financial-planner.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unit 5 - Financial Planning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set current amount of crypto assets\n",
    "my_btc = 1.2\n",
    "my_eth = 5.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fetch current BTC price\n",
    "# Execute a get request\n",
    "btc_info = requests.get(btc_url)\n",
    "\n",
    "# Print `response_btc variable`\n",
    "btc_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'{\\n\\t\"data\": {\\n\\t\\t\"1\": {\\n\\t\\t\\t\"id\": 1,\\n\\t\\t\\t\"name\": \"Bitcoin\",\\n\\t\\t\\t\"symbol\": \"BTC\",\\n\\t\\t\\t\"website_slug\": \"bitcoin\",\\n\\t\\t\\t\"rank\": 1,\\n\\t\\t\\t\"circulating_supply\": 18979325,\\n\\t\\t\\t\"total_supply\": 18979325,\\n\\t\\t\\t\"max_supply\": 21000000,\\n\\t\\t\\t\"quotes\": {\\n\\t\\t\\t\\t\"USD\": {\\n\\t\\t\\t\\t\\t\"price\": 39147.0000000000000000,\\n\\t\\t\\t\\t\\t\"volume_24h\": 26866747626,\\n\\t\\t\\t\\t\\t\"market_cap\": 742988405806,\\n\\t\\t\\t\\t\\t\"percentage_change_1h\": -0.1843840668080190,\\n\\t\\t\\t\\t\\t\"percentage_change_24h\": -7.1193998973012600,\\n\\t\\t\\t\\t\\t\"percentage_change_7d\": -10.9899461423643000,\\n\\t\\t\\t\\t\\t\"percent_change_1h\": -0.1843840668080190,\\n\\t\\t\\t\\t\\t\"percent_change_24h\": -7.1193998973012600,\\n\\t\\t\\t\\t\\t\"percent_change_7d\": -10.9899461423643000\\n\\t\\t\\t\\t},\\n\\t\\t\\t\\t\"CAD\": {\\n\\t\\t\\t\\t\\t\"price\": 50202.1128,\\n\\t\\t\\t\\t\\t\"volume_24h\": 34453917155.5824,\\n\\t\\t\\t\\t\\t\"market_cap\": 952808331605.614,\\n\\t\\t\\t\\t\\t\"percent_change_1h\": -0.1843840668080190,\\n\\t\\t\\t\\t\\t\"percent_change_24h\": -7.1193998973012600,\\n\\t\\t\\t\\t\\t\"percent_change_7d\": -10.9899461423643000\\n\\t\\t\\t\\t}\\n\\t\\t\\t},\\n\\t\\t\\t\"last_updated\": 1646909388\\n\\t\\t}\\n\\t},\\n\\t\"metadata\": {\\n\\t\\t\"timestamp\": 1646909388,\\n\\t\\t\"num_cryptocurrencies\": 3105,\\n\\t\\t\"error\": null\\n\\t}\\n}'\n"
     ]
    }
   ],
   "source": [
    "# Store response using `content` attribute\n",
    "btc_content = btc_info.content\n",
    "print(btc_content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data': {'1': {'id': 1, 'name': 'Bitcoin', 'symbol': 'BTC', 'website_slug': 'bitcoin', 'rank': 1, 'circulating_supply': 18979325, 'total_supply': 18979325, 'max_supply': 21000000, 'quotes': {'USD': {'price': 39147.0, 'volume_24h': 26866747626, 'market_cap': 742988405806, 'percentage_change_1h': -0.184384066808019, 'percentage_change_24h': -7.11939989730126, 'percentage_change_7d': -10.9899461423643, 'percent_change_1h': -0.184384066808019, 'percent_change_24h': -7.11939989730126, 'percent_change_7d': -10.9899461423643}, 'CAD': {'price': 50202.1128, 'volume_24h': 34453917155.5824, 'market_cap': 952808331605.614, 'percent_change_1h': -0.184384066808019, 'percent_change_24h': -7.11939989730126, 'percent_change_7d': -10.9899461423643}}, 'last_updated': 1646909388}}, 'metadata': {'timestamp': 1646909388, 'num_cryptocurrencies': 3105, 'error': None}}\n"
     ]
    }
   ],
   "source": [
    "# Format data as JSON\n",
    "import json\n",
    "btc_data = btc_info.json()\n",
    "print(btc_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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\": 18979325,\n",
      "            \"total_supply\": 18979325,\n",
      "            \"max_supply\": 21000000,\n",
      "            \"quotes\": {\n",
      "                \"USD\": {\n",
      "                    \"price\": 39147.0,\n",
      "                    \"volume_24h\": 26866747626,\n",
      "                    \"market_cap\": 742988405806,\n",
      "                    \"percentage_change_1h\": -0.184384066808019,\n",
      "                    \"percentage_change_24h\": -7.11939989730126,\n",
      "                    \"percentage_change_7d\": -10.9899461423643,\n",
      "                    \"percent_change_1h\": -0.184384066808019,\n",
      "                    \"percent_change_24h\": -7.11939989730126,\n",
      "                    \"percent_change_7d\": -10.9899461423643\n",
      "                },\n",
      "                \"CAD\": {\n",
      "                    \"price\": 50202.1128,\n",
      "                    \"volume_24h\": 34453917155.5824,\n",
      "                    \"market_cap\": 952808331605.614,\n",
      "                    \"percent_change_1h\": -0.184384066808019,\n",
      "                    \"percent_change_24h\": -7.11939989730126,\n",
      "                    \"percent_change_7d\": -10.9899461423643\n",
      "                }\n",
      "            },\n",
      "            \"last_updated\": 1646909388\n",
      "        }\n",
      "    },\n",
      "    \"metadata\": {\n",
      "        \"timestamp\": 1646909388,\n",
      "        \"num_cryptocurrencies\": 3105,\n",
      "        \"error\": null\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "# Use json.dumps to format data\n",
    "print(json.dumps(btc_data, indent = 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fetch current BTC price in CAD\n",
    "# Check data type of btc_data\n",
    "type(btc_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50202.1128"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find element in dictionary for BTC price in CAD\n",
    "btc_data[\"data\"][\"1\"][\"quotes\"][\"CAD\"][\"price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define element as variable\n",
    "current_btc_price = btc_data[\"data\"][\"1\"][\"quotes\"][\"CAD\"][\"price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fetch current ETH price\n",
    "# Execute a get request\n",
    "eth_info = requests.get(eth_url)\n",
    "\n",
    "# Print `response_btc variable`\n",
    "eth_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'{\\n\\t\"data\": {\\n\\t\\t\"1027\": {\\n\\t\\t\\t\"id\": 1027,\\n\\t\\t\\t\"name\": \"Ethereum\",\\n\\t\\t\\t\"symbol\": \"ETH\",\\n\\t\\t\\t\"website_slug\": \"ethereum\",\\n\\t\\t\\t\"rank\": 2,\\n\\t\\t\\t\"circulating_supply\": 119901195,\\n\\t\\t\\t\"total_supply\": 119901195,\\n\\t\\t\\t\"max_supply\": 0,\\n\\t\\t\\t\"quotes\": {\\n\\t\\t\\t\\t\"USD\": {\\n\\t\\t\\t\\t\\t\"price\": 2595.4200000000000000,\\n\\t\\t\\t\\t\\t\"volume_24h\": 13702383972,\\n\\t\\t\\t\\t\\t\"market_cap\": 311193529447,\\n\\t\\t\\t\\t\\t\"percentage_change_1h\": -0.4783023160059260,\\n\\t\\t\\t\\t\\t\"percentage_change_24h\": -5.7746279669872800,\\n\\t\\t\\t\\t\\t\"percentage_change_7d\": -12.1185605934841000,\\n\\t\\t\\t\\t\\t\"percent_change_1h\": -0.4783023160059260,\\n\\t\\t\\t\\t\\t\"percent_change_24h\": -5.7746279669872800,\\n\\t\\t\\t\\t\\t\"percent_change_7d\": -12.1185605934841000\\n\\t\\t\\t\\t},\\n\\t\\t\\t\\t\"CAD\": {\\n\\t\\t\\t\\t\\t\"price\": 3328.366608,\\n\\t\\t\\t\\t\\t\"volume_24h\": 17571937205.6928,\\n\\t\\t\\t\\t\\t\"market_cap\": 399074582162.833,\\n\\t\\t\\t\\t\\t\"percent_change_1h\": -0.4783023160059260,\\n\\t\\t\\t\\t\\t\"percent_change_24h\": -5.7746279669872800,\\n\\t\\t\\t\\t\\t\"percent_change_7d\": -12.1185605934841000\\n\\t\\t\\t\\t}\\n\\t\\t\\t},\\n\\t\\t\\t\"last_updated\": 1646909366\\n\\t\\t}\\n\\t},\\n\\t\"metadata\": {\\n\\t\\t\"timestamp\": 1646909366,\\n\\t\\t\"num_cryptocurrencies\": 3105,\\n\\t\\t\"error\": null\\n\\t}\\n}'\n"
     ]
    }
   ],
   "source": [
    "# Store response using `content` attribute\n",
    "eth_content = eth_info.content\n",
    "print(eth_content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data': {'1027': {'id': 1027, 'name': 'Ethereum', 'symbol': 'ETH', 'website_slug': 'ethereum', 'rank': 2, 'circulating_supply': 119901195, 'total_supply': 119901195, 'max_supply': 0, 'quotes': {'USD': {'price': 2595.42, 'volume_24h': 13702383972, 'market_cap': 311193529447, 'percentage_change_1h': -0.478302316005926, 'percentage_change_24h': -5.77462796698728, 'percentage_change_7d': -12.1185605934841, 'percent_change_1h': -0.478302316005926, 'percent_change_24h': -5.77462796698728, 'percent_change_7d': -12.1185605934841}, 'CAD': {'price': 3328.366608, 'volume_24h': 17571937205.6928, 'market_cap': 399074582162.833, 'percent_change_1h': -0.478302316005926, 'percent_change_24h': -5.77462796698728, 'percent_change_7d': -12.1185605934841}}, 'last_updated': 1646909366}}, 'metadata': {'timestamp': 1646909366, 'num_cryptocurrencies': 3105, 'error': None}}\n"
     ]
    }
   ],
   "source": [
    "# Format data as JSON\n",
    "import json\n",
    "eth_data = eth_info.json()\n",
