Python for Finance 10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Provides ways to work with large multidimensional arrays\n",
    "import numpy as np \n",
    "# Allows for further data manipulation and analysis\n",
    "import pandas as pd\n",
    "from pandas_datareader import data as web # Reads stock data \n",
    "import matplotlib.pyplot as plt # Plotting\n",
    "import matplotlib.dates as mdates # Styling dates\n",
    "%matplotlib inline\n",
    "\n",
    "import datetime as dt # For defining dates\n",
    "import mplfinance as mpf # Matplotlib finance\n",
    "\n",
    "import time\n",
    "\n",
    "# Used to get data from a directory\n",
    "import os\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "\n",
    "#Statsmodels is a great library we can use to run regressions.\n",
    "import statsmodels.api as sm\n",
    "# Seaborn extends the capabilities of Matplotlib\n",
    "import seaborn as sns\n",
    "# Used for calculating regressions\n",
    "from statsmodels.tsa.ar_model import AutoReg, ar_select_order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dates & Other Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define path to files\n",
    "# For MacOS\n",
    "PATH = \"/Users/derekbanas/Documents/Tutorials/Python for Finance/Stocks/\"\n",
    "# For Windows\n",
    "# PATH = \"D:/Python for Finance/Stocks/\"\n",
    "\n",
    "# Start date defaults\n",
    "S_YEAR = 2017\n",
    "S_MONTH = 1\n",
    "S_DAY = 3\n",
    "S_DATE_STR = \"2017-01-03\"\n",
    "S_DATE_DATETIME = dt.datetime(S_YEAR, S_MONTH, S_DAY)\n",
    "\n",
    "# End date defaults\n",
    "E_YEAR = 2021\n",
    "E_MONTH = 8\n",
    "E_DAY = 19\n",
    "E_DATE_STR = \"2021-08-19\"\n",
    "E_DATE_DATETIME = dt.datetime(E_YEAR, E_MONTH, E_DAY)\n",
    "\n",
    "risk_free_rate = 0.0125 # Approximate 10 year bond rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function that Saves Dataframe to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_dataframe_to_csv(df, ticker):\n",
    "    df.to_csv(PATH + ticker + '.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function that Returns a Dataframe from a CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_df_from_csv(ticker):\n",
    "    try:\n",
    "        df = pd.read_csv(PATH + ticker + '.csv', index_col='Date', \n",
    "                         parse_dates=True)\n",
    "    except FileNotFoundError:\n",
    "        pass\n",
    "        # print(\"File Doesn't Exist\")\n",
    "    else:\n",
    "        return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add Daily Return to Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shift provides the value from the previous day\n",
    "# NaN is displayed because there was no previous day price for the 1st calculation\n",
    "def add_daily_return_to_df(df, ticker):\n",
    "    df['daily_return'] = (df['Adj Close'] / df['Adj Close'].shift(1)) - 1\n",
    "    # Save data to a CSV file\n",
    "    save_dataframe_to_csv(df, ticker)\n",
    "    return df  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge Multiple Stocks in One Dataframe by Column Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge_df_by_column_name(col_name, sdate, edate, *tickers):\n",
    "    # Will hold data for all dataframes with the same column name\n",
    "    mult_df = pd.DataFrame()\n",
    "    \n",
    "    for x in tickers:\n",
    "        df = get_df_from_csv(x)\n",
    "        \n",
    "        # NEW Check if your dataframe has duplicate indexes\n",
    "        if not df.index.is_unique:\n",
    "            # Delete duplicates \n",
    "            df = df.loc[~df.index.duplicated(), :]\n",
    "        \n",
    "        mask = (df.index >= sdate) & (df.index <= edate)\n",
    "        mult_df[x] = df.loc[mask][col_name]\n",
    "        \n",
    "    return mult_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMD</th>\n",
       "      <th>CPRT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>0.068093</td>\n",
       "      <td>0.009493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>0.051913</td>\n",
       "      <td>-0.004816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>0.049351</td>\n",
       "      <td>0.008066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>-0.019802</td>\n",
       "      <td>-0.004801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>0.033670</td>\n",
       "      <td>0.000459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-03</th>\n",
       "      <td>0.006593</td>\n",
       "      <td>0.003929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-07</th>\n",
       "      <td>-0.007005</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-08</th>\n",
       "      <td>-0.027302</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-09</th>\n",
       "      <td>-0.000188</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-10</th>\n",
       "      <td>-0.008950</td>\n",
       "      <td>-0.016480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>930 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AMD      CPRT\n",
       "Date                          \n",
       "2018-01-02  0.068093  0.009493\n",
       "2018-01-03  0.051913 -0.004816\n",
       "2018-01-04  0.049351  0.008066\n",
       "2018-01-05 -0.019802 -0.004801\n",
       "2018-01-08  0.033670  0.000459\n",
       "...              ...       ...\n",
       "2021-09-03  0.006593  0.003929\n",
       "2021-09-07 -0.007005       NaN\n",
       "2021-09-08 -0.027302       NaN\n",
       "2021-09-09 -0.000188       NaN\n",
       "2021-09-10 -0.008950 -0.016480\n",
