Financial_Planner_PStanescki.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unit 5 - Financial Planning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial imports\n",
    "import os\n",
    "import requests\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": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load .env enviroment variables\n",
    "load_dotenv()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1 - Personal Finance Planner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collect Crypto Prices Using the `requests` Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set current amount of crypto assets\n",
    "my_btc = 1.2\n",
    "my_eth = 5.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"data\": {\n",
      "        \"1\": {\n",
      "            \"id\": 1,\n",
      "            \"name\": \"Bitcoin\",\n",
      "            \"symbol\": \"BTC\",\n",
      "            \"website_slug\": \"bitcoin\",\n",
      "            \"rank\": 1,\n",
      "            \"circulating_supply\": 18588650,\n",
      "            \"total_supply\": 18588650,\n",
      "            \"max_supply\": 21000000,\n",
      "            \"quotes\": {\n",
      "                \"USD\": {\n",
      "                    \"price\": 32565.0,\n",
      "                    \"volume_24h\": 48461999221,\n",
      "                    \"market_cap\": 605337696383,\n",
      "                    \"percentage_change_1h\": -1.31961651054179,\n",
      "                    \"percentage_change_24h\": 11.6089166960232,\n",
      "                    \"percentage_change_7d\": 31.9961365927112,\n",
      "                    \"percent_change_1h\": -1.31961651054179,\n",
      "                    \"percent_change_24h\": 11.6089166960232,\n",
      "                    \"percent_change_7d\": 31.9961365927112\n",
      "                },\n",
      "                \"CAD\": {\n",
      "                    \"price\": 41478.0405,\n",
      "                    \"volume_24h\": 61726048407.7877,\n",
      "                    \"market_cap\": 771018623883.027,\n",
      "                    \"percent_change_1h\": -1.31961651054179,\n",
      "                    \"percent_change_24h\": 11.6089166960232,\n",
      "                    \"percent_change_7d\": 31.9961365927112\n",
      "                }\n",
      "            },\n",
      "            \"last_updated\": 1609618133\n",
      "        }\n",
      "    },\n",
      "    \"metadata\": {\n",
      "        \"timestamp\": 1609618133,\n",
      "        \"num_cryptocurrencies\": 1389,\n",
      "        \"error\": null\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# Fetch current BTC price\n",
    "requests.get(btc_url)\n",
    "response_btc_data = requests.get(btc_url)\n",
    "current_btc_price = response_btc_data.content\n",
    "\n",
    "# Convert data to json format\n",
    "import json\n",
    "current_btc_price = response_btc_data.json()\n",
    "print(json.dumps(current_btc_price, indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32565.0\n"
     ]
    }
   ],
   "source": [
    "# Identify current BTC price\n",
    "current_btc_price = current_btc_price['data']['1']['quotes']['USD']['price']\n",
    "print(current_btc_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"data\": {\n",
      "        \"1027\": {\n",
      "            \"id\": 1027,\n",
      "            \"name\": \"Ethereum\",\n",
      "            \"symbol\": \"ETH\",\n",
      "            \"website_slug\": \"ethereum\",\n",
      "            \"rank\": 2,\n",
      "            \"circulating_supply\": 114089872,\n",
      "            \"total_supply\": 114089872,\n",
      "            \"max_supply\": 0,\n",
      "            \"quotes\": {\n",
      "                \"USD\": {\n",
      "                    \"price\": 781.1,\n",
      "                    \"volume_24h\": 16553808823,\n",
      "                    \"market_cap\": 89662484521,\n",
      "                    \"percentage_change_1h\": 0.0016439760277197,\n",
      "                    \"percentage_change_24h\": 7.17541668964417,\n",
      "                    \"percentage_change_7d\": 24.6857334639128,\n",
      "                    \"percent_change_1h\": 0.0016439760277197,\n",
      "                    \"percent_change_24h\": 7.17541668964417,\n",
      "                    \"percent_change_7d\": 24.6857334639128\n",
      "                },\n",
      "                \"CAD\": {\n",
      "                    \"price\": 994.88707,\n",
      "                    \"volume_24h\": 21084586297.8551,\n",
      "                    \"market_cap\": 114203106534.398,\n",
      "                    \"percent_change_1h\": 0.0016439760277197,\n",
      "                    \"percent_change_24h\": 7.17541668964417,\n",
      "                    \"percent_change_7d\": 24.6857334639128\n",
      "                }\n",
      "            },\n",
      "            \"last_updated\": 1609617936\n",
      "        }\n",
      "    },\n",
      "    \"metadata\": {\n",
      "        \"timestamp\": 1609617936,\n",
      "        \"num_cryptocurrencies\": 1389,\n",
      "        \"error\": null\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# Fetch current ETH price\n",
    "requests.get(eth_url)\n",
    "response_eth_data = requests.get(eth_url)\n",
    "current_eth_price = response_eth_data.content\n",
    "\n",
    "# Convert data to json format\n",
    "current_eth_price = response_eth_data.json()\n",
    "print(json.dumps(current_eth_price, indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "781.1\n"
     ]
    }
   ],
   "source": [
    "# Identify current ETH price\n",
    "current_eth_price = current_eth_price['data']['1027']['quotes']['USD']['price']\n",
    "print(current_eth_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current value of your 1.2 BTC is $39078.00\n",
      "The current value of your 5.3 ETH is $4139.83\n"
     ]
    }
   ],
   "source": [
    "# Compute current value of my crpto\n",
    "my_btc_value = my_btc * current_btc_price\n",
    "my_eth_value = my_eth * current_eth_price\n",
    "\n",
    "# Print current crypto wallet balance\n",
    "print(f\"The current value of your {my_btc} BTC is ${my_btc_value:0.2f}\")\n",
    "print(f\"The current value of your {my_eth} ETH is ${my_eth_value:0.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collect Investments Data Using Alpaca: `SPY` (stocks) and `AGG` (bonds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Current amount of shares\n",
    "my_spy = 50\n",
    "my_agg = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpaca key type: <class 'str'>\n",
      "Alpaca secret key type: <class 'str'>\n"
     ]
    }
   ],
   "source": [
    "# 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",
    "# Verify if the keys are successfully downloaded\n",
    "print(f\"Alpaca key type: {type(alpaca_api_key)}\")\n",
    "print(f\"Alpaca secret key type: {type(alpaca_secret_key)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the Alpaca API object - Function 'Rest'\n",
    "alpaca = tradeapi.REST(\n",
    "    alpaca_api_key,\n",
    "    alpaca_secret_key,\n",