    "print(eth_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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\": 119901195,\n",
      "            \"total_supply\": 119901195,\n",
      "            \"max_supply\": 0,\n",
      "            \"quotes\": {\n",
      "                \"USD\": {\n",
      "                    \"price\": 2595.42,\n",
      "                    \"volume_24h\": 13702383972,\n",
      "                    \"market_cap\": 311193529447,\n",
      "                    \"percentage_change_1h\": -0.478302316005926,\n",
      "                    \"percentage_change_24h\": -5.77462796698728,\n",
      "                    \"percentage_change_7d\": -12.1185605934841,\n",
      "                    \"percent_change_1h\": -0.478302316005926,\n",
      "                    \"percent_change_24h\": -5.77462796698728,\n",
      "                    \"percent_change_7d\": -12.1185605934841\n",
      "                },\n",
      "                \"CAD\": {\n",
      "                    \"price\": 3328.366608,\n",
      "                    \"volume_24h\": 17571937205.6928,\n",
      "                    \"market_cap\": 399074582162.833,\n",
      "                    \"percent_change_1h\": -0.478302316005926,\n",
      "                    \"percent_change_24h\": -5.77462796698728,\n",
      "                    \"percent_change_7d\": -12.1185605934841\n",
      "                }\n",
      "            },\n",
      "            \"last_updated\": 1646909366\n",
      "        }\n",
      "    },\n",
      "    \"metadata\": {\n",
      "        \"timestamp\": 1646909366,\n",
      "        \"num_cryptocurrencies\": 3105,\n",
      "        \"error\": null\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "# Use json.dumps to format data\n",
    "print(json.dumps(eth_data, indent = 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fetch current ETH price in CAD\n",
    "# Check data type of btc_data\n",
    "type(eth_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3328.366608"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find element in dictionary for ETH price in CAD\n",
    "eth_data[\"data\"][\"1027\"][\"quotes\"][\"CAD\"][\"price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define element as variable\n",
    "current_eth_price = eth_data[\"data\"][\"1027\"][\"quotes\"][\"CAD\"][\"price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "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 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current value of your 1.2 BTC is $60242.54\n",
      "The current value of your 5.3 ETH is $17640.34\n"
     ]
    }
   ],
   "source": [
    "# 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": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set current amount of shares\n",
    "my_agg = 200\n",
    "my_spy = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set Alpaca API key and secret\n",
    "alpaca_api_key = os.getenv(\"ALPACA_API_KEY_ID\")\n",
    "alpaca_secret_key = os.getenv(\"ALPACA_SECRET_KEY\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verify that Alpaca key and secret were correctly loaded\n",
    "type(alpaca_api_key)\n",
    "type(alpaca_secret_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the Alpaca API object\n",
    "alpaca = tradeapi.REST(\n",
    "    alpaca_api_key,\n",
    "    alpaca_secret_key,\n",
    "    api_version=\"v2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/alpacaenv/lib/python3.7/site-packages/ipykernel_launcher.py:16: DeprecatedWarning: get_barset is deprecated as of v1.0.0. Use get_bars instead\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">AGG</th>\n",
       "      <th colspan=\"5\" halign=\"left\">SPY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-03-07 00:00:00-05:00</th>\n",
       "      <td>109.85</td>\n",
       "      <td>110.14</td>\n",
       "      <td>109.64</td>\n",
       "      <td>109.68</td>\n",
       "      <td>11739486</td>\n",
       "      <td>431.55</td>\n",
       "      <td>432.3018</td>\n",
       "      <td>419.36</td>\n",
       "      <td>419.45</td>\n",
       "      <td>113647664</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              AGG                                       SPY  \\\n",
       "                             open    high     low   close    volume    open   \n",
       "time                                                                          \n",
       "2022-03-07 00:00:00-05:00  109.85  110.14  109.64  109.68  11739486  431.55   \n",
       "\n",
       "                                                                \n",
       "                               high     low   close     volume  \n",
       "time                                                            \n",
       "2022-03-07 00:00:00-05:00  432.3018  419.36  419.45  113647664  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format current date as ISO format\n",
    "today = pd.Timestamp(\"2022-03-08\", tz=\"Australia/Melbourne\").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",
    "# (use a limit=1000 parameter to call the most recent 1000 days of data)\n",
    "df_portfolio = alpaca.get_barset(\n",
    "    tickers,\n",
    "    timeframe,\n",
    "    start = today,\n",
    "    end = today\n",
    ").df\n",
    "\n",
    "# Preview DataFrame\n",
    "df_portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pick AGG and SPY close prices\n",
    "agg_close_price = float(df_portfolio[\"AGG\"][\"close\"])\n",
    "spy_close_price = float(df_portfolio[\"SPY\"][\"close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current AGG closing price: $109.68\n",
      "Current SPY closing price: $419.45\n"
     ]
    }
   ],
   "source": [
    "# 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": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current value of your 50 SPY shares is $20972.50\n",
      "The current value of your 200 AGG shares is $21936.00\n"
     ]
    }
   ],
   "source": [
    "# Compute the current value of shares\n",
    "my_spy_value = spy_close_price * my_spy\n",
    "my_agg_value = agg_close_price * my_agg\n",
    "\n",
    "# Print current value of shares\n",
    "print(f\"The current value of your {my_spy} SPY shares is ${my_spy_value:0.2f}\")\n",
    "print(f\"The current value of your {my_agg} AGG shares is ${my_agg_value:0.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Savings Health Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>77882.878382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shares</th>\n",
       "      <td>42908.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              amount\n",
       "crypto  77882.878382\n",
       "shares  42908.500000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set monthly household income\n",
    "monthly_income = 12000\n",
    "\n",
    "# Consolidate financial assets data\n",
    "assets = ['crypto', 'shares']\n",
    "total_crypto = my_btc_value + my_eth_value\n",
    "total_shares = my_spy_value + my_agg_value\n",
    "\n",
    "# Create savings DataFrame\n",
    "df_savings = pd.DataFrame(data = [total_crypto, total_shares], columns = ['amount'])\n",
    "\n",
    "# Create the shares DataFrame with `tickers` as index\n",
    "df_savings.index = assets\n",
    "\n",
    "# Display savings DataFrame\n",
    "display(df_savings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Savings Composition'}, ylabel='amount'>"
      ]
     },
     "execution_count": 31,
     "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(title=\"Savings Composition\", y= 'amount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Congratulations! You have enough money in this fund. You have $84791.38 above your goal.\n"
     ]
    }
   ],
   "source": [
    "# Set ideal emergency fund\n",
    "emergency_fund = monthly_income * 3\n",
    "\n",
    "# Calculate total amount of savings\n",
    "total_savings = total_crypto + total_shares\n",
    "\n",
    "# Validate saving health\n",
    "if total_savings > emergency_fund:\n",
    "    print(f\"Congratulations! You have enough money in this fund. You have ${total_savings - emergency_fund:0.2f} above your goal.\")\n",
    "elif total_savings == emergency_fund:\n",
    "    print(f\"Congratulations! You have met your financial goal of ${emergency_fund}.\")\n",
    "else:\n",