       "\n",
       "[930 rows x 2 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "port_list = [\"AMD\", \"CPRT\"]\n",
    "mult_df = merge_df_by_column_name('daily_return',  '2018-01-02', \n",
    "                                  '2021-09-10', *port_list)\n",
    "mult_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating Beta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beta provides the relationship between an investment and the overall market. Risky investments tend to fall further during bad times, but will increase quicker during good times. \n",
    "\n",
    "Beta is found by dividing the covariance of the stock and the market by the variance of the overall market. It is a measure of systematic risk that can't be diversified away.  \n",
    "\n",
    "$ \\beta = \\frac{Cov(r_x, r_m)}{\\sigma_m^2} $\n",
    "\n",
    "$ \\beta = 0 $ : No relation to market\n",
    "\n",
    "$ \\beta < 1 $ : Less risky than market\n",
    "\n",
    "$ \\beta > 1 $ : More risky than the market"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Albertsons is a grocery store chain with a low beta of 0.5 because no matter what people need food and pharmecueticals. \n",
    "\n",
    "AMD manufacturers microchips and is a high beta stock at 1.4 because during hard times there is less demand for their products."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get S&P 500 and AMD Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>daily_return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>11.430000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>11.430000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>11.240000</td>\n",
       "      <td>-0.016623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>11.320000</td>\n",
       "      <td>0.007117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>11.490000</td>\n",
       "      <td>0.015018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-03</th>\n",
       "      <td>109.919998</td>\n",
       "      <td>0.006593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-07</th>\n",
       "      <td>109.150002</td>\n",
       "      <td>-0.007005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-08</th>\n",
       "      <td>106.169998</td>\n",
       "      <td>-0.027302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-09</th>\n",
       "      <td>106.150002</td>\n",
       "      <td>-0.000188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-10</th>\n",
       "      <td>105.199997</td>\n",
       "      <td>-0.008950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1181 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Adj Close  daily_return\n",
       "Date                                \n",
       "2017-01-03   11.430000           NaN\n",
       "2017-01-04   11.430000      0.000000\n",
       "2017-01-05   11.240000     -0.016623\n",
       "2017-01-06   11.320000      0.007117\n",
       "2017-01-09   11.490000      0.015018\n",
       "...                ...           ...\n",
       "2021-09-03  109.919998      0.006593\n",
       "2021-09-07  109.150002     -0.007005\n",
       "2021-09-08  106.169998     -0.027302\n",
       "2021-09-09  106.150002     -0.000188\n",
       "2021-09-10  105.199997     -0.008950\n",
       "\n",
       "[1181 rows x 2 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Will hold S&P 500 adjusted close data\n",
    "# sp_df = pd.DataFrame()\n",
    "\n",
    "# # Download data from Yahoo\n",
    "# sp_df = web.DataReader('GSPC', 'yahoo', '2017-1-3', '2021-9-10')['Adj Close']\n",
    "\n",
    "amd_df = get_df_from_csv('AMD')\n",
    "\n",
    "amd_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>daily_return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>2257.830078</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>2270.750000</td>\n",
       "      <td>0.005722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>2269.000000</td>\n",
       "      <td>-0.000771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>2276.979980</td>\n",
       "      <td>0.003517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>2268.899902</td>\n",
       "      <td>-0.003549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-03</th>\n",
       "      <td>4535.430176</td>\n",
       "      <td>-0.000335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-07</th>\n",
       "      <td>4520.029785</td>\n",
       "      <td>-0.003396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-08</th>\n",
       "      <td>4514.069824</td>\n",
       "      <td>-0.001319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-09</th>\n",
       "      <td>4493.279785</td>\n",
       "      <td>-0.004606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-10</th>\n",
       "      <td>4458.580078</td>\n",
       "      <td>-0.007723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1181 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Adj Close  daily_return\n",
       "Date                                 \n",
       "2017-01-03  2257.830078           NaN\n",
       "2017-01-04  2270.750000      0.005722\n",
       "2017-01-05  2269.000000     -0.000771\n",
       "2017-01-06  2276.979980      0.003517\n",
       "2017-01-09  2268.899902     -0.003549\n",
       "...                 ...           ...\n",
       "2021-09-03  4535.430176     -0.000335\n",
       "2021-09-07  4520.029785     -0.003396\n",
       "2021-09-08  4514.069824     -0.001319\n",
       "2021-09-09  4493.279785     -0.004606\n",
       "2021-09-10  4458.580078     -0.007723\n",
