAcDrRe2ExwPDbMfIZjODzx22nSGHXBqJxrrYDuHtonbHTKujGC7vF4eor7OdI0eEgYtsh/B6UU+xHSDbiRAuC/yt0naOHGL9Lrh3i9oJFwO66mMSvh1afNB2hhzy5Ug0ZnUedu8WdZZ1zctmI6WqKEBjg+0cOSIInGczgBZ1DgiI6fOlwGq9C545pTYP7s2idheM9+USOulyTShWYztDDtGi7oAJuF3zVJJGy1vDwS/jbLNeQSQaszZi0KtFndVTtGajoJgBEwLrtC945lhrrb1a1GfaDuBFs4OLdO6yzJlg68DeK2on3A0YbTuGF40LbNLhqZmjLXU7nA6EbIfwopA05o+TjRtt58gRJbaWv22zqEXkpWRey6AvWjy2580OLdplO0OOCGLp3k+rLZ6IdMGdmbOviBzPp3ebewJfyEC21oyyeGzPOyuwXnvhZU4p8FymD3q0lno2sBp3GtTVzbb/BX6Z/mitGmnx2J6XJw0nlchWXR0zM4bbOGirRW2MudcYMwT4N2PMycaYIYmtxBjznxnM+Cl3gkErPyg/mR1atMN2hhxxko2DtnnDyRizQETGA5Hm+xtjHk5jrtZEyJH1ptNpUmDtYNsZcsSJNg6azI2yR4C7cdd6/ufENjbNuVqjEyKkQBepG1og722znSMH9LMxaUIyj4bGAkUmO5byyJppWL1udujZ966v+1f9eaaX4LbWGb2Hkcxz6g2A1fGhzQyyHcAvzg+sypa/U7/L+Cl4Mi11X2CjiKwEapteNMbYGPo4wMIxfamb1BZEZOf7VWaQXl+nV1YWtZPuEO3Q33YAP/lOcNG2aP3VWtTplfE74Mnc/V6WiSBJ0pY6hcqCr/aN1l9tO4bfZbylTubud42IVCe2QyLSICLVmQh3BNpSp1APDhadwO6dtnP4XPYVtTHmOGNMz8TWBbgEsNP5xL2+VykigswKLdbeZenVK9MHbPcoLWPM08CkNGRJhnY8SbGLgisy/kuXYzpl+oBtXlOLyNebfRnAfW5t65l1xn9AfteLT4r78/HuXRxvbfodn8v472wyLfWUZtv5QA3uetCZ5YQDeHP8d1YTITAz9OdNtnP4WPa11MaYbFmATlvpNPlG8C89yuun2o7hV9lX1CKSDyzAnRfMAK8A3zPGfJDmbC1pUadJH6pHb+t85V7bOfyoEamBzE4Nl0znk98CvwMuTXx9VeK1TK9CoFMCp4kIIpjetnP4UQCT8fnWk7lG7WeM+a0xpj6xPQjYuKlywMIxlTpWjZk+YDJF/ZGIXCUiwcR2FbAn3cE+x4k3ALrQm/KajP/OJlPUM4HLgA+BncA3Eq/ZoEvHKK/J+L2KZO5+v0f2LEZXg3YVVd6S8QUUkrn7PQS4ls9PZ2Sj0D+xcEyljkX2tdTA08BvgGexcNHfgi4bo7wmK4v6kDFmftqTJOd92wGUaqesLOp7ReQ24Hk+O/PJmrSlat17Fo6p1LHI+NDWZIq6GJiGOzKr6fTbYGeklha18pqtmT5gMkV9MXCyMeZwusMkQYtaec3bmT5gMs+pK7Ew0LsV79oOoFQ7HAK2Z/qgybTUA4DNIvIan15TG2NM5odfQhXQgLuioFLZbhtOPONzDyRT1Lc1+3/BXanjm+mJ0wYnfhAn/CZQaOX4SrVPxq+nIbk5ypYBcaAMeBA4F/hVemMd1esWj61Ue6y3cdCjrU89DLgCt1XeA/wBEGPMxAxla83r2DpTUKp9XrNx0KOdfm8GlgNTjDFbAUTk+oykOrq1tgMolaS/2Tjo0U6/L8EdmVUhIr8WkXPJjokK9PRbecEHOPEPbRz4aIvOLzTGXA4UAEuB64EBIvJfIvLlDOX7PCe+G9BlWFW2s3LqDcndKNtvjHnMGDMZyMdtKaNpT3Z0L1s+vlJtWWnrwO2actcYs9cYc78xxtZk/k1esnx8pdpireHx6jzaL2NvQQGl2vIRsMrWwb1Z1E58F7DBdgylWvE8Ttza3APeLGqXnoKrbPWczYN7uaiftR1AqSMwwBKbAbxc1MuAv9sOoVQLqxOXh9Z4t6jdecD/ZDuGUi08bjuAd4va9UfbAZRqphEt6mP2F9yurEplg5dx4hmfk6wlbxe1+9jgCdsxlEp41HYA8HpRux6wHUAp3DWzsuIej/eL2omvw9IQN6WaeQInnhVrvXm/qF02Z2JRCuBe2wGa+KWof4/b31YpG5bjxG0sbnFE/ihqJ34I+LXtGCpn3WM7QHP+KGrXAtx5lpXKpHdwF5HMGv4pavf5oN4JV5k23+aIrCPxT1G7ymm2iJ9SabYTuN92iJb8VdROfDvaWqvMuRMnftB2iJb8VdQuba1VJrxDlt6c9V9RO/EPgP+2HUP53u048TrbIY7Ef0XtcnBXFVEqHTaTJf28j8SfRe3E9wI/sh1D+dYNifH8WcmfRe26H13NQ6Xe/+LE/2w7xNH4t6jdZ4fX2Y6hfOUA8H3bIdri36IGcOLLgcdsx1C+cTtOvMp2iLb4u6hd30NnR1HHbh3wC9shkuH/onbie4BZtmMoTzsMzMCJ19sOkgz/FzWAE48B/2M7hvKsW7JpaGVbcqOoXd8H3rUdQnnOS8DdtkO0R+4UtTvVzHQga58vqqyzF5iOE/fUYoy5U9QATnwZ8EPbMZRnXJMYJOQpYoyn/hFKDSf8JHCJ7Rgqq/0CJ36j7RAdkVst9admAJtsh1BZ6zlgru0QHZWbLTWAEx4OrAR62o6issoWYBxOPG47SEflaksNTnwLMBXwxLNHlRH7gAu9XNCQy0UNTc+vr7EdQ2WFOuBynPibtoMcq9wuagAn/lvgZtsxlFWNwFU48edtB0kFLWoAJ34XWbTCgsoog/voyjfLImtRf+p6dERXLroRJ/4b2yFSSYu6idtraDrwkO0oKmNux4n/h+0QqaZF3Zw7Rc0MdMG9XHAzTtyxHSIdcvc5dVuc8H/ggVkuVLs1At/Fift2xlltqVvjxK8HfmI7hkqpw8AVfi5o0Ja6bU74auCXQCfbUdQx2Q983S+PrY5GizoZTvgc4Cmgt+UkqmOqgItw4pW2g2SCnn4nw4kvBcbh9gtW3vIyMDZXChq0qJPnxLcCXwSW2I6iknYP8OXEPHU5Q0+/28sJB4AfALcDQctp1JHtx73D/YjtIDZoUXeUEy7FXU/pRNtR1GesxO3H/ZbtILbo6XdHuQsFlAC/tx1FAe4Q2tuBM3O5oEFb6tRwwpfhDggZaDtKjnoLt3VeaTtINvBESy0iPxSRN0RknYi8LiLjRKRKRPrazgaQGOFTgNu9VP+VzJzDwF3AqVrQn8r6llpEzsBd7uQcY0xtopDzgP8DxhpjPurAe4aMMemZ8cQJn4G76P3ItLy/arIEuM4Pkxqkmhda6kHAR8aYWgBjzEfGmB2J710rImtEZL2IFACIyOki8n8isjbx3+GJ178lIk+IyLPA8yLSXUT+R0ReS+z7tcR+I0RkZeKMYJ2InNKutE78r8AY4EbceaNVar2L2zPsK1rQR+aFlroH8ArQDXgR+IMxZpmIVAHzjDELRGQOMMYYM0tEegIHjDH1IvIl4LvGmEtE5FvAncAoY8xeEfkpsNEY86iI9MK9azoaKAdeNcY8JiJ5QNAYc7BD4Z1wGHdWyu8n8quO2wf8HLgHJ37AdphslvVFDSAiQaAUmAjMBqKAA5xpjNkuIuOAnxhjviQig4H5wCm417edjDEFiaI+2xgzI/Geq4AufDrxYG/gfNzC/iHwMPAnY8yx30l1woOAW3EX6gsd8/vllhpgAfBznPg+22G8wBO/YMaYBmApsFRE1uNOZgBQm/hvA59+ljuACmPMxSISSfy5Jvub/b8AlxhjWnb93CQifwPKgCUiMssY8/IxfQAnvhP4Lk74btwZVmagLXdbqnGL+Rc4cb2MaYesv6YWkeEtrmtP5egL3YWBpqVSvnWU/ZbgXpNL4jijE/89GdhmjJkPPAOM6mD0z3Pib+PE/x8wGPdsQNfN/rwtwLVAPk78Fi3o9sv6ogZ6AA+JyEYRWQcU4Z56t+ZnwF0isoKjd+O8A3c45ToR2ZD4GuByYIOIvI77mOrhY8z/eU58L078p8BJuK12rj+OaQQW4V7+FOLE/zOxoKHqAE9cU+cEJ1wA/AswDci3nCZTNgOPA4/ixLfZDuMXWtTZxh0wMhG3uCcDfewGSrl3cbvW/h4n/rrtMH6kRZ3N3AL/Iu5NuzLcvuZeUw+8CryAu/Dca15b79lrtKi9xAmfgHvdeQZusReRffdFGnBXFF0GPA8sxYlX242UW7SovcwJ9wROxy3wMcBwYCjQOUMJ6nDvVq8CVie2Su0cYpcWtd+4p+wnAcMSWwQYkNj6Accntu603sofxO3BFU9s+4C/A+/gzvf1TmLbnpgrXWURLepc5/4jEMQt8CBQhxOvsxtKHQstaqV8JttusiiljpEWtVI+o0WtlM9oUSvlM1rUSvmMFrVSPqNFrZTPaFEr5TNa1Er5jBa1Uj6jRa2Uz2hRK+UzWtRK+YwWtVI+o0WtlM9oUSvlM1rUSvmMFrVSPqNFrZTPaFEr5TNa1Er5jBa1Uj6jRa2Uz2hRK+UzWtRK+YwWtVI+o0WtlM/8fxlI9JYBbzcTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot savings pie chart\n",
    "df_savings.plot(kind='pie', subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Congratulations on having more than 3x your monthly income in your emergency fund.\n"
     ]
    }
   ],
   "source": [
    "# Set ideal emergency fund\n",
    "emergency_fund = monthly_income * 3\n",
    "\n",
    "# Calculate total amount of savings\n",
    "total_savings = df_savings.sum().item()\n",
    "\n",
    "# Validate saving health\n",
    "if total_savings > emergency_fund:\n",
    "    print(\"Congratulations on having more than 3x your monthly income in your emergency fund.\")\n",
    "elif total_savings == emergency_fund:\n",
    "    print(\"Congratulations on reaching your goal of 3x your monthly income in your emergency fund.\")\n",
    "else:\n",
    "    print(f'You are ${emergency_fund - total_savings} away from reaching your goal of 3x your monthly income in your emergency fund.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retirement Planning\n",
    "\n",
    "### Monte Carlo Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set start and end dates of five years back from today.\n",
    "# Sample results may vary from the solution based on the time frame chosen\n",
    "start_date = pd.Timestamp('2015-10-21', tz='America/New_York').isoformat()\n",
    "end_date = pd.Timestamp('2020-10-21', tz='America/New_York').isoformat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">AGG</th>\n",
       "      <th colspan=\"5\" halign=\"left\">SPY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-10-21 00:00:00-04:00</th>\n",
       "      <td>109.73</td>\n",
       "      <td>109.95</td>\n",
       "      <td>109.73</td>\n",
       "      <td>109.89</td>\n",
       "      <td>1119723</td>\n",
       "      <td>203.61</td>\n",
       "      <td>203.79</td>\n",
       "      <td>201.65</td>\n",
       "      <td>201.82</td>\n",
       "      <td>83704739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-22 00:00:00-04:00</th>\n",
       "      <td>109.90</td>\n",
       "      <td>110.05</td>\n",
       "      <td>109.83</td>\n",
       "      <td>109.94</td>\n",
       "      <td>1817244</td>\n",
       "      <td>202.98</td>\n",
       "      <td>205.51</td>\n",
       "      <td>202.81</td>\n",
       "      <td>205.22</td>\n",
       "      <td>121670959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-23 00:00:00-04:00</th>\n",
       "      <td>109.79</td>\n",
       "      <td>109.79</td>\n",
       "      <td>109.69</td>\n",