    "    api_version=\"v2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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 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>2020-12-31 00:00:00-05:00</th>\n",
       "      <td>118.18</td>\n",
       "      <td>118.24</td>\n",
       "      <td>118.12</td>\n",
       "      <td>118.2</td>\n",
       "      <td>4325026</td>\n",
       "      <td>371.78</td>\n",
       "      <td>374.66</td>\n",
       "      <td>371.232</td>\n",
       "      <td>373.85</td>\n",
       "      <td>52720911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              AGG                                     SPY  \\\n",
       "                             open    high     low  close   volume    open   \n",
       "2020-12-31 00:00:00-05:00  118.18  118.24  118.12  118.2  4325026  371.78   \n",
       "\n",
       "                                                              \n",
       "                             high      low   close    volume  \n",
       "2020-12-31 00:00:00-05:00  374.66  371.232  373.85  52720911  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format current date as ISO format\n",
    "today = pd.Timestamp(\"2020-12-31\", 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",
    "all_portfolios = alpaca.get_barset(\n",
    "    tickers,\n",
    "    timeframe,\n",
    "    start = today,\n",
    "    end = today\n",
    ").df\n",
    "\n",
    "# Preview DataFrame\n",
    "all_portfolios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current AGG closing price: $118.2\n",
      "Current SPY closing price: $373.85\n"
     ]
    }
   ],
   "source": [
    "# Pick AGG and SPY close prices\n",
    "agg_close_price = float(all_portfolios['AGG']['close'])\n",
    "spy_close_price = float(all_portfolios['SPY']['close'])\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": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current value of your 200 AGG shares is $23640.00\n",
      "The current value of your 50 SPY shares is $18692.50\n"
     ]
    }
   ],
   "source": [
    "# Compute the current value of shares\n",
    "my_agg_value = my_agg * agg_close_price\n",
    "my_spy_value = my_spy * spy_close_price\n",
    "# Print current value of share\n",
    "print(f\"The current value of your {my_agg} AGG shares is ${my_agg_value:0.2f}\")\n",
    "print(f\"The current value of your {my_spy} SPY shares is ${my_spy_value:0.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Savings Health Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>crypto</th>\n",
       "      <td>43217.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shares</th>\n",
       "      <td>42332.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          amount\n",
       "crypto  43217.83\n",
       "shares  42332.50"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set monthly household income\n",
    "monthly_income = 12000\n",
    "\n",
    "# Calculate my total crypto assets and shares\n",
    "my_total_crypto = my_btc_value + my_eth_value\n",
    "my_total_shares = my_agg_value + my_spy_value\n",
    "\n",
    "# Set the data for the shares value DataFrame\n",
    "value_data = {\n",
    "    \"amount\":[my_total_crypto, my_total_shares]\n",
    "} \n",
    "\n",
    "# Create savings DataFrame\n",
    "df_savings = pd.DataFrame(value_data, index =['crypto', 'shares'])\n",
    "display(df_savings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f93a2864310>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot savings pie chart\n",
    "df_savings.plot.pie(y=\"amount\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My total amount of saving is $85550.33\n",
      "Congratulations! you have enough money 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 = my_total_crypto + my_total_shares\n",
    "print(f\"My total amount of saving is ${total_savings:0.2f}\")\n",
    "\n",
    "# Validate saving health\n",
    "if total_savings > emergency_fund:\n",
    "    print(f\"Congratulations! you have enough money in your emergency fund\")\n",
    "    \n",
    "elif total_savings == emergency_fund:\n",
    "    print(f\"Congratulations! you have reached your financial goal\")\n",
    "    \n",
    "else:\n",
    "    print(f\"Sorry! you are still missing ${total_savings - emergency_fund:0.2f} away from your financial goal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2 - Retirement Planning\n",
    "\n",
    "### Monte Carlo Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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('2015-08-07', tz='America/New_York').isoformat()\n",
    "end_date = pd.Timestamp('2020-08-07', tz='America/New_York').isoformat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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 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>2015-08-07 00:00:00-04:00</th>\n",
       "      <td>109.14</td>\n",
       "      <td>109.2750</td>\n",
       "      <td>109.035</td>\n",
       "      <td>109.21</td>\n",
       "      <td>2041167</td>\n",
       "      <td>208.16</td>\n",
       "      <td>208.34</td>\n",
       "      <td>206.87</td>\n",
       "      <td>207.93</td>\n",
       "      <td>87669782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-10 00:00:00-04:00</th>\n",
       "      <td>109.15</td>\n",
       "      <td>109.1700</td>\n",
       "      <td>108.920</td>\n",
       "      <td>109.06</td>\n",
       "      <td>1149778</td>\n",
       "      <td>209.28</td>\n",
       "      <td>210.67</td>\n",
       "      <td>209.28</td>\n",
       "      <td>210.58</td>\n",
       "      <td>66755890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-11 00:00:00-04:00</th>\n",
       "      <td>109.42</td>\n",
       "      <td>109.5765</td>\n",
       "      <td>109.284</td>\n",
       "      <td>109.42</td>\n",
       "      <td>1420907</td>\n",
       "      <td>208.98</td>\n",
       "      <td>209.47</td>\n",
       "      <td>207.76</td>\n",
       "      <td>208.63</td>\n",
       "      <td>88424557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-12 00:00:00-04:00</th>\n",
       "      <td>109.55</td>\n",
       "      <td>109.7100</td>\n",
       "      <td>109.350</td>\n",
       "      <td>109.36</td>\n",
       "      <td>1468979</td>\n",
       "      <td>207.11</td>\n",
       "      <td>209.14</td>\n",
       "      <td>205.36</td>\n",
       "      <td>208.89</td>\n",
       "      <td>136171450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-13 00:00:00-04:00</th>\n",
       "      <td>109.36</td>\n",
       "      <td>109.3651</td>\n",
       "      <td>109.110</td>\n",
       "      <td>109.15</td>\n",
       "      <td>1465173</td>\n",
       "      <td>208.73</td>\n",
       "      <td>209.55</td>\n",
       "      <td>208.01</td>\n",
       "      <td>208.63</td>\n",
       "      <td>77197796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              AGG                                         SPY  \\\n",
       "                             open      high      low   close   volume    open   \n",
       "2015-08-07 00:00:00-04:00  109.14  109.2750  109.035  109.21  2041167  208.16   \n",
