Idealized_Portfolio.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2ce6ad5e",
   "metadata": {},
   "source": [
    "# Creating an Ideal Portfolio\n",
    "We want to create a top performing portfolio with only the top two industries that have the highest cumulative returns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c58437bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 5,
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   "execution_count": 2,
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   "id": "3ac7a0de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the Average of all Cumulative returns \n",
    "Data1 = Path(\"cumulative_returns_data.csv\")\n",
    "cumulative1_df = pd.read_csv(Data1)\n",
    "\n",
    "Data2 = Path(\"cumulative_returns_data2.csv\")\n",
    "cumulative2_df = pd.read_csv(Data2)\n",
    "\n",
    "Data3 = Path(\"cumulative_returns_data3.csv\")\n",
    "cumulative3_df = pd.read_csv(Data3)\n",
    "\n",
    "Data4 = Path(\"cumulative_returns_data4.csv\")\n",
    "cumulative4_df = pd.read_csv(Data4)\n",
    "\n",
    "Data5 = Path(\"cumulative_returns_data5.csv\")\n",
    "cumulative5_df = pd.read_csv(Data5)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 6,
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   "execution_count": 3,
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   "id": "24af7e88",
   "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>Tech</th>\n",
       "      <th>Health</th>\n",
       "      <th>Finance</th>\n",
       "      <th>Consumer</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Weapons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.011021</td>\n",
       "      <td>1.017561</td>\n",
       "      <td>0.996315</td>\n",
       "      <td>1.001763</td>\n",
       "      <td>0.988372</td>\n",
       "      <td>0.990973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.017345</td>\n",
       "      <td>1.015743</td>\n",
       "      <td>0.998180</td>\n",
       "      <td>1.018728</td>\n",
       "      <td>0.978101</td>\n",
       "      <td>1.000199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.023009</td>\n",
       "      <td>1.007050</td>\n",
       "      <td>0.987742</td>\n",
       "      <td>1.034247</td>\n",
       "      <td>0.978239</td>\n",
       "      <td>1.004482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.024626</td>\n",
       "      <td>1.040404</td>\n",
       "      <td>0.997211</td>\n",
       "      <td>1.053309</td>\n",
       "      <td>0.983311</td>\n",
       "      <td>1.027965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.023928</td>\n",
       "      <td>1.042400</td>\n",
       "      <td>1.007688</td>\n",
       "      <td>1.041599</td>\n",
       "      <td>0.979630</td>\n",
       "      <td>1.027069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Tech    Health   Finance  Consumer    Energy   Weapons\n",
       "0  1.011021  1.017561  0.996315  1.001763  0.988372  0.990973\n",
       "1  1.017345  1.015743  0.998180  1.018728  0.978101  1.000199\n",
       "2  1.023009  1.007050  0.987742  1.034247  0.978239  1.004482\n",
       "3  1.024626  1.040404  0.997211  1.053309  0.983311  1.027965\n",
       "4  1.023928  1.042400  1.007688  1.041599  0.979630  1.027069"
      ]
     },
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     "execution_count": 6,
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     "execution_count": 3,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine all cumulative returns data \n",
    "combined_df = pd.concat([cumulative1_df,cumulative2_df,cumulative3_df,cumulative4_df,cumulative5_df], axis='rows')\n",
    "\n",
    "# Display the combined DataFrame\n",
    "combined_df.head()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 7,
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   "execution_count": 4,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
   "id": "95f1514a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tech    1.117667\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Assuming df is your DataFrame containing the data\n",
    "# Extract the \"Tech\" columns\n",
    "tech_columns = combined_df.filter(like='Tech')\n",
    "\n",
    "# Calculate the average of the \"Tech\" columns\n",
    "tech_average = tech_columns.mean()\n",
    "\n",
    "print(tech_average)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 10,
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   "execution_count": 5,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
   "id": "fdc077aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Health    1.094335\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Assuming df is your DataFrame containing the data\n",
    "# Extract the \"Health\" columns\n",
    "health_columns = combined_df.filter(like='Health')\n",
    "\n",
    "# Calculate the average of the \"Tech\" columns\n",
    "health_average = health_columns.mean()\n",
    "\n",
    "print(health_average)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 11,
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   "execution_count": 6,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