       "      <td>109.74</td>\n",
       "      <td>1344632</td>\n",
       "      <td>207.25</td>\n",
       "      <td>207.95</td>\n",
       "      <td>206.30</td>\n",
       "      <td>207.51</td>\n",
       "      <td>103135843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-26 00:00:00-04:00</th>\n",
       "      <td>109.85</td>\n",
       "      <td>109.90</td>\n",
       "      <td>109.74</td>\n",
       "      <td>109.79</td>\n",
       "      <td>1385981</td>\n",
       "      <td>207.30</td>\n",
       "      <td>207.37</td>\n",
       "      <td>206.56</td>\n",
       "      <td>206.97</td>\n",
       "      <td>51484043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-27 00:00:00-04:00</th>\n",
       "      <td>110.03</td>\n",
       "      <td>110.03</td>\n",
       "      <td>109.88</td>\n",
       "      <td>109.95</td>\n",
       "      <td>1791888</td>\n",
       "      <td>206.20</td>\n",
       "      <td>207.00</td>\n",
       "      <td>205.79</td>\n",
       "      <td>206.58</td>\n",
       "      <td>62899344</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              AGG                                      SPY  \\\n",
       "                             open    high     low   close   volume    open   \n",
       "2015-10-21 00:00:00-04:00  109.73  109.95  109.73  109.89  1119723  203.61   \n",
       "2015-10-22 00:00:00-04:00  109.90  110.05  109.83  109.94  1817244  202.98   \n",
       "2015-10-23 00:00:00-04:00  109.79  109.79  109.69  109.74  1344632  207.25   \n",
       "2015-10-26 00:00:00-04:00  109.85  109.90  109.74  109.79  1385981  207.30   \n",
       "2015-10-27 00:00:00-04:00  110.03  110.03  109.88  109.95  1791888  206.20   \n",
       "\n",
       "                                                              \n",
       "                             high     low   close     volume  \n",
       "2015-10-21 00:00:00-04:00  203.79  201.65  201.82   83704739  \n",
       "2015-10-22 00:00:00-04:00  205.51  202.81  205.22  121670959  \n",
       "2015-10-23 00:00:00-04:00  207.95  206.30  207.51  103135843  \n",
       "2015-10-26 00:00:00-04:00  207.37  206.56  206.97   51484043  \n",
       "2015-10-27 00:00:00-04:00  207.00  205.79  206.58   62899344  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get 5 years' worth of historical data for SPY and AGG\n",
    "df_stock_data = api.get_barset(\n",
    "    tickers,\n",
    "    timeframe,\n",
    "    start = start_date,\n",
    "    end = end_date\n",
    ").df\n",
    "\n",
    "# Display sample data\n",
    "df_stock_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuring a Monte Carlo simulation to forecast 30 years cumulative returns\n",
    "MC_thirty_year = MCSimulation(\n",
    "    portfolio_data = df_stock_data,\n",
    "    weights = [.40,.60],\n",
    "    num_simulation = 500,\n",
    "    num_trading_days = 252*30\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">AGG</th>\n",
       "      <th colspan=\"6\" halign=\"left\">SPY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>daily_return</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>daily_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-10-21 00:00:00-04:00</th>\n",
       "      <td>109.73</td>\n",
       "      <td>109.95</td>\n",
       "      <td>109.73</td>\n",
       "      <td>109.89</td>\n",
       "      <td>1119723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>203.61</td>\n",
       "      <td>203.79</td>\n",
       "      <td>201.65</td>\n",
       "      <td>201.82</td>\n",