    "    print(f\"You are ${emergency_fund - total_savings} from reaching your goal.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2 - Retirement Planning\n",
    "\n",
    "### Monte Carlo Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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('2017-03-08', tz='Australia/Melbourne').isoformat()\n",
    "end_date = pd.Timestamp('2022-03-08', tz='Australia/Melbourne').isoformat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/alpacaenv/lib/python3.7/site-packages/ipykernel_launcher.py:8: DeprecatedWarning: get_barset is deprecated as of v1.0.0. Use get_bars instead\n",
      "  \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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\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",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-19 00:00:00-04:00</th>\n",
       "      <td>106.56</td>\n",
       "      <td>106.74</td>\n",
       "      <td>106.53</td>\n",
       "      <td>106.58</td>\n",
       "      <td>2068013</td>\n",
       "      <td>273.33</td>\n",
       "      <td>273.38</td>\n",
       "      <td>268.62</td>\n",
       "      <td>270.49</td>\n",
       "      <td>77851316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-20 00:00:00-04:00</th>\n",
       "      <td>106.44</td>\n",
       "      <td>106.51</td>\n",
       "      <td>106.40</td>\n",
       "      <td>106.41</td>\n",
       "      <td>2769390</td>\n",
       "      <td>270.93</td>\n",
       "      <td>271.67</td>\n",
       "      <td>270.18</td>\n",
       "      <td>270.94</td>\n",
       "      <td>38399686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-21 00:00:00-04:00</th>\n",
       "      <td>106.32</td>\n",
       "      <td>106.48</td>\n",
       "      <td>106.08</td>\n",
       "      <td>106.41</td>\n",
       "      <td>2841377</td>\n",
       "      <td>270.90</td>\n",
       "      <td>273.27</td>\n",
       "      <td>270.19</td>\n",
       "      <td>270.43</td>\n",
       "      <td>59831437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-22 00:00:00-04:00</th>\n",
       "      <td>106.67</td>\n",
       "      <td>106.80</td>\n",
       "      <td>106.49</td>\n",
       "      <td>106.61</td>\n",
       "      <td>2789748</td>\n",
       "      <td>267.91</td>\n",
       "      <td>268.87</td>\n",
       "      <td>263.36</td>\n",
       "      <td>263.67</td>\n",
       "      <td>105012105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-23 00:00:00-04:00</th>\n",
       "      <td>106.46</td>\n",
       "      <td>106.70</td>\n",
       "      <td>106.46</td>\n",
       "      <td>106.61</td>\n",
       "      <td>2569628</td>\n",
       "      <td>264.16</td>\n",
       "      <td>265.02</td>\n",
       "      <td>257.83</td>\n",
       "      <td>258.01</td>\n",
       "      <td>117338697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              AGG                                      SPY  \\\n",
       "                             open    high     low   close   volume    open   \n",
       "time                                                                         \n",
       "2018-03-19 00:00:00-04:00  106.56  106.74  106.53  106.58  2068013  273.33   \n",
       "2018-03-20 00:00:00-04:00  106.44  106.51  106.40  106.41  2769390  270.93   \n",
       "2018-03-21 00:00:00-04:00  106.32  106.48  106.08  106.41  2841377  270.90   \n",
       "2018-03-22 00:00:00-04:00  106.67  106.80  106.49  106.61  2789748  267.91   \n",
       "2018-03-23 00:00:00-04:00  106.46  106.70  106.46  106.61  2569628  264.16   \n",
       "\n",
       "                                                              \n",
       "                             high     low   close     volume  \n",
       "time                                                          \n",
       "2018-03-19 00:00:00-04:00  273.38  268.62  270.49   77851316  \n",
       "2018-03-20 00:00:00-04:00  271.67  270.18  270.94   38399686  \n",
       "2018-03-21 00:00:00-04:00  273.27  270.19  270.43   59831437  \n",
       "2018-03-22 00:00:00-04:00  268.87  263.36  263.67  105012105  \n",
       "2018-03-23 00:00:00-04:00  265.02  257.83  258.01  117338697  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get 5 years' worth of historical data for SPY and AGG\n",
    "# (use a limit=1000 parameter to call the most recent 1000 days of data)\n",
    "df_historical = alpaca.get_barset(\n",
    "    tickers,\n",
    "    timeframe,\n",
    "    start = start_date,\n",
    "    end = end_date,\n",
    "    limit = 1000\n",
    ").df\n",
    "\n",
    "# Display sample data\n",
    "df_historical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mMCSimulation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mportfolio_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnum_simulation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnum_trading_days\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m252\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "A Python class for runnning Monte Carlo simulation on portfolio price data. \n",
       "\n",
       "...\n",
       "\n",
       "Attributes\n",
       "----------\n",
       "portfolio_data : pandas.DataFrame\n",
       "    portfolio dataframe\n",
       "weights: list(float)\n",
       "    portfolio investment breakdown\n",
       "nSim: int\n",
       "    number of samples in simulation\n",
       "nTrading: int\n",
       "    number of trading days to simulate\n",
       "simulated_return : pandas.DataFrame\n",
       "    Simulated data from Monte Carlo\n",
       "confidence_interval : pandas.Series\n",
       "    the 95% confidence intervals for simulated final cumulative returns\n",
       "    \n",
       "\u001b[0;31mInit docstring:\u001b[0m\n",
       "Constructs all the necessary attributes for the MCSimulation object.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "portfolio_data: pandas.DataFrame\n",
       "    DataFrame containing stock price information from Alpaca API\n",
       "weights: list(float)\n",
       "    A list fractions representing percentage of total investment per stock. DEFAULT: Equal distribution\n",
       "num_simulation: int\n",
       "    Number of simulation samples. DEFAULT: 1000 simulation samples\n",
       "num_trading_days: int\n",
       "    Number of trading days to simulate. DEFAULT: 252 days (1 year of business days)\n",
       "\u001b[0;31mFile:\u001b[0m           ~/unit-5-assignment/MCForecastTools.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print the documentation of the MCSimulation module of the MCForecastTools library\n",
    "?MCSimulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuring a Monte Carlo simulation to forecast 30 years cumulative returns\n",
    "MC_thirty_year = MCSimulation(\n",
    "    portfolio_data = df_historical,\n",
    "    weights = [.40,.60],\n",
    "    num_simulation = 500,\n",
    "    num_trading_days = 252*30\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-19 00:00:00-04:00</th>\n",
       "      <td>106.56</td>\n",
       "      <td>106.74</td>\n",
       "      <td>106.53</td>\n",
       "      <td>106.58</td>\n",
       "      <td>2068013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>273.33</td>\n",
       "      <td>273.38</td>\n",
       "      <td>268.62</td>\n",
       "      <td>270.49</td>\n",
       "      <td>77851316</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-20 00:00:00-04:00</th>\n",
       "      <td>106.44</td>\n",
       "      <td>106.51</td>\n",
       "      <td>106.40</td>\n",
       "      <td>106.41</td>\n",
       "      <td>2769390</td>\n",
       "      <td>-0.001595</td>\n",
       "      <td>270.93</td>\n",
       "      <td>271.67</td>\n",
       "      <td>270.18</td>\n",
       "      <td>270.94</td>\n",
       "      <td>38399686</td>\n",
       "      <td>0.001664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-21 00:00:00-04:00</th>\n",
       "      <td>106.32</td>\n",
       "      <td>106.48</td>\n",
       "      <td>106.08</td>\n",
       "      <td>106.41</td>\n",
       "      <td>2841377</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>270.90</td>\n",
       "      <td>273.27</td>\n",
       "      <td>270.19</td>\n",
       "      <td>270.43</td>\n",
       "      <td>59831437</td>\n",
       "      <td>-0.001882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-22 00:00:00-04:00</th>\n",