   "id": "bb702931",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finance    1.049386\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Assuming df is your DataFrame containing the data\n",
    "# Extract the \"Finance\" columns\n",
    "finance_columns = combined_df.filter(like='Finance')\n",
    "\n",
    "# Calculate the average of the \"Tech\" columns\n",
    "finance_average = finance_columns.mean()\n",
    "\n",
    "print(finance_average)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 12,
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   "execution_count": 7,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
   "id": "659970dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Consumer    1.034884\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Assuming df is your DataFrame containing the data\n",
    "# Extract the \"Consumer\" columns\n",
    "consumer_columns = combined_df.filter(like='Consumer')\n",
    "\n",
    "# Calculate the average of the \"Tech\" columns\n",
    "consumer_average = consumer_columns.mean()\n",
    "\n",
    "print(consumer_average)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 13,
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   "execution_count": 8,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
   "id": "db4ee294",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy    1.046224\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Assuming df is your DataFrame containing the data\n",
    "# Extract the \"Energy\" columns\n",
    "energy_columns = combined_df.filter(like='Energy')\n",
    "\n",
    "# Calculate the average of the \"Tech\" columns\n",
    "energy_average = energy_columns.mean()\n",
    "\n",
    "print(energy_average)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 14,
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   "execution_count": 9,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
   "id": "9648bde6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weapons    1.087985\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Assuming df is your DataFrame containing the data\n",
    "# Extract the \"Weapons\" columns\n",
    "weapons_columns = combined_df.filter(like='Weapons')\n",
    "\n",
    "# Calculate the average of the \"Tech\" columns\n",
    "weapons_average = weapons_columns.mean()\n",
    "\n",
    "print(weapons_average)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 15,
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   "execution_count": 20,
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   "id": "d37cee0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/Utilities/anaconda3/lib/python3.11/site-packages/numpy/core/fromnumeric.py:59: RuntimeWarning: overflow encountered in accumulate\n",
      "  return bound(*args, **kwds)\n",
      "/Applications/Utilities/anaconda3/lib/python3.11/site-packages/matplotlib/axes/_base.py:2923: RuntimeWarning: overflow encountered in scalar add\n",
      "  x0, x1 = inverse_trans.transform([x0t - delta, x1t + delta])\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cumulative_returns = (1 + combined_df).cumprod()\n",
    "\n",
    "cumulative_returns.plot(figsize=(10, 6))  # Adjust figsize as needed\n",
    "plt.title('Cumulative Returns of Each Industry')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Cumulative Returns')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d348fc2d",
   "metadata": {},
   "source": [
    "# Monte Carlo Simulations"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 16,
=======
   "execution_count": 11,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
   "id": "90a67a54",
   "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",
<<<<<<< HEAD
   "execution_count": 17,
=======
   "execution_count": 12,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
   "id": "34501754",
   "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('2018-10-31', tz='America/New_York').isoformat()\n",
    "end_date = pd.Timestamp('2023-10-31', tz='America/New_York').isoformat()"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 19,
=======
   "execution_count": 13,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
   "id": "cf64fe3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the path to your .env file\n",
    "dotenv_path = 'api.env'\n",
    "\n",
    "# Load .env enviroment variables\n",
    "load_dotenv(dotenv_path)\n",
    "\n",
    "# Load environment variables\n",
    "load_dotenv(dotenv_path)\n",
    "\n",
    "# Access Alpaca API key ID\n",
    "alpaca_api_key_id = os.getenv(\"APCA_API_KEY_ID\")\n",
    "alpaca_secret_key = os.getenv(\"APCA_API_SECRET_KEY\")\n",
    "\n",
    "# Create the Alpaca API object\n",
    "alpaca = tradeapi.REST(\n",
    "    alpaca_api_key_id,\n",
    "    alpaca_secret_key,\n",
    "    api_version=\"v2\")"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 20,
=======
   "execution_count": 23,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
   "id": "612e8319",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get 5 years' worth of historical data for SPY and AGG\n",
    "tickers = [\"AAPL\",\"GOOG\",\"MSFT\",\"JNJ\",\"PFE\",\"UNH\",\"JPM\",\"BAC\",\"V\"]\n",
    "\n",
    "timeframe = \"1Day\"\n",
    "\n",
    "# Get bars data for the tickers\n",
    "df_ticker = alpaca.get_bars(\n",
    "    tickers,\n",