       "2015-08-10 00:00:00-04:00  109.15  109.1700  108.920  109.06  1149778  209.28   \n",
       "2015-08-11 00:00:00-04:00  109.42  109.5765  109.284  109.42  1420907  208.98   \n",
       "2015-08-12 00:00:00-04:00  109.55  109.7100  109.350  109.36  1468979  207.11   \n",
       "2015-08-13 00:00:00-04:00  109.36  109.3651  109.110  109.15  1465173  208.73   \n",
       "\n",
       "                                                              \n",
       "                             high     low   close     volume  \n",
       "2015-08-07 00:00:00-04:00  208.34  206.87  207.93   87669782  \n",
       "2015-08-10 00:00:00-04:00  210.67  209.28  210.58   66755890  \n",
       "2015-08-11 00:00:00-04:00  209.47  207.76  208.63   88424557  \n",
       "2015-08-12 00:00:00-04:00  209.14  205.36  208.89  136171450  \n",
       "2015-08-13 00:00:00-04:00  209.55  208.01  208.63   77197796  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set timeframe to '1D'\n",
    "timeframe = \"1D\"\n",
    "\n",
    "# Get 5 years' worth of historical data for SPY and AGG\n",
    "tickers = [\"AGG\",\"SPY\"]\n",
    "\n",
    "df_stock_data = alpaca.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": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuring a Monte Carlo simulation to forecast 30 years cumulative returns\n",
    "MC_30_years = 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": 129,
   "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 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=\"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>2015-08-07 00:00:00-04:00</th>\n",
       "      <td>109.14</td>\n",
       "      <td>109.2750</td>\n",
       "      <td>109.035</td>\n",
       "      <td>109.21</td>\n",
       "      <td>2041167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>208.16</td>\n",
       "      <td>208.34</td>\n",
       "      <td>206.87</td>\n",
       "      <td>207.93</td>\n",
       "      <td>87669782</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-10 00:00:00-04:00</th>\n",
       "      <td>109.15</td>\n",
       "      <td>109.1700</td>\n",
       "      <td>108.920</td>\n",
       "      <td>109.06</td>\n",
       "      <td>1149778</td>\n",
       "      <td>-0.001374</td>\n",
       "      <td>209.28</td>\n",
       "      <td>210.67</td>\n",
       "      <td>209.28</td>\n",
       "      <td>210.58</td>\n",
       "      <td>66755890</td>\n",
       "      <td>0.012745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-11 00:00:00-04:00</th>\n",
       "      <td>109.42</td>\n",
       "      <td>109.5765</td>\n",
       "      <td>109.284</td>\n",
       "      <td>109.42</td>\n",
       "      <td>1420907</td>\n",
       "      <td>0.003301</td>\n",
       "      <td>208.98</td>\n",
       "      <td>209.47</td>\n",
       "      <td>207.76</td>\n",
       "      <td>208.63</td>\n",
       "      <td>88424557</td>\n",
       "      <td>-0.009260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-12 00:00:00-04:00</th>\n",
       "      <td>109.55</td>\n",
       "      <td>109.7100</td>\n",
       "      <td>109.350</td>\n",
       "      <td>109.36</td>\n",
       "      <td>1468979</td>\n",
       "      <td>-0.000548</td>\n",
       "      <td>207.11</td>\n",
       "      <td>209.14</td>\n",
       "      <td>205.36</td>\n",
       "      <td>208.89</td>\n",
       "      <td>136171450</td>\n",
       "      <td>0.001246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-13 00:00:00-04:00</th>\n",
       "      <td>109.36</td>\n",
       "      <td>109.3651</td>\n",
       "      <td>109.110</td>\n",
       "      <td>109.15</td>\n",
       "      <td>1465173</td>\n",
       "      <td>-0.001920</td>\n",
       "      <td>208.73</td>\n",
       "      <td>209.55</td>\n",
       "      <td>208.01</td>\n",
       "      <td>208.63</td>\n",
       "      <td>77197796</td>\n",
       "      <td>-0.001245</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              AGG                                      \\\n",
       "                             open      high      low   close   volume   \n",
       "2015-08-07 00:00:00-04:00  109.14  109.2750  109.035  109.21  2041167   \n",
       "2015-08-10 00:00:00-04:00  109.15  109.1700  108.920  109.06  1149778   \n",
       "2015-08-11 00:00:00-04:00  109.42  109.5765  109.284  109.42  1420907   \n",
       "2015-08-12 00:00:00-04:00  109.55  109.7100  109.350  109.36  1468979   \n",
       "2015-08-13 00:00:00-04:00  109.36  109.3651  109.110  109.15  1465173   \n",
       "\n",
       "                                           SPY                          \\\n",
       "                          daily_return    open    high     low   close   \n",
       "2015-08-07 00:00:00-04:00          NaN  208.16  208.34  206.87  207.93   \n",
       "2015-08-10 00:00:00-04:00    -0.001374  209.28  210.67  209.28  210.58   \n",
       "2015-08-11 00:00:00-04:00     0.003301  208.98  209.47  207.76  208.63   \n",
       "2015-08-12 00:00:00-04:00    -0.000548  207.11  209.14  205.36  208.89   \n",
       "2015-08-13 00:00:00-04:00    -0.001920  208.73  209.55  208.01  208.63   \n",
       "\n",
       "                                                   \n",
       "                              volume daily_return  \n",
       "2015-08-07 00:00:00-04:00   87669782          NaN  \n",
       "2015-08-10 00:00:00-04:00   66755890     0.012745  \n",
       "2015-08-11 00:00:00-04:00   88424557    -0.009260  \n",
       "2015-08-12 00:00:00-04:00  136171450     0.001246  \n",
       "2015-08-13 00:00:00-04:00   77197796    -0.001245  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the simulation input data\n",
    "MC_30_years.portfolio_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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": {
      "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>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>0.994999</td>\n",
       "      <td>0.979198</td>\n",
       "      <td>1.009429</td>\n",
       "      <td>1.012919</td>\n",
       "      <td>0.998494</td>\n",
       "      <td>0.995940</td>\n",
       "      <td>0.999967</td>\n",
       "      <td>1.008479</td>\n",
       "      <td>1.017443</td>\n",
       "      <td>0.987114</td>\n",
       "      <td>...</td>\n",
       "      <td>0.985458</td>\n",
       "      <td>0.999605</td>\n",
       "      <td>1.004315</td>\n",
       "      <td>1.005786</td>\n",
       "      <td>0.996572</td>\n",
       "      <td>1.008138</td>\n",
       "      <td>0.988415</td>\n",
       "      <td>0.998940</td>\n",
       "      <td>0.993164</td>\n",
       "      <td>1.011103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.998787</td>\n",
       "      <td>0.968086</td>\n",
       "      <td>1.008712</td>\n",
       "      <td>1.023978</td>\n",
       "      <td>0.995223</td>\n",
       "      <td>1.002665</td>\n",
       "      <td>0.996173</td>\n",
       "      <td>1.002996</td>\n",
       "      <td>1.018015</td>\n",
       "      <td>0.984797</td>\n",
       "      <td>...</td>\n",
       "      <td>0.986461</td>\n",
       "      <td>0.993514</td>\n",