       "      <td>83704739</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-22 00:00:00-04:00</th>\n",
       "      <td>109.90</td>\n",
       "      <td>110.05</td>\n",
       "      <td>109.83</td>\n",
       "      <td>109.94</td>\n",
       "      <td>1817244</td>\n",
       "      <td>0.000455</td>\n",
       "      <td>202.98</td>\n",
       "      <td>205.51</td>\n",
       "      <td>202.81</td>\n",
       "      <td>205.22</td>\n",
       "      <td>121670959</td>\n",
       "      <td>0.016847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-23 00:00:00-04:00</th>\n",
       "      <td>109.79</td>\n",
       "      <td>109.79</td>\n",
       "      <td>109.69</td>\n",
       "      <td>109.74</td>\n",
       "      <td>1344632</td>\n",
       "      <td>-0.001819</td>\n",
       "      <td>207.25</td>\n",
       "      <td>207.95</td>\n",
       "      <td>206.30</td>\n",
       "      <td>207.51</td>\n",
       "      <td>103135843</td>\n",
       "      <td>0.011159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-26 00:00:00-04:00</th>\n",
       "      <td>109.85</td>\n",
       "      <td>109.90</td>\n",
       "      <td>109.74</td>\n",
       "      <td>109.79</td>\n",
       "      <td>1385981</td>\n",
       "      <td>0.000456</td>\n",
       "      <td>207.30</td>\n",
       "      <td>207.37</td>\n",
       "      <td>206.56</td>\n",
       "      <td>206.97</td>\n",
       "      <td>51484043</td>\n",
       "      <td>-0.002602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-27 00:00:00-04:00</th>\n",
       "      <td>110.03</td>\n",
       "      <td>110.03</td>\n",
       "      <td>109.88</td>\n",
       "      <td>109.95</td>\n",
       "      <td>1791888</td>\n",
       "      <td>0.001457</td>\n",
       "      <td>206.20</td>\n",
       "      <td>207.00</td>\n",
       "      <td>205.79</td>\n",
       "      <td>206.58</td>\n",
       "      <td>62899344</td>\n",
       "      <td>-0.001884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              AGG                                   \\\n",
       "                             open    high     low   close   volume   \n",
       "2015-10-21 00:00:00-04:00  109.73  109.95  109.73  109.89  1119723   \n",
       "2015-10-22 00:00:00-04:00  109.90  110.05  109.83  109.94  1817244   \n",
       "2015-10-23 00:00:00-04:00  109.79  109.79  109.69  109.74  1344632   \n",
       "2015-10-26 00:00:00-04:00  109.85  109.90  109.74  109.79  1385981   \n",
       "2015-10-27 00:00:00-04:00  110.03  110.03  109.88  109.95  1791888   \n",
       "\n",
       "                                           SPY                          \\\n",
       "                          daily_return    open    high     low   close   \n",
       "2015-10-21 00:00:00-04:00          NaN  203.61  203.79  201.65  201.82   \n",
       "2015-10-22 00:00:00-04:00     0.000455  202.98  205.51  202.81  205.22   \n",
       "2015-10-23 00:00:00-04:00    -0.001819  207.25  207.95  206.30  207.51   \n",
       "2015-10-26 00:00:00-04:00     0.000456  207.30  207.37  206.56  206.97   \n",
       "2015-10-27 00:00:00-04:00     0.001457  206.20  207.00  205.79  206.58   \n",
       "\n",
       "                                                   \n",
       "                              volume daily_return  \n",
       "2015-10-21 00:00:00-04:00   83704739          NaN  \n",
       "2015-10-22 00:00:00-04:00  121670959     0.016847  \n",
       "2015-10-23 00:00:00-04:00  103135843     0.011159  \n",
       "2015-10-26 00:00:00-04:00   51484043    -0.002602  \n",