    "    timeframe,\n",
    "    start=start_date,\n",
    "    end=end_date\n",
    ").df"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 21,
=======
   "execution_count": 24,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
   "id": "ff8b0063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"7\" halign=\"left\">AAPL</th>\n",
       "      <th colspan=\"3\" halign=\"left\">GOOG</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"3\" halign=\"left\">BAC</th>\n",
       "      <th colspan=\"7\" halign=\"left\">V</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>trade_count</th>\n",
       "      <th>open</th>\n",
       "      <th>volume</th>\n",
       "      <th>vwap</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>...</th>\n",
       "      <th>open</th>\n",
       "      <th>volume</th>\n",
       "      <th>vwap</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>trade_count</th>\n",
       "      <th>open</th>\n",
       "      <th>volume</th>\n",
       "      <th>vwap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</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",
       "      <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>2018-10-31 04:00:00+00:00</th>\n",
       "      <td>218.86</td>\n",
       "      <td>220.45</td>\n",
       "      <td>216.62</td>\n",
       "      <td>301129</td>\n",
       "      <td>216.88</td>\n",
       "      <td>42444508</td>\n",
       "      <td>218.856231</td>\n",
       "      <td>1076.77</td>\n",
       "      <td>1091.940</td>\n",
       "      <td>1057.00</td>\n",
       "      <td>...</td>\n",
       "      <td>27.06</td>\n",
       "      <td>100829076</td>\n",
       "      <td>27.559997</td>\n",
       "      <td>137.85</td>\n",
       "      <td>139.1200</td>\n",
       "      <td>134.99</td>\n",
       "      <td>112011</td>\n",
       "      <td>134.99</td>\n",
       "      <td>14536840</td>\n",
       "      <td>137.814819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 04:00:00+00:00</th>\n",
       "      <td>222.22</td>\n",
       "      <td>222.36</td>\n",
       "      <td>216.81</td>\n",
       "      <td>505926</td>\n",
       "      <td>219.05</td>\n",
       "      <td>59915448</td>\n",
       "      <td>217.683523</td>\n",
       "      <td>1070.00</td>\n",
       "      <td>1083.975</td>\n",
       "      <td>1062.46</td>\n",
       "      <td>...</td>\n",
       "      <td>27.77</td>\n",
       "      <td>53269150</td>\n",
       "      <td>27.779776</td>\n",
       "      <td>140.83</td>\n",
       "      <td>140.8700</td>\n",
       "      <td>137.49</td>\n",
       "      <td>94137</td>\n",
       "      <td>139.10</td>\n",
       "      <td>19763054</td>\n",
       "      <td>140.303124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02 04:00:00+00:00</th>\n",
       "      <td>207.48</td>\n",
       "      <td>213.65</td>\n",
       "      <td>205.43</td>\n",
       "      <td>756041</td>\n",
       "      <td>209.55</td>\n",
       "      <td>95135063</td>\n",
       "      <td>208.088580</td>\n",
       "      <td>1057.79</td>\n",
       "      <td>1082.975</td>\n",
       "      <td>1054.61</td>\n",
       "      <td>...</td>\n",
       "      <td>28.01</td>\n",
       "      <td>72947599</td>\n",
       "      <td>27.868507</td>\n",
       "      <td>139.78</td>\n",
       "      <td>141.4546</td>\n",
       "      <td>137.85</td>\n",
       "      <td>91637</td>\n",
       "      <td>141.11</td>\n",
       "      <td>22543939</td>\n",
       "      <td>139.629106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05 05:00:00+00:00</th>\n",
       "      <td>201.59</td>\n",
       "      <td>204.39</td>\n",
       "      <td>198.17</td>\n",
       "      <td>569649</td>\n",
       "      <td>204.26</td>\n",
       "      <td>68848036</td>\n",
       "      <td>200.964339</td>\n",
       "      <td>1040.09</td>\n",
       "      <td>1058.470</td>\n",
       "      <td>1021.24</td>\n",
       "      <td>...</td>\n",
       "      <td>27.95</td>\n",
       "      <td>46601234</td>\n",
       "      <td>28.072948</td>\n",
       "      <td>139.80</td>\n",
       "      <td>140.2100</td>\n",
       "      <td>138.07</td>\n",
       "      <td>63041</td>\n",
       "      <td>139.80</td>\n",
       "      <td>6828165</td>\n",
       "      <td>139.367560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-06 05:00:00+00:00</th>\n",
       "      <td>203.77</td>\n",
       "      <td>204.72</td>\n",
       "      <td>201.69</td>\n",
       "      <td>253890</td>\n",
       "      <td>201.92</td>\n",
       "      <td>34030512</td>\n",
       "      <td>203.264572</td>\n",
       "      <td>1055.81</td>\n",
       "      <td>1064.345</td>\n",
       "      <td>1038.07</td>\n",
       "      <td>...</td>\n",
       "      <td>28.02</td>\n",
       "      <td>48358851</td>\n",
       "      <td>28.089798</td>\n",
       "      <td>140.79</td>\n",
       "      <td>140.9100</td>\n",
       "      <td>139.72</td>\n",
       "      <td>49355</td>\n",
       "      <td>139.80</td>\n",
       "      <td>5152556</td>\n",
       "      <td>140.463290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             AAPL                                      \\\n",