       "      <td>1.001237</td>\n",
       "      <td>1.013390</td>\n",
       "      <td>1.008626</td>\n",
       "      <td>1.006859</td>\n",
       "      <td>0.990516</td>\n",
       "      <td>0.999873</td>\n",
       "      <td>1.005687</td>\n",
       "      <td>1.008405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.999754</td>\n",
       "      <td>0.960033</td>\n",
       "      <td>1.017886</td>\n",
       "      <td>1.027497</td>\n",
       "      <td>0.991123</td>\n",
       "      <td>1.013292</td>\n",
       "      <td>0.995759</td>\n",
       "      <td>1.000815</td>\n",
       "      <td>1.008732</td>\n",
       "      <td>0.988057</td>\n",
       "      <td>...</td>\n",
       "      <td>0.992928</td>\n",
       "      <td>1.001032</td>\n",
       "      <td>1.005628</td>\n",
       "      <td>1.011390</td>\n",
       "      <td>0.995711</td>\n",
       "      <td>1.003208</td>\n",
       "      <td>0.986405</td>\n",
       "      <td>0.991440</td>\n",
       "      <td>1.004069</td>\n",
       "      <td>1.005910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.003966</td>\n",
       "      <td>0.969715</td>\n",
       "      <td>1.013189</td>\n",
       "      <td>1.023337</td>\n",
       "      <td>0.989938</td>\n",
       "      <td>1.010272</td>\n",
       "      <td>0.991841</td>\n",
       "      <td>1.005674</td>\n",
       "      <td>0.998230</td>\n",
       "      <td>0.993934</td>\n",
       "      <td>...</td>\n",
       "      <td>0.993530</td>\n",
       "      <td>0.998638</td>\n",
       "      <td>1.008330</td>\n",
       "      <td>1.005409</td>\n",
       "      <td>1.008426</td>\n",
       "      <td>1.000867</td>\n",
       "      <td>0.993470</td>\n",
       "      <td>0.989178</td>\n",
       "      <td>1.000545</td>\n",
       "      <td>1.002211</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>3.045359</td>\n",
       "      <td>10.031770</td>\n",
       "      <td>13.487463</td>\n",
       "      <td>21.459798</td>\n",
       "      <td>8.130467</td>\n",
       "      <td>7.759078</td>\n",
       "      <td>7.310078</td>\n",
       "      <td>25.193432</td>\n",
       "      <td>6.887439</td>\n",
       "      <td>3.933703</td>\n",
       "      <td>...</td>\n",
       "      <td>6.642832</td>\n",
       "      <td>7.334801</td>\n",
       "      <td>2.800257</td>\n",
       "      <td>4.403198</td>\n",
       "      <td>5.960541</td>\n",
       "      <td>6.444197</td>\n",
       "      <td>1.309505</td>\n",
       "      <td>6.182893</td>\n",
       "      <td>12.634843</td>\n",
       "      <td>7.347887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7557</th>\n",
       "      <td>3.064694</td>\n",
       "      <td>10.086958</td>\n",
       "      <td>13.398145</td>\n",
       "      <td>21.449467</td>\n",
       "      <td>8.034673</td>\n",
       "      <td>7.710009</td>\n",
       "      <td>7.390661</td>\n",
       "      <td>25.239325</td>\n",
       "      <td>6.992192</td>\n",
       "      <td>3.972751</td>\n",
       "      <td>...</td>\n",
       "      <td>6.649806</td>\n",
       "      <td>7.385733</td>\n",
       "      <td>2.813455</td>\n",
       "      <td>4.378268</td>\n",
       "      <td>6.029037</td>\n",
       "      <td>6.513433</td>\n",
       "      <td>1.324546</td>\n",
       "      <td>6.250140</td>\n",
       "      <td>12.593789</td>\n",
       "      <td>7.449264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7558</th>\n",
       "      <td>3.047649</td>\n",
       "      <td>10.196946</td>\n",
       "      <td>13.456110</td>\n",
       "      <td>21.547102</td>\n",
       "      <td>8.094521</td>\n",
       "      <td>7.761834</td>\n",
       "      <td>7.448410</td>\n",
       "      <td>25.221123</td>\n",
       "      <td>7.035734</td>\n",
       "      <td>3.977495</td>\n",
       "      <td>...</td>\n",
       "      <td>6.665326</td>\n",
       "      <td>7.393971</td>\n",
       "      <td>2.805877</td>\n",
       "      <td>4.323458</td>\n",
       "      <td>6.007334</td>\n",
       "      <td>6.502925</td>\n",
       "      <td>1.317158</td>\n",
       "      <td>6.215103</td>\n",
       "      <td>12.553576</td>\n",
       "      <td>7.462400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7559</th>\n",
       "      <td>3.060113</td>\n",
       "      <td>10.138927</td>\n",
       "      <td>13.421150</td>\n",
       "      <td>21.440706</td>\n",
       "      <td>8.171132</td>\n",
       "      <td>7.736561</td>\n",
       "      <td>7.460292</td>\n",
       "      <td>25.245535</td>\n",
       "      <td>7.063057</td>\n",
       "      <td>3.964958</td>\n",
       "      <td>...</td>\n",
       "      <td>6.593975</td>\n",
       "      <td>7.420558</td>\n",
       "      <td>2.820308</td>\n",
       "      <td>4.303546</td>\n",
       "      <td>6.000239</td>\n",
       "      <td>6.445084</td>\n",
       "      <td>1.295830</td>\n",
       "      <td>6.239587</td>\n",
       "      <td>12.506446</td>\n",
       "      <td>7.492534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7560</th>\n",
       "      <td>3.061726</td>\n",
       "      <td>10.001495</td>\n",
       "      <td>13.444678</td>\n",
       "      <td>21.444821</td>\n",
       "      <td>8.118510</td>\n",
       "      <td>7.780700</td>\n",
       "      <td>7.504416</td>\n",
       "      <td>25.468486</td>\n",
       "      <td>7.098525</td>\n",
       "      <td>3.983344</td>\n",
       "      <td>...</td>\n",
       "      <td>6.583930</td>\n",
       "      <td>7.424562</td>\n",
       "      <td>2.792682</td>\n",
       "      <td>4.304018</td>\n",
       "      <td>6.038765</td>\n",
       "      <td>6.468971</td>\n",
       "      <td>1.298446</td>\n",
       "      <td>6.247347</td>\n",
       "      <td>12.575114</td>\n",
       "      <td>7.430788</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     0.994999   0.979198   1.009429   1.012919  0.998494  0.995940  0.999967   \n",
       "2     0.998787   0.968086   1.008712   1.023978  0.995223  1.002665  0.996173   \n",
       "3     0.999754   0.960033   1.017886   1.027497  0.991123  1.013292  0.995759   \n",
       "4     1.003966   0.969715   1.013189   1.023337  0.989938  1.010272  0.991841   \n",
       "...        ...        ...        ...        ...       ...       ...       ...   \n",
       "7556  3.045359  10.031770  13.487463  21.459798  8.130467  7.759078  7.310078   \n",
       "7557  3.064694  10.086958  13.398145  21.449467  8.034673  7.710009  7.390661   \n",
       "7558  3.047649  10.196946  13.456110  21.547102  8.094521  7.761834  7.448410   \n",
       "7559  3.060113  10.138927  13.421150  21.440706  8.171132  7.736561  7.460292   \n",
       "7560  3.061726  10.001495  13.444678  21.444821  8.118510  7.780700  7.504416   \n",
       "\n",
       "            7         8         9    ...       490       491       492  \\\n",
       "0      1.000000  1.000000  1.000000  ...  1.000000  1.000000  1.000000   \n",
       "1      1.008479  1.017443  0.987114  ...  0.985458  0.999605  1.004315   \n",
       "2      1.002996  1.018015  0.984797  ...  0.986461  0.993514  1.001237   \n",