       "      <td>106.67</td>\n",
       "      <td>106.80</td>\n",
       "      <td>106.49</td>\n",
       "      <td>106.61</td>\n",
       "      <td>2789748</td>\n",
       "      <td>0.001880</td>\n",
       "      <td>267.91</td>\n",
       "      <td>268.87</td>\n",
       "      <td>263.36</td>\n",
       "      <td>263.67</td>\n",
       "      <td>105012105</td>\n",
       "      <td>-0.024997</td>\n",
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       "    <tr>\n",
       "      <th>2018-03-23 00:00:00-04:00</th>\n",
       "      <td>106.46</td>\n",
       "      <td>106.70</td>\n",
       "      <td>106.46</td>\n",
       "      <td>106.61</td>\n",
       "      <td>2569628</td>\n",
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       "      <td>264.16</td>\n",
       "      <td>265.02</td>\n",
       "      <td>257.83</td>\n",
       "      <td>258.01</td>\n",
       "      <td>117338697</td>\n",
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      ],
      "text/plain": [
       "                              AGG                                   \\\n",
       "                             open    high     low   close   volume   \n",
       "time                                                                 \n",
       "2018-03-19 00:00:00-04:00  106.56  106.74  106.53  106.58  2068013   \n",
       "2018-03-20 00:00:00-04:00  106.44  106.51  106.40  106.41  2769390   \n",
       "2018-03-21 00:00:00-04:00  106.32  106.48  106.08  106.41  2841377   \n",
       "2018-03-22 00:00:00-04:00  106.67  106.80  106.49  106.61  2789748   \n",
       "2018-03-23 00:00:00-04:00  106.46  106.70  106.46  106.61  2569628   \n",
       "\n",
       "                                           SPY                          \\\n",
       "                          daily_return    open    high     low   close   \n",
       "time                                                                     \n",
       "2018-03-19 00:00:00-04:00          NaN  273.33  273.38  268.62  270.49   \n",
       "2018-03-20 00:00:00-04:00    -0.001595  270.93  271.67  270.18  270.94   \n",
       "2018-03-21 00:00:00-04:00     0.000000  270.90  273.27  270.19  270.43   \n",
       "2018-03-22 00:00:00-04:00     0.001880  267.91  268.87  263.36  263.67   \n",
       "2018-03-23 00:00:00-04:00     0.000000  264.16  265.02  257.83  258.01   \n",
       "\n",
       "                                                   \n",
       "                              volume daily_return  \n",
       "time                                               \n",
       "2018-03-19 00:00:00-04:00   77851316          NaN  \n",
       "2018-03-20 00:00:00-04:00   38399686     0.001664  \n",
       "2018-03-21 00:00:00-04:00   59831437    -0.001882  \n",
       "2018-03-22 00:00:00-04:00  105012105    -0.024997  \n",
       "2018-03-23 00:00:00-04:00  117338697    -0.021466  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the simulation input data\n",
    "MC_thirty_year.portfolio_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sarahm_44/unit-5-assignment/MCForecastTools.py:117: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  portfolio_cumulative_returns[n] = (1 + sim_df.fillna(0)).cumprod()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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",
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      "Running Monte Carlo simulation number 310.\n",
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      "Running Monte Carlo simulation number 330.\n",
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      "Running Monte Carlo simulation number 350.\n",
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      "Running Monte Carlo simulation number 380.\n",
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      "Running Monte Carlo simulation number 400.\n",
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      "Running Monte Carlo simulation number 480.\n",
      "Running Monte Carlo simulation number 490.\n"
     ]
    },
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       "      <td>0.975442</td>\n",
       "      <td>1.033192</td>\n",
       "      <td>0.998925</td>\n",
       "      <td>0.999581</td>\n",
       "      <td>1.010845</td>\n",
       "      <td>0.999586</td>\n",
       "      <td>0.989842</td>\n",
       "      <td>0.999941</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>8.068448</td>\n",
       "      <td>21.157276</td>\n",
       "      <td>84.336385</td>\n",
       "      <td>3.605803</td>\n",
       "      <td>41.350446</td>\n",
       "      <td>29.940262</td>\n",
       "      <td>2.836491</td>\n",
       "      <td>4.490593</td>\n",
       "      <td>5.886203</td>\n",
       "      <td>10.733828</td>\n",
       "      <td>...</td>\n",
       "      <td>15.032029</td>\n",
       "      <td>7.137491</td>\n",
       "      <td>23.890064</td>\n",
       "      <td>7.080464</td>\n",
       "      <td>6.693095</td>\n",
       "      <td>8.666965</td>\n",
       "      <td>3.413706</td>\n",
       "      <td>14.075374</td>\n",
       "      <td>23.983954</td>\n",
       "      <td>9.812162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7557</th>\n",
       "      <td>8.183382</td>\n",
       "      <td>21.203510</td>\n",
       "      <td>83.327973</td>\n",
       "      <td>3.620259</td>\n",
       "      <td>40.930835</td>\n",
       "      <td>30.059173</td>\n",
       "      <td>2.841842</td>\n",
       "      <td>4.565916</td>\n",
       "      <td>6.020796</td>\n",
       "      <td>10.704896</td>\n",
       "      <td>...</td>\n",
       "      <td>15.063816</td>\n",
       "      <td>7.197810</td>\n",
       "      <td>23.811199</td>\n",
       "      <td>7.084319</td>\n",
       "      <td>6.757280</td>\n",
       "      <td>8.681097</td>\n",
       "      <td>3.466014</td>\n",
       "      <td>14.287315</td>\n",
       "      <td>24.257944</td>\n",
       "      <td>9.815241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7558</th>\n",
       "      <td>8.269383</td>\n",
       "      <td>21.088021</td>\n",
       "      <td>82.941280</td>\n",
       "      <td>3.604198</td>\n",
       "      <td>40.877999</td>\n",
       "      <td>30.434727</td>\n",
       "      <td>2.846316</td>\n",
       "      <td>4.568131</td>\n",
       "      <td>6.048106</td>\n",
       "      <td>10.733413</td>\n",
       "      <td>...</td>\n",
       "      <td>15.204965</td>\n",
       "      <td>7.242525</td>\n",
       "      <td>23.859183</td>\n",
       "      <td>7.149833</td>\n",
       "      <td>6.721498</td>\n",
       "      <td>8.802670</td>\n",
       "      <td>3.466687</td>\n",
       "      <td>14.177022</td>\n",
       "      <td>24.625678</td>\n",
       "      <td>9.952258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7559</th>\n",
       "      <td>8.254628</td>\n",
       "      <td>21.024896</td>\n",
       "      <td>83.192437</td>\n",
       "      <td>3.578660</td>\n",
       "      <td>40.722519</td>\n",
       "      <td>29.892470</td>\n",
       "      <td>2.850776</td>\n",
       "      <td>4.631545</td>\n",
       "      <td>6.062626</td>\n",
       "      <td>10.750674</td>\n",
       "      <td>...</td>\n",
       "      <td>15.266163</td>\n",
       "      <td>7.338686</td>\n",
       "      <td>24.023395</td>\n",
       "      <td>7.124136</td>\n",
       "      <td>6.782821</td>\n",
       "      <td>8.893072</td>\n",
       "      <td>3.474156</td>\n",
       "      <td>14.284214</td>\n",
       "      <td>24.845219</td>\n",
       "      <td>9.860619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7560</th>\n",
       "      <td>8.158659</td>\n",
       "      <td>21.202918</td>\n",
       "      <td>83.450531</td>\n",
       "      <td>3.559001</td>\n",
       "      <td>40.139908</td>\n",
       "      <td>29.689922</td>\n",
       "      <td>2.830893</td>\n",
       "      <td>4.649682</td>\n",
       "      <td>6.113867</td>\n",
       "      <td>10.638485</td>\n",
       "      <td>...</td>\n",
       "      <td>15.291881</td>\n",
       "      <td>7.438459</td>\n",
       "      <td>24.375175</td>\n",
       "      <td>7.050452</td>\n",
       "      <td>6.806380</td>\n",
       "      <td>8.985791</td>\n",
       "      <td>3.503991</td>\n",
       "      <td>14.220389</td>\n",
       "      <td>25.004139</td>\n",