       "                            close    high     low trade_count    open   \n",
       "timestamp                                                               \n",
       "2018-10-31 04:00:00+00:00  218.86  220.45  216.62      301129  216.88   \n",
       "2018-11-01 04:00:00+00:00  222.22  222.36  216.81      505926  219.05   \n",
       "2018-11-02 04:00:00+00:00  207.48  213.65  205.43      756041  209.55   \n",
       "2018-11-05 05:00:00+00:00  201.59  204.39  198.17      569649  204.26   \n",
       "2018-11-06 05:00:00+00:00  203.77  204.72  201.69      253890  201.92   \n",
       "\n",
       "                                                    GOOG                     \\\n",
       "                             volume        vwap    close      high      low   \n",
       "timestamp                                                                     \n",
       "2018-10-31 04:00:00+00:00  42444508  218.856231  1076.77  1091.940  1057.00   \n",
       "2018-11-01 04:00:00+00:00  59915448  217.683523  1070.00  1083.975  1062.46   \n",
       "2018-11-02 04:00:00+00:00  95135063  208.088580  1057.79  1082.975  1054.61   \n",
       "2018-11-05 05:00:00+00:00  68848036  200.964339  1040.09  1058.470  1021.24   \n",
       "2018-11-06 05:00:00+00:00  34030512  203.264572  1055.81  1064.345  1038.07   \n",
       "\n",
       "                           ...    BAC                             V            \\\n",
       "                           ...   open     volume       vwap   close      high   \n",
       "timestamp                  ...                                                  \n",
       "2018-10-31 04:00:00+00:00  ...  27.06  100829076  27.559997  137.85  139.1200   \n",
       "2018-11-01 04:00:00+00:00  ...  27.77   53269150  27.779776  140.83  140.8700   \n",
       "2018-11-02 04:00:00+00:00  ...  28.01   72947599  27.868507  139.78  141.4546   \n",
       "2018-11-05 05:00:00+00:00  ...  27.95   46601234  28.072948  139.80  140.2100   \n",
       "2018-11-06 05:00:00+00:00  ...  28.02   48358851  28.089798  140.79  140.9100   \n",
       "\n",
       "                                                                             \n",
       "                              low trade_count    open    volume        vwap  \n",
       "timestamp                                                                    \n",
       "2018-10-31 04:00:00+00:00  134.99      112011  134.99  14536840  137.814819  \n",
       "2018-11-01 04:00:00+00:00  137.49       94137  139.10  19763054  140.303124  \n",
       "2018-11-02 04:00:00+00:00  137.85       91637  141.11  22543939  139.629106  \n",
       "2018-11-05 05:00:00+00:00  138.07       63041  139.80   6828165  139.367560  \n",
       "2018-11-06 05:00:00+00:00  139.72       49355  139.80   5152556  140.463290  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
<<<<<<< HEAD
     "execution_count": 21,
=======
     "execution_count": 24,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reorganize the DataFrame\n",
    "AAPL = df_ticker[df_ticker['symbol']=='AAPL'].drop('symbol', axis=1)\n",
    "GOOG = df_ticker[df_ticker['symbol']=='GOOG'].drop('symbol', axis=1)\n",
    "MSFT = df_ticker[df_ticker['symbol']=='MSFT'].drop('symbol', axis=1)\n",
    "JNJ = df_ticker[df_ticker['symbol']=='JNJ'].drop('symbol', axis=1)\n",
    "PFE = df_ticker[df_ticker['symbol']=='PFE'].drop('symbol', axis=1)\n",
    "UNH = df_ticker[df_ticker['symbol']=='UNH'].drop('symbol', axis=1)\n",
    "JPM = df_ticker[df_ticker['symbol']=='JPM'].drop('symbol', axis=1)\n",
    "BAC = df_ticker[df_ticker['symbol']=='BAC'].drop('symbol', axis=1)\n",
    "V = df_ticker[df_ticker['symbol']=='V'].drop('symbol', axis=1)\n",
    "\n",
    "# Concatenate the ticker DataFrames\n",
    "df_ticker = pd.concat([AAPL,GOOG,MSFT,JNJ,PFE,UNH,JPM,BAC,V], axis=1, keys=[\"AAPL\",\"GOOG\",\"MSFT\",\"JNJ\",\"PFE\",\"UNH\",\"JPM\",\"BAC\",\"V\"])\n",
    "\n",
    "# Display sample data\n",
    "df_ticker.head()"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 22,
=======
   "execution_count": 25,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
   "id": "bf806ae3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>JNJ</th>\n",
       "      <th>PFE</th>\n",
       "      <th>UNH</th>\n",
       "      <th>JPM</th>\n",
       "      <th>BAC</th>\n",
       "      <th>V</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-31</th>\n",
       "      <td>218.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>106.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>222.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02</th>\n",
       "      <td>207.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>106.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05</th>\n",
       "      <td>201.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-06</th>\n",
       "      <td>203.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-25</th>\n",
       "      <td>171.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>340.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-26</th>\n",
       "      <td>166.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>327.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-27</th>\n",