       "3      1.000815  1.008732  0.988057  ...  0.992928  1.001032  1.005628   \n",
       "4      1.005674  0.998230  0.993934  ...  0.993530  0.998638  1.008330   \n",
       "...         ...       ...       ...  ...       ...       ...       ...   \n",
       "7556  25.193432  6.887439  3.933703  ...  6.642832  7.334801  2.800257   \n",
       "7557  25.239325  6.992192  3.972751  ...  6.649806  7.385733  2.813455   \n",
       "7558  25.221123  7.035734  3.977495  ...  6.665326  7.393971  2.805877   \n",
       "7559  25.245535  7.063057  3.964958  ...  6.593975  7.420558  2.820308   \n",
       "7560  25.468486  7.098525  3.983344  ...  6.583930  7.424562  2.792682   \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     1.005786  0.996572  1.008138  0.988415  0.998940   0.993164  1.011103  \n",
       "2     1.013390  1.008626  1.006859  0.990516  0.999873   1.005687  1.008405  \n",
       "3     1.011390  0.995711  1.003208  0.986405  0.991440   1.004069  1.005910  \n",
       "4     1.005409  1.008426  1.000867  0.993470  0.989178   1.000545  1.002211  \n",
       "...        ...       ...       ...       ...       ...        ...       ...  \n",
       "7556  4.403198  5.960541  6.444197  1.309505  6.182893  12.634843  7.347887  \n",
       "7557  4.378268  6.029037  6.513433  1.324546  6.250140  12.593789  7.449264  \n",
       "7558  4.323458  6.007334  6.502925  1.317158  6.215103  12.553576  7.462400  \n",
       "7559  4.303546  6.000239  6.445084  1.295830  6.239587  12.506446  7.492534  \n",
       "7560  4.304018  6.038765  6.468971  1.298446  6.247347  12.575114  7.430788  \n",
       "\n",
       "[7561 rows x 500 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running a Monte Carlo simulation to forecast 30 years cumulative returns\n",
    "MC_30_years.calc_cumulative_return()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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_30_years.plot_simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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_30_years.plot_distribution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retirement Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count           500.000000\n",
      "mean              9.760391\n",
      "std               6.665067\n",
      "min               1.161588\n",
      "25%               5.511008\n",
      "50%               7.923181\n",
      "75%              11.922572\n",
      "max              41.243741\n",
      "95% CI Lower      2.157761\n",
      "95% CI Upper     26.744925\n",
      "Name: 7560, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Fetch summary statistics from the Monte Carlo simulation results\n",
    "thirty_yr_summary_stat = MC_30_years.summarize_cumulative_return()\n",
    "\n",
    "# Print summary statistics\n",
    "print(thirty_yr_summary_stat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the expected portfolio return at the 95% lower and upper confidence intervals based on a `$20,000` initial investment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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 $43155.22 and $534898.51\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\n",
    "ci_lower = round(thirty_yr_summary_stat[8]*20000,2)\n",
    "ci_upper = round(thirty_yr_summary_stat[9]*20000,2)\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": [
    "### Calculate 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": 135,
   "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 $64732.84 and $802347.76\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\n",
    "ci_lower = round(thirty_yr_summary_stat[8]*initial_investment,2)\n",
    "ci_upper = round(thirty_yr_summary_stat[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": [
    "## Optional Challenge - Early Retirement\n",
    "\n",
    "\n",
    "### Five Years Retirement Option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuring a Monte Carlo simulation to forecast 5 years cumulative returns\n",
    "MC_5_years = MCSimulation(\n",
    "    portfolio_data = df_stock_data,\n",
    "    weights = [.40,.60],\n",
    "    num_simulation = 500,\n",
    "    num_trading_days = 252*5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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": {
      "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>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>0.998368</td>\n",
       "      <td>1.002007</td>\n",
       "      <td>0.993729</td>\n",
       "      <td>0.995364</td>\n",
       "      <td>1.004523</td>\n",
       "      <td>1.010519</td>\n",
       "      <td>0.994388</td>\n",
       "      <td>0.996950</td>\n",
       "      <td>0.993060</td>\n",
       "      <td>1.010576</td>\n",
       "      <td>...</td>\n",
       "      <td>1.003621</td>\n",
       "      <td>1.013898</td>\n",
       "      <td>1.001039</td>\n",
       "      <td>0.995142</td>\n",
       "      <td>0.997608</td>\n",
       "      <td>1.002075</td>\n",
       "      <td>1.004373</td>\n",
       "      <td>1.002478</td>\n",
       "      <td>0.985791</td>\n",
       "      <td>1.009757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.997406</td>\n",
       "      <td>0.999789</td>\n",
       "      <td>0.991742</td>\n",
       "      <td>1.000050</td>\n",
       "      <td>1.001336</td>\n",
       "      <td>1.017984</td>\n",
       "      <td>0.989225</td>\n",
       "      <td>1.018901</td>\n",
       "      <td>1.002266</td>\n",
       "      <td>1.018836</td>\n",
       "      <td>...</td>\n",
       "      <td>1.019881</td>\n",
       "      <td>1.012281</td>\n",
       "      <td>0.992768</td>\n",
       "      <td>0.989364</td>\n",
       "      <td>0.997222</td>\n",
       "      <td>0.998800</td>\n",
       "      <td>1.001705</td>\n",
       "      <td>1.011065</td>\n",
       "      <td>0.979763</td>\n",
       "      <td>1.012780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.994178</td>\n",
       "      <td>1.008209</td>\n",
       "      <td>0.983272</td>\n",
       "      <td>1.000693</td>\n",
       "      <td>1.002686</td>\n",
       "      <td>1.030410</td>\n",
       "      <td>0.982663</td>\n",
       "      <td>1.027607</td>\n",
       "      <td>1.016393</td>\n",
       "      <td>1.025816</td>\n",
       "      <td>...</td>\n",
       "      <td>1.015248</td>\n",
       "      <td>1.007897</td>\n",
       "      <td>0.994381</td>\n",
       "      <td>0.991371</td>\n",
       "      <td>1.003112</td>\n",
       "      <td>1.018613</td>\n",
       "      <td>1.005541</td>\n",
       "      <td>1.021846</td>\n",
       "      <td>0.974965</td>\n",
       "      <td>0.996843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.991934</td>\n",
       "      <td>1.002917</td>\n",
       "      <td>0.978445</td>\n",
       "      <td>1.011027</td>\n",
       "      <td>1.014841</td>\n",
       "      <td>1.037972</td>\n",
       "      <td>0.988072</td>\n",
       "      <td>1.027972</td>\n",
       "      <td>1.017219</td>\n",
       "      <td>1.033695</td>\n",
       "      <td>...</td>\n",
       "      <td>1.011123</td>\n",
       "      <td>1.010479</td>\n",