       "      <td>9.911541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7561 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0          1          2         3          4          5    \\\n",
       "0     1.000000   1.000000   1.000000  1.000000   1.000000   1.000000   \n",
       "1     1.009568   0.994635   1.003846  1.001285   1.005448   0.999854   \n",
       "2     1.024504   0.980731   0.991077  1.000683   1.020324   1.010528   \n",
       "3     1.027940   0.982157   1.004465  0.997718   1.003157   1.015678   \n",
       "4     1.034800   0.987974   0.999144  1.018032   0.993971   1.005330   \n",
       "...        ...        ...        ...       ...        ...        ...   \n",
       "7556  8.068448  21.157276  84.336385  3.605803  41.350446  29.940262   \n",
       "7557  8.183382  21.203510  83.327973  3.620259  40.930835  30.059173   \n",
       "7558  8.269383  21.088021  82.941280  3.604198  40.877999  30.434727   \n",
       "7559  8.254628  21.024896  83.192437  3.578660  40.722519  29.892470   \n",
       "7560  8.158659  21.202918  83.450531  3.559001  40.139908  29.689922   \n",
       "\n",
       "           6         7         8          9    ...        490       491  \\\n",
       "0     1.000000  1.000000  1.000000   1.000000  ...   1.000000  1.000000   \n",
       "1     0.996204  1.004835  1.010377   1.003606  ...   0.996078  1.007284   \n",
       "2     0.986012  0.995176  1.013990   0.988083  ...   0.986479  1.006137   \n",
       "3     0.983244  1.000439  1.012079   1.009930  ...   1.005260  1.012878   \n",
       "4     0.975100  0.985296  1.009891   0.995654  ...   1.007462  1.022222   \n",
       "...        ...       ...       ...        ...  ...        ...       ...   \n",
       "7556  2.836491  4.490593  5.886203  10.733828  ...  15.032029  7.137491   \n",
       "7557  2.841842  4.565916  6.020796  10.704896  ...  15.063816  7.197810   \n",
       "7558  2.846316  4.568131  6.048106  10.733413  ...  15.204965  7.242525   \n",
       "7559  2.850776  4.631545  6.062626  10.750674  ...  15.266163  7.338686   \n",
       "7560  2.830893  4.649682  6.113867  10.638485  ...  15.291881  7.438459   \n",
       "\n",
       "            492       493       494       495       496        497        498  \\\n",
       "0      1.000000  1.000000  1.000000  1.000000  1.000000   1.000000   1.000000   \n",
       "1      0.982173  1.004519  1.002220  1.011454  0.996235   0.998067   0.998563   \n",
       "2      0.974425  1.005136  0.993662  1.014287  0.995813   0.991255   0.997185   \n",
       "3      0.974770  1.010379  0.994234  1.007708  0.997660   0.996330   0.985478   \n",
       "4      0.975442  1.033192  0.998925  0.999581  1.010845   0.999586   0.989842   \n",
       "...         ...       ...       ...       ...       ...        ...        ...   \n",
       "7556  23.890064  7.080464  6.693095  8.666965  3.413706  14.075374  23.983954   \n",
       "7557  23.811199  7.084319  6.757280  8.681097  3.466014  14.287315  24.257944   \n",
       "7558  23.859183  7.149833  6.721498  8.802670  3.466687  14.177022  24.625678   \n",
       "7559  24.023395  7.124136  6.782821  8.893072  3.474156  14.284214  24.845219   \n",
       "7560  24.375175  7.050452  6.806380  8.985791  3.503991  14.220389  25.004139   \n",
       "\n",
       "           499  \n",
       "0     1.000000  \n",
       "1     1.007558  \n",
       "2     1.007200  \n",
       "3     0.996200  \n",
       "4     0.999941  \n",
       "...        ...  \n",
       "7556  9.812162  \n",
       "7557  9.815241  \n",
       "7558  9.952258  \n",
       "7559  9.860619  \n",
       "7560  9.911541  \n",
       "\n",
       "[7561 rows x 500 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running a Monte Carlo simulation to forecast 30 years cumulative returns\n",
    "MC_thirty_year.calc_cumulative_return()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot simulation outcomes\n",
    "line_plot = MC_thirty_year.plot_simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot probability distribution and confidence intervals\n",
    "dist_plot = MC_thirty_year.plot_distribution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retirement Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count           500.000000\n",
      "mean             12.494757\n",
      "std              10.463689\n",
      "min               1.008157\n",
      "25%               6.002944\n",
      "50%               9.515495\n",
      "75%              16.049230\n",
      "max              98.872147\n",
      "95% CI Lower      2.854088\n",
      "95% CI Upper     37.569648\n",
      "Name: 7560, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Fetch summary statistics from the Monte Carlo simulation results\n",
    "tbl = MC_thirty_year.summarize_cumulative_return()\n",
    "\n",
    "# Print summary statistics\n",
    "print(tbl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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": 65,
   "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 $16368.71 and $51655.29\n"
     ]
    }
   ],
   "source": [
    "# Set initial investment\n",
    "initial_investment = 20000\n",
    "\n",
    "# Use the lower and upper `95%` confidence intervals \n",
    "# to calculate the range of the possible outcomes of our $20,000\n",
    "ci_lower = round(tbl[8]*initial_investment,2)\n",
    "ci_upper = round(tbl[9]*initial_investment,2)\n",
    "\n",
    "# Print results\n",
    "print(f\"There is a 95% chance that an initial investment of ${initial_investment} in the portfolio\"\n",
    "      f\" over the next 30 years will end within in the range of\"\n",
    "      f\" ${ci_lower} and ${ci_upper}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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": 66,
   "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 $24553.07 and $77482.93\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_30k = round(tbl[8]*initial_investment,2)\n",
    "ci_upper_30k = round(tbl[9]*initial_investment,2)\n",
    "\n",
    "# Print results\n",
    "print(f\"There is a 95% chance that an initial investment of ${initial_investment} in the portfolio\"\n",
    "      f\" over the next 30 years will end within in the range of\"\n",
    "      f\" ${ci_lower_30k} and ${ci_upper_30k}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optional Challenge - Early Retirement\n",
    "\n",
    "\n",
    "### Five Years Retirement Option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">AGG</th>\n",
       "      <th colspan=\"6\" halign=\"left\">SPY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>daily_return</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>daily_return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-19 00:00:00-04:00</th>\n",
       "      <td>106.56</td>\n",
       "      <td>106.74</td>\n",
       "      <td>106.53</td>\n",
       "      <td>106.58</td>\n",
       "      <td>2068013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>273.33</td>\n",
       "      <td>273.38</td>\n",
       "      <td>268.62</td>\n",
       "      <td>270.49</td>\n",
       "      <td>77851316</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-20 00:00:00-04:00</th>\n",
       "      <td>106.44</td>\n",
       "      <td>106.51</td>\n",
       "      <td>106.40</td>\n",
       "      <td>106.41</td>\n",
       "      <td>2769390</td>\n",
       "      <td>-0.001595</td>\n",
       "      <td>270.93</td>\n",
       "      <td>271.67</td>\n",
       "      <td>270.18</td>\n",
       "      <td>270.94</td>\n",
       "      <td>38399686</td>\n",
       "      <td>0.001664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-21 00:00:00-04:00</th>\n",
       "      <td>106.32</td>\n",
       "      <td>106.48</td>\n",
       "      <td>106.08</td>\n",
       "      <td>106.41</td>\n",
       "      <td>2841377</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>270.90</td>\n",
       "      <td>273.27</td>\n",
       "      <td>270.19</td>\n",
       "      <td>270.43</td>\n",
       "      <td>59831437</td>\n",
       "      <td>-0.001882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-22 00:00:00-04:00</th>\n",
       "      <td>106.67</td>\n",
       "      <td>106.80</td>\n",
       "      <td>106.49</td>\n",
       "      <td>106.61</td>\n",
       "      <td>2789748</td>\n",
       "      <td>0.001880</td>\n",
       "      <td>267.91</td>\n",