       "      <td>168.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>329.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-30</th>\n",
       "      <td>170.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>337.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-31</th>\n",
       "      <td>170.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>338.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1258 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              AAPL  GOOG    MSFT  JNJ  PFE  UNH  JPM  BAC   V\n",
       "2018-10-31  218.86   NaN  106.81  NaN  NaN  NaN  NaN  NaN NaN\n",
       "2018-11-01  222.22   NaN  105.92  NaN  NaN  NaN  NaN  NaN NaN\n",
       "2018-11-02  207.48   NaN  106.16  NaN  NaN  NaN  NaN  NaN NaN\n",
       "2018-11-05  201.59   NaN  107.51  NaN  NaN  NaN  NaN  NaN NaN\n",
       "2018-11-06  203.77   NaN  107.72  NaN  NaN  NaN  NaN  NaN NaN\n",
       "...            ...   ...     ...  ...  ...  ...  ...  ...  ..\n",
       "2023-10-25  171.10   NaN  340.67  NaN  NaN  NaN  NaN  NaN NaN\n",
       "2023-10-26  166.89   NaN  327.89  NaN  NaN  NaN  NaN  NaN NaN\n",
       "2023-10-27  168.22   NaN  329.81  NaN  NaN  NaN  NaN  NaN NaN\n",
       "2023-10-30  170.29   NaN  337.31  NaN  NaN  NaN  NaN  NaN NaN\n",
       "2023-10-31  170.77   NaN  338.11  NaN  NaN  NaN  NaN  NaN NaN\n",
       "\n",
       "[1258 rows x 9 columns]"
      ]
     },
<<<<<<< HEAD
     "execution_count": 22,
=======
     "execution_count": 25,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and empty DataFrame for closing prices\n",
    "df_closing_prices = pd.DataFrame()\n",
    "\n",
    "# Fetch the closing prices of KO and TSLA\n",
    "df_closing_prices[\"AAPL\"] = df_ticker[\"AAPL\"][\"close\"]\n",
    "df_closing_prices[\"GOOG\"] = df_ticker[\"GOOG\"][\"close\"]\n",
    "df_closing_prices[\"MSFT\"] = df_ticker[\"MSFT\"][\"close\"]\n",
    "df_closing_prices[\"JNJ\"] = df_ticker[\"JNJ\"][\"close\"]\n",
    "df_closing_prices[\"PFE\"] = df_ticker[\"PFE\"][\"close\"]\n",
    "df_closing_prices[\"UNH\"] = df_ticker[\"UNH\"][\"close\"]\n",
    "df_closing_prices[\"JPM\"] = df_ticker[\"JPM\"][\"close\"]\n",
    "df_closing_prices[\"BAC\"] = df_ticker[\"BAC\"][\"close\"]\n",
    "df_closing_prices[\"V\"] = df_ticker[\"V\"][\"close\"]\n",
    "\n",
    "# Check for NaN values in the 'V' column\n",
    "# You need to explain \n",
    "v_nan_indices = df_closing_prices['V'].isna()\n",
    "average_V = df_closing_prices['V'].mean()\n",
    "df_closing_prices['V'] = df_closing_prices['V'].fillna(average_V)\n",
    "\n",
    "# Drop the time component of the date\n",
    "df_closing_prices.index = df_closing_prices.index.date\n",
    "\n",
    "# Ensure the index is in datetime format\n",
    "df_closing_prices.index = pd.to_datetime(df_closing_prices.index)\n",
    "\n",
    "df_closing_prices "
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 23,
=======
   "execution_count": 26,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
   "id": "cd08a77e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:62: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  close_df = portfolio_data.xs('close',level=1,axis=1).pct_change()\n"
     ]
    }
   ],
   "source": [
    "# Configuring a Monte Carlo simulation to forecast 30 years cumulative returns\n",
    "# Weights for each stock are assumed to be equal \n",
    "MC_two_year = MCSimulation(\n",
    "    portfolio_data = df_ticker,\n",
    "    num_simulation = 200,\n",
    "    num_trading_days = 252*2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 24,
=======
   "execution_count": 27,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
   "id": "8395133a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">AAPL</th>\n",
       "      <th colspan=\"2\" halign=\"left\">GOOG</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">BAC</th>\n",
       "      <th colspan=\"8\" halign=\"left\">V</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>trade_count</th>\n",
       "      <th>open</th>\n",
       "      <th>volume</th>\n",
       "      <th>vwap</th>\n",
       "      <th>daily_return</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>...</th>\n",
       "      <th>vwap</th>\n",
       "      <th>daily_return</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>trade_count</th>\n",
       "      <th>open</th>\n",
       "      <th>volume</th>\n",
       "      <th>vwap</th>\n",
       "      <th>daily_return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</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",
       "      <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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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-31 04:00:00+00:00</th>\n",
       "      <td>218.86</td>\n",
       "      <td>220.45</td>\n",
       "      <td>216.62</td>\n",
       "      <td>301129</td>\n",
       "      <td>216.88</td>\n",
       "      <td>42444508</td>\n",
       "      <td>218.856231</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 04:00:00+00:00</th>\n",
       "      <td>222.22</td>\n",
       "      <td>222.36</td>\n",
       "      <td>216.81</td>\n",
       "      <td>505926</td>\n",
       "      <td>219.05</td>\n",
       "      <td>59915448</td>\n",