       "      <td>0.982082</td>\n",
       "      <td>0.995309</td>\n",
       "      <td>1.000259</td>\n",
       "      <td>1.022143</td>\n",
       "      <td>1.008225</td>\n",
       "      <td>1.037127</td>\n",
       "      <td>0.976471</td>\n",
       "      <td>1.003308</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>1256</th>\n",
       "      <td>2.799853</td>\n",
       "      <td>1.155203</td>\n",
       "      <td>1.582267</td>\n",
       "      <td>1.438178</td>\n",
       "      <td>1.663794</td>\n",
       "      <td>1.864090</td>\n",
       "      <td>0.989203</td>\n",
       "      <td>2.309199</td>\n",
       "      <td>1.758279</td>\n",
       "      <td>1.380144</td>\n",
       "      <td>...</td>\n",
       "      <td>1.033506</td>\n",
       "      <td>1.397959</td>\n",
       "      <td>1.484203</td>\n",
       "      <td>1.289073</td>\n",
       "      <td>1.209084</td>\n",
       "      <td>2.236906</td>\n",
       "      <td>1.712187</td>\n",
       "      <td>0.938530</td>\n",
       "      <td>3.136725</td>\n",
       "      <td>1.133671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>2.807322</td>\n",
       "      <td>1.136380</td>\n",
       "      <td>1.581446</td>\n",
       "      <td>1.441549</td>\n",
       "      <td>1.668603</td>\n",
       "      <td>1.895625</td>\n",
       "      <td>0.982100</td>\n",
       "      <td>2.323074</td>\n",
       "      <td>1.768848</td>\n",
       "      <td>1.363882</td>\n",
       "      <td>...</td>\n",
       "      <td>1.031787</td>\n",
       "      <td>1.403275</td>\n",
       "      <td>1.498072</td>\n",
       "      <td>1.282831</td>\n",
       "      <td>1.225646</td>\n",
       "      <td>2.249719</td>\n",
       "      <td>1.697552</td>\n",
       "      <td>0.944955</td>\n",
       "      <td>3.172528</td>\n",
       "      <td>1.129908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>2.797050</td>\n",
       "      <td>1.127733</td>\n",
       "      <td>1.577939</td>\n",
       "      <td>1.434864</td>\n",
       "      <td>1.653126</td>\n",
       "      <td>1.902844</td>\n",
       "      <td>0.966491</td>\n",
       "      <td>2.331193</td>\n",
       "      <td>1.774042</td>\n",
       "      <td>1.350759</td>\n",
       "      <td>...</td>\n",
       "      <td>1.034894</td>\n",
       "      <td>1.402665</td>\n",
       "      <td>1.497935</td>\n",
       "      <td>1.291318</td>\n",
       "      <td>1.229622</td>\n",
       "      <td>2.277231</td>\n",
       "      <td>1.687290</td>\n",
       "      <td>0.943466</td>\n",
       "      <td>3.172180</td>\n",
       "      <td>1.125812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1259</th>\n",
       "      <td>2.816394</td>\n",
       "      <td>1.125740</td>\n",
       "      <td>1.606337</td>\n",
       "      <td>1.438265</td>\n",
       "      <td>1.639680</td>\n",
       "      <td>1.919629</td>\n",
       "      <td>0.971494</td>\n",
       "      <td>2.329547</td>\n",
       "      <td>1.766575</td>\n",
       "      <td>1.346183</td>\n",
       "      <td>...</td>\n",
       "      <td>1.019001</td>\n",
       "      <td>1.421231</td>\n",
       "      <td>1.516934</td>\n",
       "      <td>1.293393</td>\n",
       "      <td>1.220330</td>\n",
       "      <td>2.273738</td>\n",
       "      <td>1.691800</td>\n",
       "      <td>0.935665</td>\n",
       "      <td>3.206237</td>\n",
       "      <td>1.131800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1260</th>\n",
       "      <td>2.809383</td>\n",
       "      <td>1.137491</td>\n",
       "      <td>1.610451</td>\n",
       "      <td>1.419262</td>\n",
       "      <td>1.643937</td>\n",
       "      <td>1.906232</td>\n",
       "      <td>0.969172</td>\n",
       "      <td>2.327700</td>\n",
       "      <td>1.779538</td>\n",
       "      <td>1.361136</td>\n",
       "      <td>...</td>\n",
       "      <td>1.020770</td>\n",
       "      <td>1.418046</td>\n",
       "      <td>1.530387</td>\n",
       "      <td>1.273339</td>\n",
       "      <td>1.217048</td>\n",
       "      <td>2.268291</td>\n",
       "      <td>1.717929</td>\n",
       "      <td>0.938830</td>\n",
       "      <td>3.213253</td>\n",
       "      <td>1.121323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1261 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     0.998368  1.002007  0.993729  0.995364  1.004523  1.010519  0.994388   \n",
       "2     0.997406  0.999789  0.991742  1.000050  1.001336  1.017984  0.989225   \n",
       "3     0.994178  1.008209  0.983272  1.000693  1.002686  1.030410  0.982663   \n",
       "4     0.991934  1.002917  0.978445  1.011027  1.014841  1.037972  0.988072   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "1256  2.799853  1.155203  1.582267  1.438178  1.663794  1.864090  0.989203   \n",
       "1257  2.807322  1.136380  1.581446  1.441549  1.668603  1.895625  0.982100   \n",
       "1258  2.797050  1.127733  1.577939  1.434864  1.653126  1.902844  0.966491   \n",
       "1259  2.816394  1.125740  1.606337  1.438265  1.639680  1.919629  0.971494   \n",
       "1260  2.809383  1.137491  1.610451  1.419262  1.643937  1.906232  0.969172   \n",
       "\n",
       "           7         8         9    ...       490       491       492  \\\n",
       "0     1.000000  1.000000  1.000000  ...  1.000000  1.000000  1.000000   \n",
       "1     0.996950  0.993060  1.010576  ...  1.003621  1.013898  1.001039   \n",
       "2     1.018901  1.002266  1.018836  ...  1.019881  1.012281  0.992768   \n",
       "3     1.027607  1.016393  1.025816  ...  1.015248  1.007897  0.994381   \n",
       "4     1.027972  1.017219  1.033695  ...  1.011123  1.010479  0.982082   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "1256  2.309199  1.758279  1.380144  ...  1.033506  1.397959  1.484203   \n",
       "1257  2.323074  1.768848  1.363882  ...  1.031787  1.403275  1.498072   \n",
       "1258  2.331193  1.774042  1.350759  ...  1.034894  1.402665  1.497935   \n",
       "1259  2.329547  1.766575  1.346183  ...  1.019001  1.421231  1.516934   \n",
       "1260  2.327700  1.779538  1.361136  ...  1.020770  1.418046  1.530387   \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     0.995142  0.997608  1.002075  1.004373  1.002478  0.985791  1.009757  \n",
       "2     0.989364  0.997222  0.998800  1.001705  1.011065  0.979763  1.012780  \n",
       "3     0.991371  1.003112  1.018613  1.005541  1.021846  0.974965  0.996843  \n",
       "4     0.995309  1.000259  1.022143  1.008225  1.037127  0.976471  1.003308  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "1256  1.289073  1.209084  2.236906  1.712187  0.938530  3.136725  1.133671  \n",
       "1257  1.282831  1.225646  2.249719  1.697552  0.944955  3.172528  1.129908  \n",
       "1258  1.291318  1.229622  2.277231  1.687290  0.943466  3.172180  1.125812  \n",
       "1259  1.293393  1.220330  2.273738  1.691800  0.935665  3.206237  1.131800  \n",
       "1260  1.273339  1.217048  2.268291  1.717929  0.938830  3.213253  1.121323  \n",
       "\n",
       "[1261 rows x 500 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running a Monte Carlo simulation to forecast 5 years cumulative returns\n",