       "      <td>268.87</td>\n",
       "      <td>263.36</td>\n",
       "      <td>263.67</td>\n",
       "      <td>105012105</td>\n",
       "      <td>-0.024997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-23 00:00:00-04:00</th>\n",
       "      <td>106.46</td>\n",
       "      <td>106.70</td>\n",
       "      <td>106.46</td>\n",
       "      <td>106.61</td>\n",
       "      <td>2569628</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>264.16</td>\n",
       "      <td>265.02</td>\n",
       "      <td>257.83</td>\n",
       "      <td>258.01</td>\n",
       "      <td>117338697</td>\n",
       "      <td>-0.021466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              AGG                                   \\\n",
       "                             open    high     low   close   volume   \n",
       "time                                                                 \n",
       "2018-03-19 00:00:00-04:00  106.56  106.74  106.53  106.58  2068013   \n",
       "2018-03-20 00:00:00-04:00  106.44  106.51  106.40  106.41  2769390   \n",
       "2018-03-21 00:00:00-04:00  106.32  106.48  106.08  106.41  2841377   \n",
       "2018-03-22 00:00:00-04:00  106.67  106.80  106.49  106.61  2789748   \n",
       "2018-03-23 00:00:00-04:00  106.46  106.70  106.46  106.61  2569628   \n",
       "\n",
       "                                           SPY                          \\\n",
       "                          daily_return    open    high     low   close   \n",
       "time                                                                     \n",
       "2018-03-19 00:00:00-04:00          NaN  273.33  273.38  268.62  270.49   \n",
       "2018-03-20 00:00:00-04:00    -0.001595  270.93  271.67  270.18  270.94   \n",
       "2018-03-21 00:00:00-04:00     0.000000  270.90  273.27  270.19  270.43   \n",
       "2018-03-22 00:00:00-04:00     0.001880  267.91  268.87  263.36  263.67   \n",
       "2018-03-23 00:00:00-04:00     0.000000  264.16  265.02  257.83  258.01   \n",
       "\n",
       "                                                   \n",
       "                              volume daily_return  \n",
       "time                                               \n",
       "2018-03-19 00:00:00-04:00   77851316          NaN  \n",
       "2018-03-20 00:00:00-04:00   38399686     0.001664  \n",
       "2018-03-21 00:00:00-04:00   59831437    -0.001882  \n",
       "2018-03-22 00:00:00-04:00  105012105    -0.024997  \n",
       "2018-03-23 00:00:00-04:00  117338697    -0.021466  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Configuring a Monte Carlo simulation to forecast 5 years cumulative returns\n",
    "# Note: Will use a $60,000 initial investment for this simulation\n",
    "MC_five_year = MCSimulation(\n",
    "    portfolio_data = df_historical,\n",
    "    weights = [.40,.60],\n",
    "    num_simulation = 500,\n",
    "    num_trading_days = 252*5\n",
    ")\n",
    "\n",
    "# Printing the simulation input data\n",
    "MC_five_year.portfolio_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sarahm_44/unit-5-assignment/MCForecastTools.py:117: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  portfolio_cumulative_returns[n] = (1 + sim_df.fillna(0)).cumprod()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Monte Carlo simulation number 110.\n",
      "Running Monte Carlo simulation number 120.\n",
      "Running Monte Carlo simulation number 130.\n",
      "Running Monte Carlo simulation number 140.\n",
      "Running Monte Carlo simulation number 150.\n",
      "Running Monte Carlo simulation number 160.\n",
      "Running Monte Carlo simulation number 170.\n",
      "Running Monte Carlo simulation number 180.\n",
      "Running Monte Carlo simulation number 190.\n",
      "Running Monte Carlo simulation number 200.\n",
      "Running Monte Carlo simulation number 210.\n",
      "Running Monte Carlo simulation number 220.\n",
      "Running Monte Carlo simulation number 230.\n",
      "Running Monte Carlo simulation number 240.\n",
      "Running Monte Carlo simulation number 250.\n",
      "Running Monte Carlo simulation number 260.\n",
      "Running Monte Carlo simulation number 270.\n",
      "Running Monte Carlo simulation number 280.\n",
      "Running Monte Carlo simulation number 290.\n",
      "Running Monte Carlo simulation number 300.\n",
      "Running Monte Carlo simulation number 310.\n",
      "Running Monte Carlo simulation number 320.\n",
      "Running Monte Carlo simulation number 330.\n",
      "Running Monte Carlo simulation number 340.\n",
      "Running Monte Carlo simulation number 350.\n",
      "Running Monte Carlo simulation number 360.\n",
      "Running Monte Carlo simulation number 370.\n",
      "Running Monte Carlo simulation number 380.\n",
      "Running Monte Carlo simulation number 390.\n",
      "Running Monte Carlo simulation number 400.\n",
      "Running Monte Carlo simulation number 410.\n",
      "Running Monte Carlo simulation number 420.\n",
      "Running Monte Carlo simulation number 430.\n",
      "Running Monte Carlo simulation number 440.\n",
      "Running Monte Carlo simulation number 450.\n",
      "Running Monte Carlo simulation number 460.\n",
      "Running Monte Carlo simulation number 470.\n",
      "Running Monte Carlo simulation number 480.\n",
      "Running Monte Carlo simulation number 490.\n"
     ]
    },
    {
     "data": {
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       "1256  0.678878  2.153978  1.406837  1.049520  1.188691  0.988273  1.069599  \n",
       "1257  0.679445  2.152290  1.407109  1.035902  1.188786  0.998185  1.061415  \n",
       "1258  0.678924  2.152769  1.415505  1.037555  1.189297  0.996849  1.059717  \n",
       "1259  0.679168  2.148036  1.430620  1.038236  1.183074  0.996644  1.072825  \n",
       "1260  0.680664  2.147870  1.451473  1.048881  1.194761  0.996299  1.068749  \n",
       "\n",
       "[1261 rows x 500 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running a Monte Carlo simulation to forecast 5 years cumulative returns\n",
    "MC_five_year.calc_cumulative_return()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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_five_year.plot_simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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_five_year.plot_distribution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count           500.000000\n",
      "mean              1.530907\n",
      "std               0.448230\n",
      "min               0.487543\n",
      "25%               1.213657\n",
      "50%               1.471769\n",
      "75%               1.767206\n",
      "max               3.201970\n",
      "95% CI Lower      0.818436\n",
      "95% CI Upper      2.582764\n",
      "Name: 1260, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Fetch summary statistics from the Monte Carlo simulation results\n",
    "tbl_five = MC_five_year.summarize_cumulative_return()\n",
    "\n",
    "# Print summary statistics\n",
    "print(tbl_five)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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 $49106.13 and $154965.86\n"
     ]
    }
   ],
   "source": [
    "# Set initial investment\n",
    "initial_investment = 60000\n",
    "\n",
    "# Use the lower and upper `95%` confidence intervals to \n",
    "# calculate the range of the possible outcomes of our $60,000\n",
    "ci_lower_five = round(tbl_five[8]*initial_investment,2)\n",
    "ci_upper_five = round(tbl_five[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": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-19 00:00:00-04:00</th>\n",
       "      <td>106.56</td>\n",
       "      <td>106.74</td>\n",
       "      <td>106.53</td>\n",
       "      <td>106.58</td>\n",
       "      <td>2068013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>273.33</td>\n",
       "      <td>273.38</td>\n",
       "      <td>268.62</td>\n",
       "      <td>270.49</td>\n",
       "      <td>77851316</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-20 00:00:00-04:00</th>\n",
       "      <td>106.44</td>\n",
       "      <td>106.51</td>\n",
       "      <td>106.40</td>\n",
       "      <td>106.41</td>\n",
       "      <td>2769390</td>\n",
       "      <td>-0.001595</td>\n",
       "      <td>270.93</td>\n",
       "      <td>271.67</td>\n",
       "      <td>270.18</td>\n",