       "      <td>217.683523</td>\n",
       "      <td>0.015352</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02 04:00:00+00:00</th>\n",
       "      <td>207.48</td>\n",
       "      <td>213.65</td>\n",
       "      <td>205.43</td>\n",
       "      <td>756041</td>\n",
       "      <td>209.55</td>\n",
       "      <td>95135063</td>\n",
       "      <td>208.088580</td>\n",
       "      <td>-0.066331</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05 05:00:00+00:00</th>\n",
       "      <td>201.59</td>\n",
       "      <td>204.39</td>\n",
       "      <td>198.17</td>\n",
       "      <td>569649</td>\n",
       "      <td>204.26</td>\n",
       "      <td>68848036</td>\n",
       "      <td>200.964339</td>\n",
       "      <td>-0.028388</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-06 05:00:00+00:00</th>\n",
       "      <td>203.77</td>\n",
       "      <td>204.72</td>\n",
       "      <td>201.69</td>\n",
       "      <td>253890</td>\n",
       "      <td>201.92</td>\n",
       "      <td>34030512</td>\n",
       "      <td>203.264572</td>\n",
       "      <td>0.010814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             AAPL                                      \\\n",
       "                            close    high     low trade_count    open   \n",
       "timestamp                                                               \n",
       "2018-10-31 04:00:00+00:00  218.86  220.45  216.62      301129  216.88   \n",
       "2018-11-01 04:00:00+00:00  222.22  222.36  216.81      505926  219.05   \n",
       "2018-11-02 04:00:00+00:00  207.48  213.65  205.43      756041  209.55   \n",
       "2018-11-05 05:00:00+00:00  201.59  204.39  198.17      569649  204.26   \n",
       "2018-11-06 05:00:00+00:00  203.77  204.72  201.69      253890  201.92   \n",
       "\n",
       "                                                              GOOG       ...  \\\n",
       "                             volume        vwap daily_return close high  ...   \n",
       "timestamp                                                                ...   \n",
       "2018-10-31 04:00:00+00:00  42444508  218.856231          NaN   NaN  NaN  ...   \n",
       "2018-11-01 04:00:00+00:00  59915448  217.683523     0.015352   NaN  NaN  ...   \n",
       "2018-11-02 04:00:00+00:00  95135063  208.088580    -0.066331   NaN  NaN  ...   \n",
       "2018-11-05 05:00:00+00:00  68848036  200.964339    -0.028388   NaN  NaN  ...   \n",
       "2018-11-06 05:00:00+00:00  34030512  203.264572     0.010814   NaN  NaN  ...   \n",
       "\n",
       "                           BAC                  V                            \\\n",
       "                          vwap daily_return close high low trade_count open   \n",
       "timestamp                                                                     \n",
       "2018-10-31 04:00:00+00:00  NaN          NaN   NaN  NaN NaN         NaN  NaN   \n",
       "2018-11-01 04:00:00+00:00  NaN          NaN   NaN  NaN NaN         NaN  NaN   \n",
       "2018-11-02 04:00:00+00:00  NaN          NaN   NaN  NaN NaN         NaN  NaN   \n",
       "2018-11-05 05:00:00+00:00  NaN          NaN   NaN  NaN NaN         NaN  NaN   \n",
       "2018-11-06 05:00:00+00:00  NaN          NaN   NaN  NaN NaN         NaN  NaN   \n",
       "\n",
       "                                                    \n",
       "                          volume vwap daily_return  \n",
       "timestamp                                           \n",
       "2018-10-31 04:00:00+00:00    NaN  NaN          NaN  \n",
       "2018-11-01 04:00:00+00:00    NaN  NaN          NaN  \n",
       "2018-11-02 04:00:00+00:00    NaN  NaN          NaN  \n",
       "2018-11-05 05:00:00+00:00    NaN  NaN          NaN  \n",
       "2018-11-06 05:00:00+00:00    NaN  NaN          NaN  \n",
       "\n",
       "[5 rows x 72 columns]"
      ]
     },
<<<<<<< HEAD
     "execution_count": 24,
=======
     "execution_count": 27,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the simulation input data\n",
    "MC_two_year.portfolio_data.head()"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 27,
=======
   "execution_count": 29,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
   "id": "a742e8ee",
   "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Monte Carlo simulation number 140.\n",
      "Running Monte Carlo simulation number 150.\n",
      "Running Monte Carlo simulation number 160.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Monte Carlo simulation number 170.\n",
      "Running Monte Carlo simulation number 180.\n",
      "Running Monte Carlo simulation number 190.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n",
      "/Users/helobonetti/Desktop/BERKELEY/Project1/MCForecastTools.py:112: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  sim_df = pd.DataFrame(simvals).T.pct_change()\n"
     ]
    },
    {
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>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
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<<<<<<< HEAD
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>503</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
=======
       "      <td>0.886732</td>\n",
       "      <td>1.679638</td>\n",