       "2015-10-27 00:00:00-04:00   62899344    -0.001884  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the simulation input data\n",
    "MC_thirty_year.portfolio_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
      "Running Monte Carlo simulation number 120.\n",
      "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",
      "Running Monte Carlo simulation number 300.\n",
      "Running Monte Carlo simulation number 310.\n",
      "Running Monte Carlo simulation number 320.\n",
      "Running Monte Carlo simulation number 330.\n",
      "Running Monte Carlo simulation number 340.\n",
      "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>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <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",
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       "      <td>1.004634</td>\n",
       "      <td>1.004794</td>\n",
       "      <td>0.997637</td>\n",
       "      <td>1.008061</td>\n",
       "      <td>1.004035</td>\n",
       "      <td>1.006898</td>\n",
       "      <td>0.991970</td>\n",
       "      <td>...</td>\n",
       "      <td>0.998831</td>\n",
       "      <td>0.990174</td>\n",
       "      <td>0.996348</td>\n",
       "      <td>0.988832</td>\n",
       "      <td>0.999214</td>\n",
       "      <td>1.001006</td>\n",
       "      <td>0.996773</td>\n",
       "      <td>1.012876</td>\n",
       "      <td>0.994925</td>\n",
       "      <td>0.996436</td>\n",
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       "      <td>0.994118</td>\n",
       "      <td>1.031845</td>\n",
       "      <td>1.007326</td>\n",
       "      <td>1.006377</td>\n",
       "      <td>0.985677</td>\n",
       "      <td>1.022640</td>\n",
       "      <td>1.008017</td>\n",
       "      <td>1.001438</td>\n",
       "      <td>0.995878</td>\n",
       "      <td>...</td>\n",
       "      <td>0.998247</td>\n",
       "      <td>0.993568</td>\n",
       "      <td>0.993154</td>\n",
       "      <td>0.993338</td>\n",
       "      <td>1.003746</td>\n",
       "      <td>1.003224</td>\n",
       "      <td>0.989501</td>\n",
       "      <td>1.020044</td>\n",
       "      <td>0.995093</td>\n",
       "      <td>1.010175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1.004694</td>\n",
       "      <td>1.036806</td>\n",
       "      <td>1.007881</td>\n",
       "      <td>1.007624</td>\n",
       "      <td>0.989701</td>\n",
       "      <td>1.012041</td>\n",
       "      <td>1.010940</td>\n",
       "      <td>1.010252</td>\n",
       "      <td>0.999949</td>\n",
       "      <td>...</td>\n",
       "      <td>1.005555</td>\n",
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       "      <td>0.981554</td>\n",
       "      <td>1.001303</td>\n",
       "      <td>0.998509</td>\n",
       "      <td>1.011135</td>\n",
       "      <td>0.983916</td>\n",
       "      <td>1.020294</td>\n",
       "      <td>0.985955</td>\n",
       "      <td>1.026232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.984718</td>\n",
       "      <td>1.011315</td>\n",
       "      <td>1.046850</td>\n",
       "      <td>1.012583</td>\n",
       "      <td>1.008128</td>\n",
       "      <td>0.992152</td>\n",
       "      <td>1.004207</td>\n",
       "      <td>1.011684</td>\n",
       "      <td>1.017736</td>\n",
       "      <td>0.996850</td>\n",
       "      <td>...</td>\n",
       "      <td>1.005877</td>\n",
       "      <td>1.003422</td>\n",
       "      <td>0.986428</td>\n",
       "      <td>0.994502</td>\n",
       "      <td>0.996124</td>\n",
       "      <td>1.009252</td>\n",