       "      <td>270.94</td>\n",
       "      <td>38399686</td>\n",
       "      <td>0.001664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-21 00:00:00-04:00</th>\n",
       "      <td>106.32</td>\n",
       "      <td>106.48</td>\n",
       "      <td>106.08</td>\n",
       "      <td>106.41</td>\n",
       "      <td>2841377</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>270.90</td>\n",
       "      <td>273.27</td>\n",
       "      <td>270.19</td>\n",
       "      <td>270.43</td>\n",
       "      <td>59831437</td>\n",
       "      <td>-0.001882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-22 00:00:00-04:00</th>\n",
       "      <td>106.67</td>\n",
       "      <td>106.80</td>\n",
       "      <td>106.49</td>\n",
       "      <td>106.61</td>\n",
       "      <td>2789748</td>\n",
       "      <td>0.001880</td>\n",
       "      <td>267.91</td>\n",
       "      <td>268.87</td>\n",
       "      <td>263.36</td>\n",
       "      <td>263.67</td>\n",
       "      <td>105012105</td>\n",
       "      <td>-0.024997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-23 00:00:00-04:00</th>\n",
       "      <td>106.46</td>\n",
       "      <td>106.70</td>\n",
       "      <td>106.46</td>\n",
       "      <td>106.61</td>\n",
       "      <td>2569628</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>264.16</td>\n",
       "      <td>265.02</td>\n",
       "      <td>257.83</td>\n",
       "      <td>258.01</td>\n",
       "      <td>117338697</td>\n",
       "      <td>-0.021466</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                              AGG                                   \\\n",
       "                             open    high     low   close   volume   \n",
       "time                                                                 \n",
       "2018-03-19 00:00:00-04:00  106.56  106.74  106.53  106.58  2068013   \n",
       "2018-03-20 00:00:00-04:00  106.44  106.51  106.40  106.41  2769390   \n",
       "2018-03-21 00:00:00-04:00  106.32  106.48  106.08  106.41  2841377   \n",
       "2018-03-22 00:00:00-04:00  106.67  106.80  106.49  106.61  2789748   \n",
       "2018-03-23 00:00:00-04:00  106.46  106.70  106.46  106.61  2569628   \n",
       "\n",
       "                                           SPY                          \\\n",
       "                          daily_return    open    high     low   close   \n",
       "time                                                                     \n",
       "2018-03-19 00:00:00-04:00          NaN  273.33  273.38  268.62  270.49   \n",
       "2018-03-20 00:00:00-04:00    -0.001595  270.93  271.67  270.18  270.94   \n",
       "2018-03-21 00:00:00-04:00     0.000000  270.90  273.27  270.19  270.43   \n",
       "2018-03-22 00:00:00-04:00     0.001880  267.91  268.87  263.36  263.67   \n",
       "2018-03-23 00:00:00-04:00     0.000000  264.16  265.02  257.83  258.01   \n",
       "\n",
       "                                                   \n",
       "                              volume daily_return  \n",
       "time                                               \n",
       "2018-03-19 00:00:00-04:00   77851316          NaN  \n",
       "2018-03-20 00:00:00-04:00   38399686     0.001664  \n",
       "2018-03-21 00:00:00-04:00   59831437    -0.001882  \n",
       "2018-03-22 00:00:00-04:00  105012105    -0.024997  \n",
       "2018-03-23 00:00:00-04:00  117338697    -0.021466  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Configuring a Monte Carlo simulation to forecast 10 years cumulative returns\n",
    "# Note: Will use a $60,000 initial investment for this simulation\n",
    "MC_ten_year = MCSimulation(\n",
    "    portfolio_data = df_historical,\n",
    "    weights = [.40,.60],\n",
    "    num_simulation = 500,\n",
    "    num_trading_days = 252*10\n",
    ")\n",
    "\n",
    "# Printing the simulation input data\n",
    "MC_ten_year.portfolio_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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",
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      "Running Monte Carlo simulation number 150.\n",
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      "Running Monte Carlo simulation number 200.\n",
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      "Running Monte Carlo simulation number 220.\n",
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      "Running Monte Carlo simulation number 240.\n",
      "Running Monte Carlo simulation number 250.\n",
      "Running Monte Carlo simulation number 260.\n",
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      "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",
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      "Running Monte Carlo simulation number 450.\n",
      "Running Monte Carlo simulation number 460.\n",
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      "Running Monte Carlo simulation number 480.\n",
      "Running Monte Carlo simulation number 490.\n"
     ]
    },
    {
     "data": {
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       "      <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",
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       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.006014</td>\n",
       "      <td>1.007731</td>\n",
       "      <td>0.997493</td>\n",
       "      <td>0.987150</td>\n",
       "      <td>1.000669</td>\n",
       "      <td>0.998810</td>\n",
       "      <td>1.016211</td>\n",
       "      <td>1.007386</td>\n",
       "      <td>0.994519</td>\n",
       "      <td>1.013157</td>\n",
       "      <td>...</td>\n",
       "      <td>1.009582</td>\n",
       "      <td>0.999198</td>\n",
       "      <td>1.000843</td>\n",
       "      <td>1.008025</td>\n",
       "      <td>0.994925</td>\n",
       "      <td>0.997395</td>\n",
       "      <td>1.001998</td>\n",
       "      <td>1.007020</td>\n",
       "      <td>0.998073</td>\n",
       "      <td>0.983867</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.002765</td>\n",
       "      <td>1.014762</td>\n",
       "      <td>0.995765</td>\n",
       "      <td>0.991792</td>\n",
       "      <td>1.004286</td>\n",
       "      <td>0.994648</td>\n",
       "      <td>1.011641</td>\n",
       "      <td>1.005350</td>\n",
       "      <td>0.990975</td>\n",
       "      <td>1.018629</td>\n",
       "      <td>...</td>\n",
       "      <td>1.010390</td>\n",
       "      <td>1.000546</td>\n",
       "      <td>1.002128</td>\n",
       "      <td>1.009037</td>\n",
       "      <td>0.989311</td>\n",
       "      <td>0.995437</td>\n",
       "      <td>1.003014</td>\n",
       "      <td>0.996131</td>\n",
       "      <td>1.017168</td>\n",
       "      <td>0.986573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.016227</td>\n",
       "      <td>1.024584</td>\n",
       "      <td>0.981850</td>\n",
       "      <td>0.982434</td>\n",
       "      <td>1.009506</td>\n",
       "      <td>1.001189</td>\n",
       "      <td>1.001898</td>\n",
       "      <td>1.006439</td>\n",
       "      <td>0.996807</td>\n",
       "      <td>1.022858</td>\n",
       "      <td>...</td>\n",
       "      <td>1.024695</td>\n",
       "      <td>1.001231</td>\n",
       "      <td>0.996258</td>\n",
       "      <td>1.012431</td>\n",
       "      <td>0.998743</td>\n",
       "      <td>0.992367</td>\n",
       "      <td>1.005472</td>\n",
       "      <td>1.000233</td>\n",
       "      <td>1.019054</td>\n",
       "      <td>0.984765</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.021128</td>\n",
       "      <td>1.033198</td>\n",
       "      <td>0.964368</td>\n",
       "      <td>0.976999</td>\n",
       "      <td>1.012607</td>\n",
       "      <td>1.001417</td>\n",
       "      <td>1.014541</td>\n",
       "      <td>0.993911</td>\n",
       "      <td>0.996037</td>\n",
       "      <td>1.044200</td>\n",
       "      <td>...</td>\n",
       "      <td>1.013150</td>\n",
       "      <td>0.998337</td>\n",
       "      <td>1.001748</td>\n",
       "      <td>1.011605</td>\n",
       "      <td>1.006036</td>\n",
       "      <td>0.998546</td>\n",
       "      <td>1.012308</td>\n",
       "      <td>1.017530</td>\n",
       "      <td>1.026739</td>\n",
       "      <td>0.988594</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>2516</th>\n",