       "      <td>1.166660</td>\n",
       "      <td>1.006832</td>\n",
       "      <td>1.016375</td>\n",
       "      <td>0.987341</td>\n",
       "      <td>1.183922</td>\n",
       "      <td>1.102869</td>\n",
       "      <td>1.153382</td>\n",
       "      <td>0.896434</td>\n",
       "      <td>...</td>\n",
       "      <td>1.421366</td>\n",
       "      <td>1.127740</td>\n",
       "      <td>1.191717</td>\n",
       "      <td>0.877947</td>\n",
       "      <td>1.079689</td>\n",
       "      <td>0.925252</td>\n",
       "      <td>1.343200</td>\n",
       "      <td>1.006744</td>\n",
       "      <td>1.212978</td>\n",
       "      <td>1.573391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.898394</td>\n",
       "      <td>1.687032</td>\n",
       "      <td>1.181884</td>\n",
       "      <td>1.008059</td>\n",
       "      <td>1.023527</td>\n",
       "      <td>0.988022</td>\n",
       "      <td>1.185058</td>\n",
       "      <td>1.103156</td>\n",
       "      <td>1.154979</td>\n",
       "      <td>0.891630</td>\n",
       "      <td>...</td>\n",
       "      <td>1.440639</td>\n",
       "      <td>1.131643</td>\n",
       "      <td>1.196165</td>\n",
       "      <td>0.873729</td>\n",
       "      <td>1.083432</td>\n",
       "      <td>0.921470</td>\n",
       "      <td>1.344605</td>\n",
       "      <td>1.004977</td>\n",
       "      <td>1.217797</td>\n",
       "      <td>1.600657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.893378</td>\n",
       "      <td>1.693354</td>\n",
       "      <td>1.168935</td>\n",
       "      <td>1.018585</td>\n",
       "      <td>1.029434</td>\n",
       "      <td>0.988132</td>\n",
       "      <td>1.188392</td>\n",
       "      <td>1.096853</td>\n",
       "      <td>1.150118</td>\n",
       "      <td>0.898636</td>\n",
       "      <td>...</td>\n",
       "      <td>1.451660</td>\n",
       "      <td>1.136073</td>\n",
       "      <td>1.193471</td>\n",
       "      <td>0.864856</td>\n",
       "      <td>1.085222</td>\n",
       "      <td>0.918749</td>\n",
       "      <td>1.347689</td>\n",
       "      <td>1.003441</td>\n",
       "      <td>1.223677</td>\n",
       "      <td>1.602366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.896605</td>\n",
       "      <td>1.703515</td>\n",
       "      <td>1.158723</td>\n",
       "      <td>1.008121</td>\n",
       "      <td>1.025209</td>\n",
       "      <td>0.985524</td>\n",
       "      <td>1.193705</td>\n",
       "      <td>1.092067</td>\n",
       "      <td>1.149015</td>\n",
       "      <td>0.911669</td>\n",
       "      <td>...</td>\n",
       "      <td>1.460318</td>\n",
       "      <td>1.130738</td>\n",
       "      <td>1.193291</td>\n",
       "      <td>0.863900</td>\n",
       "      <td>1.089010</td>\n",
       "      <td>0.917883</td>\n",
       "      <td>1.362957</td>\n",
       "      <td>1.019788</td>\n",
       "      <td>1.208917</td>\n",
       "      <td>1.612604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.889186</td>\n",
       "      <td>1.700332</td>\n",
       "      <td>1.155935</td>\n",
       "      <td>1.011615</td>\n",
       "      <td>1.023715</td>\n",
       "      <td>0.979388</td>\n",
       "      <td>1.206500</td>\n",
       "      <td>1.080219</td>\n",
       "      <td>1.146698</td>\n",
       "      <td>0.903494</td>\n",
       "      <td>...</td>\n",
       "      <td>1.481003</td>\n",
       "      <td>1.138648</td>\n",
       "      <td>1.180096</td>\n",
       "      <td>0.870842</td>\n",
       "      <td>1.076846</td>\n",
       "      <td>0.914983</td>\n",
       "      <td>1.350517</td>\n",
       "      <td>1.029620</td>\n",
       "      <td>1.211281</td>\n",
       "      <td>1.580210</td>\n",
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>505 rows × 200 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
<<<<<<< HEAD
       "     0    1    2    3    4    5    6    7    8    9    ...  190  191  192  \\\n",
       "0    1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
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       "2    1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "3    1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "4    1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "500  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "501  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "502  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "503  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "504  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "\n",
       "     193  194  195  196  197  198  199  \n",
       "0    1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "1    1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "2    1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "3    1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "4    1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "..   ...  ...  ...  ...  ...  ...  ...  \n",
       "500  1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "501  1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "502  1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "503  1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "504  1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