    "MC_5_years.calc_cumulative_return()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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_5_years.plot_simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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_5_years.plot_distribution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count           500.000000\n",
      "mean              1.460092\n",
      "std               0.393829\n",
      "min               0.709041\n",
      "25%               1.171839\n",
      "50%               1.400334\n",
      "75%               1.691345\n",
      "max               3.213253\n",
      "95% CI Lower      0.870733\n",
      "95% CI Upper      2.396486\n",
      "Name: 1260, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Fetch summary statistics from the Monte Carlo simulation results\n",
    "five_yr_summary_stat = MC_5_years.summarize_cumulative_return()\n",
    "\n",
    "# Print summary statistics\n",
    "print(five_yr_summary_stat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a 95% chance that an initial investment of $60000 in the portfolio over the next 5 years will end within in the range of $52243.97 and $143789.19\n"
     ]
    }
   ],
   "source": [
    "# Set initial investment\n",
    "initial_investment = 20000 * 3\n",
    "\n",
    "# Use the lower and upper `95%` confidence intervals to calculate the range of the possible outcomes of our $60,000\n",
    "ci_lower_five = round(five_yr_summary_stat[8]*initial_investment,2)\n",
    "ci_upper_five = round(five_yr_summary_stat[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 5 years will end within in the range of\"\n",
    "      f\" ${ci_lower_five} and ${ci_upper_five}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ten Years Retirement Option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuring a Monte Carlo simulation to forecast 10 years cumulative returns\n",
    "MC_10_years = MCSimulation(\n",
    "    portfolio_data = df_stock_data,\n",
    "    weights = [.40,.60],\n",
    "    num_simulation = 500,\n",
    "    num_trading_days = 252*10\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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": {
      "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>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>1.003065</td>\n",
       "      <td>0.996940</td>\n",
       "      <td>1.001354</td>\n",
       "      <td>0.997394</td>\n",
       "      <td>0.987884</td>\n",
       "      <td>0.996397</td>\n",
       "      <td>0.997710</td>\n",
       "      <td>1.001582</td>\n",
       "      <td>1.001989</td>\n",
       "      <td>1.005211</td>\n",
       "      <td>...</td>\n",
       "      <td>1.001257</td>\n",
       "      <td>0.990317</td>\n",
       "      <td>1.010417</td>\n",
       "      <td>0.989494</td>\n",
       "      <td>1.008455</td>\n",
       "      <td>0.995973</td>\n",
       "      <td>0.996852</td>\n",
       "      <td>0.998924</td>\n",
       "      <td>0.999510</td>\n",
       "      <td>0.999990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.009045</td>\n",
       "      <td>1.007959</td>\n",
       "      <td>0.999737</td>\n",
       "      <td>1.001555</td>\n",
       "      <td>0.991534</td>\n",
       "      <td>0.989058</td>\n",
       "      <td>0.999022</td>\n",
       "      <td>0.995803</td>\n",
       "      <td>1.008111</td>\n",
       "      <td>1.007750</td>\n",
       "      <td>...</td>\n",
       "      <td>1.001634</td>\n",
       "      <td>0.990413</td>\n",
       "      <td>1.008317</td>\n",
       "      <td>0.987873</td>\n",
       "      <td>1.013024</td>\n",
       "      <td>1.003407</td>\n",
       "      <td>1.001763</td>\n",
       "      <td>0.982183</td>\n",
       "      <td>0.993109</td>\n",
       "      <td>0.999547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.012752</td>\n",
       "      <td>1.004980</td>\n",
       "      <td>0.995700</td>\n",
       "      <td>1.013486</td>\n",
       "      <td>0.989712</td>\n",
       "      <td>0.983142</td>\n",
       "      <td>1.002487</td>\n",
       "      <td>0.996914</td>\n",
       "      <td>1.014351</td>\n",
       "      <td>1.003389</td>\n",
       "      <td>...</td>\n",
       "      <td>1.008325</td>\n",
       "      <td>1.003756</td>\n",
       "      <td>1.012761</td>\n",
       "      <td>0.986772</td>\n",
       "      <td>1.010861</td>\n",
       "      <td>0.992675</td>\n",
       "      <td>1.011614</td>\n",
       "      <td>0.988248</td>\n",
       "      <td>0.988888</td>\n",
       "      <td>1.001014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.013569</td>\n",
       "      <td>1.001128</td>\n",
       "      <td>0.984432</td>\n",
       "      <td>1.007431</td>\n",
       "      <td>0.997329</td>\n",
       "      <td>0.987615</td>\n",
       "      <td>0.999072</td>\n",
       "      <td>0.992877</td>\n",
       "      <td>1.011703</td>\n",
       "      <td>1.003910</td>\n",
       "      <td>...</td>\n",
       "      <td>1.011873</td>\n",
       "      <td>1.003601</td>\n",
       "      <td>1.016574</td>\n",
       "      <td>1.000369</td>\n",
       "      <td>1.016581</td>\n",
       "      <td>0.997110</td>\n",
       "      <td>1.006914</td>\n",
       "      <td>0.990494</td>\n",
       "      <td>0.988582</td>\n",
       "      <td>1.007211</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>2516</th>\n",
       "      <td>1.502875</td>\n",
       "      <td>2.003244</td>\n",
       "      <td>2.648390</td>\n",
       "      <td>3.092538</td>\n",
       "      <td>1.245619</td>\n",
       "      <td>0.666652</td>\n",
       "      <td>2.476699</td>\n",
       "      <td>2.039689</td>\n",
       "      <td>4.658528</td>\n",
       "      <td>4.065484</td>\n",
       "      <td>...</td>\n",
       "      <td>2.206350</td>\n",
       "      <td>2.592431</td>\n",
       "      <td>3.197105</td>\n",
       "      <td>1.123081</td>\n",
       "      <td>1.616897</td>\n",
       "      <td>4.153201</td>\n",
       "      <td>1.886018</td>\n",
       "      <td>3.045438</td>\n",
       "      <td>2.411329</td>\n",
       "      <td>1.283839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2517</th>\n",
       "      <td>1.521666</td>\n",
       "      <td>2.015255</td>\n",
       "      <td>2.659112</td>\n",
       "      <td>3.083126</td>\n",
       "      <td>1.258732</td>\n",
       "      <td>0.668694</td>\n",
       "      <td>2.493943</td>\n",
       "      <td>2.033090</td>\n",
       "      <td>4.664545</td>\n",
       "      <td>4.107058</td>\n",
       "      <td>...</td>\n",
       "      <td>2.185254</td>\n",
       "      <td>2.576659</td>\n",
       "      <td>3.199526</td>\n",
       "      <td>1.128426</td>\n",
       "      <td>1.612870</td>\n",
       "      <td>4.153966</td>\n",
       "      <td>1.892407</td>\n",
       "      <td>3.059168</td>\n",
       "      <td>2.415141</td>\n",
       "      <td>1.285782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2518</th>\n",
       "      <td>1.530069</td>\n",
       "      <td>1.994299</td>\n",
       "      <td>2.658591</td>\n",