       "      <td>0.979036</td>\n",
       "      <td>1.030427</td>\n",
       "      <td>0.980562</td>\n",
       "      <td>1.038958</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>7556</th>\n",
       "      <td>7.509816</td>\n",
       "      <td>7.544985</td>\n",
       "      <td>9.507046</td>\n",
       "      <td>19.536036</td>\n",
       "      <td>11.472317</td>\n",
       "      <td>14.486174</td>\n",
       "      <td>4.581281</td>\n",
       "      <td>4.388137</td>\n",
       "      <td>14.050528</td>\n",
       "      <td>8.044076</td>\n",
       "      <td>...</td>\n",
       "      <td>10.684405</td>\n",
       "      <td>8.648751</td>\n",
       "      <td>9.448848</td>\n",
       "      <td>3.655797</td>\n",
       "      <td>4.936023</td>\n",
       "      <td>18.621973</td>\n",
       "      <td>4.124304</td>\n",
       "      <td>9.387220</td>\n",
       "      <td>3.980043</td>\n",
       "      <td>14.785469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7557</th>\n",
       "      <td>7.535557</td>\n",
       "      <td>7.554440</td>\n",
       "      <td>9.416623</td>\n",
       "      <td>19.152060</td>\n",
       "      <td>11.604540</td>\n",
       "      <td>14.330169</td>\n",
       "      <td>4.635949</td>\n",
       "      <td>4.385319</td>\n",
       "      <td>13.773179</td>\n",
       "      <td>8.061356</td>\n",
       "      <td>...</td>\n",
       "      <td>10.599070</td>\n",
       "      <td>8.668854</td>\n",
       "      <td>9.459935</td>\n",
       "      <td>3.641122</td>\n",
       "      <td>4.860499</td>\n",
       "      <td>18.745474</td>\n",
       "      <td>4.132262</td>\n",
       "      <td>9.473374</td>\n",
       "      <td>3.955752</td>\n",
       "      <td>14.839292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7558</th>\n",
       "      <td>7.619862</td>\n",
       "      <td>7.540083</td>\n",
       "      <td>9.465590</td>\n",
       "      <td>19.093121</td>\n",
       "      <td>11.553650</td>\n",
       "      <td>14.478258</td>\n",
       "      <td>4.598068</td>\n",
       "      <td>4.429762</td>\n",
       "      <td>13.585132</td>\n",
       "      <td>8.014156</td>\n",
       "      <td>...</td>\n",
       "      <td>10.574194</td>\n",
       "      <td>8.779796</td>\n",
       "      <td>9.483142</td>\n",
       "      <td>3.671991</td>\n",
       "      <td>4.833449</td>\n",
       "      <td>18.524109</td>\n",
       "      <td>4.131206</td>\n",
       "      <td>9.479862</td>\n",
       "      <td>3.983105</td>\n",
       "      <td>14.974003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7559</th>\n",
       "      <td>7.709326</td>\n",
       "      <td>7.479555</td>\n",
       "      <td>9.380970</td>\n",
       "      <td>19.028649</td>\n",
       "      <td>11.567193</td>\n",
       "      <td>14.625168</td>\n",
       "      <td>4.607638</td>\n",
       "      <td>4.493710</td>\n",
       "      <td>13.635125</td>\n",
       "      <td>7.989497</td>\n",
       "      <td>...</td>\n",
       "      <td>10.543765</td>\n",
       "      <td>8.726478</td>\n",
       "      <td>9.402831</td>\n",
       "      <td>3.688203</td>\n",
       "      <td>4.816254</td>\n",
       "      <td>18.596524</td>\n",
       "      <td>4.106620</td>\n",
       "      <td>9.431688</td>\n",
       "      <td>3.989676</td>\n",
       "      <td>14.969370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7560</th>\n",
       "      <td>7.732749</td>\n",
       "      <td>7.530119</td>\n",
       "      <td>9.397025</td>\n",
       "      <td>19.300614</td>\n",