=======
       "          0         1         2         3         4         5         6    \\\n",
       "0    1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "1    0.995344  1.004087  1.007346  0.993256  0.994928  1.013597  1.011026   \n",
       "2    0.991443  0.993118  1.015309  0.986071  0.989820  1.014901  1.005160   \n",
       "3    0.991478  1.002402  1.024902  0.982340  0.989770  1.017546  1.014960   \n",
       "4    0.993548  1.007465  1.034519  0.975800  0.975772  1.010108  1.013008   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "500  0.886732  1.679638  1.166660  1.006832  1.016375  0.987341  1.183922   \n",
       "501  0.898394  1.687032  1.181884  1.008059  1.023527  0.988022  1.185058   \n",
       "502  0.893378  1.693354  1.168935  1.018585  1.029434  0.988132  1.188392   \n",
       "503  0.896605  1.703515  1.158723  1.008121  1.025209  0.985524  1.193705   \n",
       "504  0.889186  1.700332  1.155935  1.011615  1.023715  0.979388  1.206500   \n",
       "\n",
       "          7         8         9    ...       190       191       192  \\\n",
       "0    1.000000  1.000000  1.000000  ...  1.000000  1.000000  1.000000   \n",
       "1    0.996631  1.015872  1.000179  ...  0.997949  0.995938  1.020818   \n",
       "2    0.981569  1.023676  0.998225  ...  0.996464  0.986169  1.014042   \n",
       "3    0.975979  1.020665  0.995636  ...  0.987248  0.984781  1.018519   \n",
       "4    0.981270  1.015509  0.997873  ...  0.998337  0.991809  1.024099   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "500  1.102869  1.153382  0.896434  ...  1.421366  1.127740  1.191717   \n",
       "501  1.103156  1.154979  0.891630  ...  1.440639  1.131643  1.196165   \n",
       "502  1.096853  1.150118  0.898636  ...  1.451660  1.136073  1.193471   \n",
       "503  1.092067  1.149015  0.911669  ...  1.460318  1.130738  1.193291   \n",
       "504  1.080219  1.146698  0.903494  ...  1.481003  1.138648  1.180096   \n",
       "\n",
       "          193       194       195       196       197       198       199  \n",
       "0    1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  \n",
       "1    1.007222  1.000654  1.011560  0.994137  1.009097  0.995309  1.006666  \n",
       "2    0.998561  0.993650  1.027144  0.999749  1.010339  0.995901  1.005873  \n",
       "3    0.999044  0.988150  1.034201  1.000595  1.020190  1.001124  1.014927  \n",
       "4    1.000172  0.983766  1.038951  0.997918  1.019427  0.987781  1.010672  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "500  0.877947  1.079689  0.925252  1.343200  1.006744  1.212978  1.573391  \n",
       "501  0.873729  1.083432  0.921470  1.344605  1.004977  1.217797  1.600657  \n",
       "502  0.864856  1.085222  0.918749  1.347689  1.003441  1.223677  1.602366  \n",
       "503  0.863900  1.089010  0.917883  1.362957  1.019788  1.208917  1.612604  \n",
       "504  0.870842  1.076846  0.914983  1.350517  1.029620  1.211281  1.580210  \n",
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
       "\n",
       "[505 rows x 200 columns]"
      ]
     },
<<<<<<< HEAD
     "execution_count": 27,
=======
     "execution_count": 29,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
<<<<<<< HEAD
    "# Running a Monte Carlo simulation to forecast 2 years cumulative returns\n",
=======
    "# Running a Monte Carlo simulation to forecast 30 years cumulative returns\n",
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
    "MC_two_year.calc_cumulative_return()"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 26,
=======
   "execution_count": 32,
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
   "id": "a2dbceab",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'MC_thirty_year' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[26], line 7\u001b[0m\n\u001b[1;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# Get the matplotlib Axes object\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m ax \u001b[38;5;241m=\u001b[39m MC_thirty_year\u001b[38;5;241m.\u001b[39mplot_simulation()\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# Customize the plot\u001b[39;00m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m ax\u001b[38;5;241m.\u001b[39mlines:\n",
      "\u001b[0;31mNameError\u001b[0m: name 'MC_thirty_year' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
<<<<<<< HEAD
=======
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set the figure size\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Get the matplotlib Axes object\n",
    "ax = MC_two_year.plot_simulation()\n",
    "\n",
    "# Customize the plot\n",
    "for line in ax.lines:\n",
    "    line.set_linewidth(0.5)  # Adjust line width\n",
    "\n",
    "# Set title and labels\n",
    "plt.title('Monte Carlo Simulation of Two Year Cumulative Returns', fontsize=16)\n",
    "plt.xlabel('Trading Days', fontsize=14)\n",
    "plt.ylabel('Portfolio Value', fontsize=14)\n",
    "\n",
    "# Add grid\n",
    "plt.grid(True)\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
<<<<<<< HEAD
   "id": "f89fef08-1cd3-4177-a8de-70af33454bfe",
=======
   "id": "c8e27976",
>>>>>>> 8a058b6915d81497dedcda9fed477836aab6710a
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}