       "      <td>2.033232</td>\n",
       "      <td>1.515738</td>\n",
       "      <td>0.772083</td>\n",
       "      <td>2.542634</td>\n",
       "      <td>1.470677</td>\n",
       "      <td>2.609638</td>\n",
       "      <td>3.674539</td>\n",
       "      <td>1.970858</td>\n",
       "      <td>1.196402</td>\n",
       "      <td>3.258880</td>\n",
       "      <td>...</td>\n",
       "      <td>1.794237</td>\n",
       "      <td>2.055292</td>\n",
       "      <td>3.191482</td>\n",
       "      <td>1.554811</td>\n",
       "      <td>2.752875</td>\n",
       "      <td>3.522383</td>\n",
       "      <td>3.655702</td>\n",
       "      <td>1.337878</td>\n",
       "      <td>0.987028</td>\n",
       "      <td>2.084350</td>\n",
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       "    <tr>\n",
       "      <th>2517</th>\n",
       "      <td>2.024304</td>\n",
       "      <td>1.501586</td>\n",
       "      <td>0.771628</td>\n",
       "      <td>2.560330</td>\n",
       "      <td>1.480949</td>\n",
       "      <td>2.609360</td>\n",
       "      <td>3.671124</td>\n",
       "      <td>1.954909</td>\n",
       "      <td>1.193366</td>\n",
       "      <td>3.256837</td>\n",
       "      <td>...</td>\n",
       "      <td>1.811117</td>\n",
       "      <td>2.049662</td>\n",
       "      <td>3.201365</td>\n",
       "      <td>1.549942</td>\n",
       "      <td>2.730501</td>\n",
       "      <td>3.532172</td>\n",
       "      <td>3.703336</td>\n",
       "      <td>1.326526</td>\n",
       "      <td>0.977295</td>\n",
       "      <td>2.060161</td>\n",
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       "    <tr>\n",
       "      <th>2518</th>\n",
       "      <td>2.034407</td>\n",
       "      <td>1.503806</td>\n",
       "      <td>0.768883</td>\n",
       "      <td>2.572776</td>\n",
       "      <td>1.491849</td>\n",
       "      <td>2.620355</td>\n",
       "      <td>3.673916</td>\n",
       "      <td>1.972608</td>\n",
       "      <td>1.197504</td>\n",
       "      <td>3.262313</td>\n",
       "      <td>...</td>\n",
       "      <td>1.817699</td>\n",
       "      <td>2.051343</td>\n",
       "      <td>3.181889</td>\n",
       "      <td>1.556278</td>\n",
       "      <td>2.748411</td>\n",
       "      <td>3.513798</td>\n",
       "      <td>3.686011</td>\n",
       "      <td>1.331051</td>\n",
       "      <td>0.976689</td>\n",
       "      <td>2.076150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2519</th>\n",
       "      <td>2.036593</td>\n",
       "      <td>1.509257</td>\n",
       "      <td>0.767189</td>\n",
       "      <td>2.556253</td>\n",
       "      <td>1.503204</td>\n",
       "      <td>2.624725</td>\n",
       "      <td>3.712548</td>\n",
       "      <td>1.957819</td>\n",
       "      <td>1.202983</td>\n",
       "      <td>3.254821</td>\n",
       "      <td>...</td>\n",
       "      <td>1.825228</td>\n",
       "      <td>2.017651</td>\n",
       "      <td>3.167777</td>\n",
       "      <td>1.568948</td>\n",
       "      <td>2.743485</td>\n",
       "      <td>3.517326</td>\n",
       "      <td>3.652257</td>\n",
       "      <td>1.335791</td>\n",
       "      <td>0.985726</td>\n",
       "      <td>2.055509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2520</th>\n",
       "      <td>2.037807</td>\n",
       "      <td>1.503869</td>\n",
       "      <td>0.766930</td>\n",
       "      <td>2.542099</td>\n",
       "      <td>1.493330</td>\n",
       "      <td>2.628086</td>\n",
       "      <td>3.747018</td>\n",
       "      <td>1.935061</td>\n",
       "      <td>1.217005</td>\n",
       "      <td>3.258900</td>\n",
       "      <td>...</td>\n",
       "      <td>1.832629</td>\n",
       "      <td>2.037347</td>\n",
       "      <td>3.178706</td>\n",
       "      <td>1.562357</td>\n",
       "      <td>2.782007</td>\n",
       "      <td>3.543263</td>\n",
       "      <td>3.675891</td>\n",
       "      <td>1.322088</td>\n",
       "      <td>0.975987</td>\n",
       "      <td>2.056241</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     1.006014  1.007731  0.997493  0.987150  1.000669  0.998810  1.016211   \n",
       "2     1.002765  1.014762  0.995765  0.991792  1.004286  0.994648  1.011641   \n",
       "3     1.016227  1.024584  0.981850  0.982434  1.009506  1.001189  1.001898   \n",
       "4     1.021128  1.033198  0.964368  0.976999  1.012607  1.001417  1.014541   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "2516  2.033232  1.515738  0.772083  2.542634  1.470677  2.609638  3.674539   \n",
       "2517  2.024304  1.501586  0.771628  2.560330  1.480949  2.609360  3.671124   \n",
       "2518  2.034407  1.503806  0.768883  2.572776  1.491849  2.620355  3.673916   \n",
       "2519  2.036593  1.509257  0.767189  2.556253  1.503204  2.624725  3.712548   \n",
       "2520  2.037807  1.503869  0.766930  2.542099  1.493330  2.628086  3.747018   \n",
       "\n",
       "           7         8         9    ...       490       491       492  \\\n",
       "0     1.000000  1.000000  1.000000  ...  1.000000  1.000000  1.000000   \n",
       "1     1.007386  0.994519  1.013157  ...  1.009582  0.999198  1.000843   \n",
       "2     1.005350  0.990975  1.018629  ...  1.010390  1.000546  1.002128   \n",
       "3     1.006439  0.996807  1.022858  ...  1.024695  1.001231  0.996258   \n",
       "4     0.993911  0.996037  1.044200  ...  1.013150  0.998337  1.001748   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "2516  1.970858  1.196402  3.258880  ...  1.794237  2.055292  3.191482   \n",
       "2517  1.954909  1.193366  3.256837  ...  1.811117  2.049662  3.201365   \n",
       "2518  1.972608  1.197504  3.262313  ...  1.817699  2.051343  3.181889   \n",
       "2519  1.957819  1.202983  3.254821  ...  1.825228  2.017651  3.167777   \n",
       "2520  1.935061  1.217005  3.258900  ...  1.832629  2.037347  3.178706   \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     1.008025  0.994925  0.997395  1.001998  1.007020  0.998073  0.983867  \n",
       "2     1.009037  0.989311  0.995437  1.003014  0.996131  1.017168  0.986573  \n",
       "3     1.012431  0.998743  0.992367  1.005472  1.000233  1.019054  0.984765  \n",
       "4     1.011605  1.006036  0.998546  1.012308  1.017530  1.026739  0.988594  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "2516  1.554811  2.752875  3.522383  3.655702  1.337878  0.987028  2.084350  \n",
       "2517  1.549942  2.730501  3.532172  3.703336  1.326526  0.977295  2.060161  \n",
       "2518  1.556278  2.748411  3.513798  3.686011  1.331051  0.976689  2.076150  \n",
       "2519  1.568948  2.743485  3.517326  3.652257  1.335791  0.985726  2.055509  \n",
       "2520  1.562357  2.782007  3.543263  3.675891  1.322088  0.975987  2.056241  \n",
       "\n",
       "[2521 rows x 500 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running a Monte Carlo simulation to forecast 10 years cumulative returns\n",
    "MC_ten_year.calc_cumulative_return()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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_ten_year.plot_simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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_ten_year.plot_distribution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count           500.000000\n",
      "mean              2.258431\n",
      "std               1.038472\n",
      "min               0.578037\n",
      "25%               1.549478\n",
      "50%               2.030795\n",
      "75%               2.703228\n",
      "max               7.527487\n",
      "95% CI Lower      0.920034\n",
      "95% CI Upper      4.965415\n",
      "Name: 2520, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Fetch summary statistics from the Monte Carlo simulation results\n",
    "tbl_ten = MC_ten_year.summarize_cumulative_return()\n",
    "\n",
    "# Print summary statistics\n",
    "print(tbl_ten)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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 $55202.01 and $297924.88\n"
     ]
    }
   ],
   "source": [
    "# Set initial investment\n",
    "initial_investment = 60000\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(tbl_ten[8]*initial_investment,2)\n",
    "ci_upper_ten = round(tbl_ten[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": []
  }
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