       "      <td>3.104513</td>\n",
       "      <td>1.265855</td>\n",
       "      <td>0.664881</td>\n",
       "      <td>2.479872</td>\n",
       "      <td>2.034851</td>\n",
       "      <td>4.651744</td>\n",
       "      <td>4.091238</td>\n",
       "      <td>...</td>\n",
       "      <td>2.153325</td>\n",
       "      <td>2.623909</td>\n",
       "      <td>3.220736</td>\n",
       "      <td>1.124889</td>\n",
       "      <td>1.599044</td>\n",
       "      <td>4.157508</td>\n",
       "      <td>1.900080</td>\n",
       "      <td>3.048080</td>\n",
       "      <td>2.399975</td>\n",
       "      <td>1.296079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2519</th>\n",
       "      <td>1.523714</td>\n",
       "      <td>2.004086</td>\n",
       "      <td>2.653624</td>\n",
       "      <td>3.107414</td>\n",
       "      <td>1.259325</td>\n",
       "      <td>0.664964</td>\n",
       "      <td>2.510420</td>\n",
       "      <td>2.007740</td>\n",
       "      <td>4.681517</td>\n",
       "      <td>4.084164</td>\n",
       "      <td>...</td>\n",
       "      <td>2.143750</td>\n",
       "      <td>2.626327</td>\n",
       "      <td>3.205427</td>\n",
       "      <td>1.126281</td>\n",
       "      <td>1.585980</td>\n",
       "      <td>4.106729</td>\n",
       "      <td>1.881412</td>\n",
       "      <td>3.049434</td>\n",
       "      <td>2.394858</td>\n",
       "      <td>1.289452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2520</th>\n",
       "      <td>1.521771</td>\n",
       "      <td>2.004154</td>\n",
       "      <td>2.683846</td>\n",
       "      <td>3.107735</td>\n",
       "      <td>1.254104</td>\n",
       "      <td>0.668051</td>\n",
       "      <td>2.507758</td>\n",
       "      <td>2.010449</td>\n",
       "      <td>4.758622</td>\n",
       "      <td>4.061852</td>\n",
       "      <td>...</td>\n",
       "      <td>2.143444</td>\n",
       "      <td>2.591759</td>\n",
       "      <td>3.245429</td>\n",
       "      <td>1.122172</td>\n",
       "      <td>1.576446</td>\n",
       "      <td>4.176110</td>\n",
       "      <td>1.871106</td>\n",
       "      <td>3.048520</td>\n",
       "      <td>2.398632</td>\n",
       "      <td>1.295757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2521 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     1.003065  0.996940  1.001354  0.997394  0.987884  0.996397  0.997710   \n",
       "2     1.009045  1.007959  0.999737  1.001555  0.991534  0.989058  0.999022   \n",
       "3     1.012752  1.004980  0.995700  1.013486  0.989712  0.983142  1.002487   \n",
       "4     1.013569  1.001128  0.984432  1.007431  0.997329  0.987615  0.999072   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "2516  1.502875  2.003244  2.648390  3.092538  1.245619  0.666652  2.476699   \n",
       "2517  1.521666  2.015255  2.659112  3.083126  1.258732  0.668694  2.493943   \n",
       "2518  1.530069  1.994299  2.658591  3.104513  1.265855  0.664881  2.479872   \n",
       "2519  1.523714  2.004086  2.653624  3.107414  1.259325  0.664964  2.510420   \n",
       "2520  1.521771  2.004154  2.683846  3.107735  1.254104  0.668051  2.507758   \n",
       "\n",
       "           7         8         9    ...       490       491       492  \\\n",
       "0     1.000000  1.000000  1.000000  ...  1.000000  1.000000  1.000000   \n",
       "1     1.001582  1.001989  1.005211  ...  1.001257  0.990317  1.010417   \n",
       "2     0.995803  1.008111  1.007750  ...  1.001634  0.990413  1.008317   \n",
       "3     0.996914  1.014351  1.003389  ...  1.008325  1.003756  1.012761   \n",
       "4     0.992877  1.011703  1.003910  ...  1.011873  1.003601  1.016574   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "2516  2.039689  4.658528  4.065484  ...  2.206350  2.592431  3.197105   \n",
       "2517  2.033090  4.664545  4.107058  ...  2.185254  2.576659  3.199526   \n",
       "2518  2.034851  4.651744  4.091238  ...  2.153325  2.623909  3.220736   \n",
       "2519  2.007740  4.681517  4.084164  ...  2.143750  2.626327  3.205427   \n",
       "2520  2.010449  4.758622  4.061852  ...  2.143444  2.591759  3.245429   \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     0.989494  1.008455  0.995973  0.996852  0.998924  0.999510  0.999990  \n",
       "2     0.987873  1.013024  1.003407  1.001763  0.982183  0.993109  0.999547  \n",
       "3     0.986772  1.010861  0.992675  1.011614  0.988248  0.988888  1.001014  \n",
       "4     1.000369  1.016581  0.997110  1.006914  0.990494  0.988582  1.007211  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "2516  1.123081  1.616897  4.153201  1.886018  3.045438  2.411329  1.283839  \n",
       "2517  1.128426  1.612870  4.153966  1.892407  3.059168  2.415141  1.285782  \n",
       "2518  1.124889  1.599044  4.157508  1.900080  3.048080  2.399975  1.296079  \n",
       "2519  1.126281  1.585980  4.106729  1.881412  3.049434  2.394858  1.289452  \n",
       "2520  1.122172  1.576446  4.176110  1.871106  3.048520  2.398632  1.295757  \n",
       "\n",
       "[2521 rows x 500 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running a Monte Carlo simulation to forecast 10 years cumulative returns\n",
    "MC_10_years.calc_cumulative_return()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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": {
      "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_10_years.plot_simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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_10_years.plot_distribution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count           500.000000\n",
      "mean              2.121531\n",
      "std               0.809394\n",
      "min               0.730939\n",
      "25%               1.526268\n",
      "50%               1.988032\n",
      "75%               2.591329\n",
      "max               6.009586\n",
      "95% CI Lower      0.884194\n",
      "95% CI Upper      3.940896\n",
      "Name: 2520, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Fetch summary statistics from the Monte Carlo simulation results\n",
    "ten_yr_summary_stat = MC_10_years.summarize_cumulative_return()\n",
    "\n",
    "# Print summary statistics\n",
    "print(ten_yr_summary_stat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a 95% chance that an initial investment of $60000 in the portfolio over the next 10 years will end within in the range of $53051.65 and $236453.74\n"
     ]
    }
   ],
   "source": [
    "# Set initial investment\n",
    "initial_investment = 20000 * 3\n",
    "\n",
    "# Use the lower and upper `95%` confidence intervals to calculate the range of the possible outcomes of our $60,000\n",
    "ci_lower_ten = round(ten_yr_summary_stat[8]*initial_investment,2)\n",
    "ci_upper_ten = round(ten_yr_summary_stat[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 10 years will end within in the range of\"\n",
    "      f\" ${ci_lower_ten} and ${ci_upper_ten}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:dev]",
   "language": "python",
   "name": "conda-env-dev-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}