       "      <td>11.583042</td>\n",
       "      <td>14.514741</td>\n",
       "      <td>4.546373</td>\n",
       "      <td>4.477369</td>\n",
       "      <td>13.739769</td>\n",
       "      <td>7.967003</td>\n",
       "      <td>...</td>\n",
       "      <td>10.435493</td>\n",
       "      <td>8.722848</td>\n",
       "      <td>9.320513</td>\n",
       "      <td>3.722567</td>\n",
       "      <td>4.826221</td>\n",
       "      <td>18.583247</td>\n",
       "      <td>4.089134</td>\n",
       "      <td>9.477395</td>\n",
       "      <td>3.977579</td>\n",
       "      <td>14.926380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7561 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     0.991071  1.005072  1.014898   1.004634   1.004794   0.997637  1.008061   \n",
       "2     0.986311  0.994118  1.031845   1.007326   1.006377   0.985677  1.022640   \n",
       "3     0.994614  1.004694  1.036806   1.007881   1.007624   0.989701  1.012041   \n",
       "4     0.984718  1.011315  1.046850   1.012583   1.008128   0.992152  1.004207   \n",
       "...        ...       ...       ...        ...        ...        ...       ...   \n",
       "7556  7.509816  7.544985  9.507046  19.536036  11.472317  14.486174  4.581281   \n",
       "7557  7.535557  7.554440  9.416623  19.152060  11.604540  14.330169  4.635949   \n",
       "7558  7.619862  7.540083  9.465590  19.093121  11.553650  14.478258  4.598068   \n",
       "7559  7.709326  7.479555  9.380970  19.028649  11.567193  14.625168  4.607638   \n",
       "7560  7.732749  7.530119  9.397025  19.300614  11.583042  14.514741  4.546373   \n",
       "\n",
       "           7          8         9    ...        490       491       492  \\\n",
       "0     1.000000   1.000000  1.000000  ...   1.000000  1.000000  1.000000   \n",
       "1     1.004035   1.006898  0.991970  ...   0.998831  0.990174  0.996348   \n",
       "2     1.008017   1.001438  0.995878  ...   0.998247  0.993568  0.993154   \n",
       "3     1.010940   1.010252  0.999949  ...   1.005555  0.995925  0.981554   \n",
       "4     1.011684   1.017736  0.996850  ...   1.005877  1.003422  0.986428   \n",
       "...        ...        ...       ...  ...        ...       ...       ...   \n",
       "7556  4.388137  14.050528  8.044076  ...  10.684405  8.648751  9.448848   \n",
       "7557  4.385319  13.773179  8.061356  ...  10.599070  8.668854  9.459935   \n",
       "7558  4.429762  13.585132  8.014156  ...  10.574194  8.779796  9.483142   \n",
       "7559  4.493710  13.635125  7.989497  ...  10.543765  8.726478  9.402831   \n",
       "7560  4.477369  13.739769  7.967003  ...  10.435493  8.722848  9.320513   \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     0.988832  0.999214   1.001006  0.996773  1.012876  0.994925   0.996436  \n",
       "2     0.993338  1.003746   1.003224  0.989501  1.020044  0.995093   1.010175  \n",
       "3     1.001303  0.998509   1.011135  0.983916  1.020294  0.985955   1.026232  \n",
       "4     0.994502  0.996124   1.009252  0.979036  1.030427  0.980562   1.038958  \n",
       "...        ...       ...        ...       ...       ...       ...        ...  \n",
       "7556  3.655797  4.936023  18.621973  4.124304  9.387220  3.980043  14.785469  \n",
       "7557  3.641122  4.860499  18.745474  4.132262  9.473374  3.955752  14.839292  \n",
       "7558  3.671991  4.833449  18.524109  4.131206  9.479862  3.983105  14.974003  \n",
       "7559  3.688203  4.816254  18.596524  4.106620  9.431688  3.989676  14.969370  \n",
       "7560  3.722567  4.826221  18.583247  4.089134  9.477395  3.977579  14.926380  \n",
       "\n",
       "[7561 rows x 500 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running a Monte Carlo simulation to forecast 30 years cumulative returns\n",
    "MC_thirty_year.calc_cumulative_return()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot simulation outcomes\n",
    "line_plot = MC_thirty_year.plot_simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot probability distribution and confidence intervals\n",
    "dist_plot = MC_thirty_year.plot_distribution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retirement Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count           500.000000\n",
      "mean             11.489296\n",
      "std               7.574982\n",
      "min               2.002780\n",
      "25%               6.352121\n",
      "50%               9.288875\n",
      "75%              14.882975\n",
      "max              52.852932\n",
      "95% CI Lower      3.084758\n",
      "95% CI Upper     31.608223\n",
      "Name: 7560, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Fetch summary statistics from the Monte Carlo simulation results\n",
    "tbl = MC_thirty_year.summarize_cumulative_return()\n",
    "\n",
    "# Print summary statistics\n",
    "print(tbl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating the expected portfolio return at the 95% lower and upper confidence intervals based on a `$20,000` initial investment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a 95% chance that an initial investment of $20000 in the portfolio over the next 30 years will end within in the range of $61695.16 and $632164.45.\n"
     ]
    }
   ],
   "source": [
    "# Set initial investment\n",
    "initial_investment = 20000\n",
    "\n",
    "# Use the lower and upper `95%` confidence intervals to calculate the range of the possible outcomes of our $20,000 initial investment\n",
    "ci_lower = round(tbl[8]*initial_investment,2)\n",
    "ci_upper = round(tbl[9]*initial_investment,2)\n",
    "\n",
    "# Print results\n",
    "print(f\"There is a 95% chance that an initial investment of ${initial_investment} in the portfolio\"\n",
    "      f\" over the next 30 years will end within in the range of\"\n",
    "      f\" ${ci_lower} and ${ci_upper}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating the expected portfolio return at the `95%` lower and upper confidence intervals based on a `50%` increase in the initial investment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a 95% chance that an initial investment of $30000.0 in the portfolio over the next 30 years will end within in the range of $92542.74 and $948246.68.\n"
     ]
    }
   ],
   "source": [
    "# Set initial investment\n",
    "initial_investment = 20000 * 1.5\n",
    "\n",
    "# Use the lower and upper `95%` confidence intervals to calculate the range of the possible outcomes of our $30,000 initial investment\n",
    "ci_lower = round(tbl[8]*initial_investment,2)\n",
    "ci_upper = round(tbl[9]*initial_investment,2)\n",
    "\n",
    "# Print results\n",
    "print(f\"There is a 95% chance that an initial investment of ${initial_investment} in the portfolio\"\n",
    "      f\" over the next 30 years will end within in the range of\"\n",
    "      f\" ${ci_lower} and ${ci_upper}.\")"
   ]
  }
 ],
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   "display_name": "Python [conda env:alpacaenv]",
   "language": "python",
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   "codemirror_mode": {
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