       "\n",
       "[1181 rows x 2 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp_df = get_df_from_csv('GSPC')\n",
    "\n",
    "sp_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add Daily Return & Save to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Save S&P to csv\n",
    "# save_dataframe_to_csv(sp_df, 'GSPC')\n",
    "\n",
    "# # Get dataframe from csv\n",
    "# sp_df2 = get_df_from_csv('GSPC')\n",
    "\n",
    "# # Add daily return to dataframe\n",
    "# add_daily_return_to_df(sp_df2, 'GSPC')\n",
    "# sp_df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find Beta for Stock versus S&P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_beta(ticker):\n",
    "    # Tickers analyzed being the S&P and the stock passed\n",
    "    port_list =['GSPC']\n",
    "    port_list.append(ticker)\n",
    "\n",
    "    mult_df = merge_df_by_column_name('daily_return',  '2018-01-02', \n",
    "                                  '2021-09-10', *port_list)\n",
    "    \n",
    "    # Provides the covariance between the securities\n",
    "    cov = mult_df.cov() * 252\n",
    "    \n",
    "    # Get the covariance of the stock and the market\n",
    "    cov_vs_market = cov.iloc[0,1]\n",
    "    \n",
    "    # Get annualized variance of the S&P\n",
    "    sp_var = mult_df['GSPC'].var() * 252\n",
    "    \n",
    "    # Beta is normally calculated over a 5 year period which is why you may see a difference\n",
    "    beta = cov_vs_market / sp_var\n",
    "    return beta\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Stock Beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AMD Beta : 1.4094430275300194\n"
     ]
    }
   ],
   "source": [
    "print(\"AMD Beta :\", find_beta('AMD'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Capital Asset Pricing Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sharpe continued to create the CAPM based on the research of Markowitz. It focuses on investments in stocks and bonds. With it we can more exactly create portfolios that match the risk an investor is willing to assume. CAPM assumes a risk free asset which of course provides a small return. So if the investor wants less risk they simply buy more of the risk free assets.\n",
    "\n",
    "There is risk that you can limit through diversifaction (Idiosyncratic) and risk that you can't (Systematic). This portfolio contains no Idiosyncratic risk and like before it lies on the efficient frontier.\n",
    "\n",
    "To find this portfolio we will draw a line ( The Capital Market Line ) from the Y intercept to the efficient frontier. \n",
    "\n",
    "Here is the formula. The securities expected return equals the risk free asset plus Beta times the market return minus the risk free asset. it is common for $ r_m - r_f $ to be considered 5% which is called the Equity Risk Premium.\n",
    "\n",
    "$ r_i = r_f + \\beta_i (r_m - r_f) $"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate AMDs Expected Return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08347215137650096"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "risk_free_rate = 0.013\n",
    "ri = risk_free_rate + find_beta('AMD') * 0.05\n",
    "ri"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sharpe Ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "William Sharpe created the Sharpe Ratio to find the portfolio that provides the best return for the lowest amount of risk. \n",
    "\n",
    "*Sharpe Ratio* = $\\frac{r_i - r_f}{\\sigma_i}$\n",
    "\n",
    "$r_f = $ Risk Free Rate\n",
    "\n",
    "$r_i = $ Rate of Return of the stock\n",
    "\n",
    "$\\sigma_i = $ Standard Deviation of the Stock\n",
    "\n",
    "As return increases so does the Sharpe Ratio, but as Standard Deviation increase the Sharpe Ratio decreases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1253990235142509"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can find the Sharpe ratio for AMD\n",
    "amd_sharpe = (ri - risk_free_rate) / (mult_df['AMD'].std() * 252 ** 0.5)\n",
    "amd_sharpe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Stock Prices on Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_prices_on_date(stocks_df, date):\n",
    "    return stocks_df.loc[pd.DatetimeIndex([date])]['Adj Close'].item()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Returns the Value of Portfolio by Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_port_val_by_date(date, shares, tickers):\n",
    "    port_prices = merge_df_by_column_name('Adj Close',  date, \n",
    "                                  date, *port_list)\n",
    "    # Convert from dataframe to Python list\n",
    "    port_prices = port_prices.values.tolist()\n",
    "    # Trick that converts a list of lists into a single list\n",
    "    port_prices = sum(port_prices, [])\n",
    "    \n",
    "    # Create a list of values by multiplying shares by price\n",
    "    value_list = []\n",
    "    for price, share in zip(port_prices, shares):\n",
    "        value_list.append(price * share)\n",
    "    \n",
    "    return sum(value_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Value of Portfolio at Beginning and End of Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Portfolio Value at Start of 2020 : $11738.29\n",
      "Portfolio Value at End of 2020 : $19232.34\n"
     ]
    }
   ],
   "source": [
    "port_list = [\"GNRC\", \"CPRT\", \"ODFL\", \"AMD\", \"PAYC\", \"CHTR\", \"MKC\", \n",
    "             \"PG\", \"PGR\", \"NEM\", \"CCI\", \"COG\"]\n",
    "\n",
    "port_shares = [25, 20, 22, 26, 1, 1, 4, 1, 5, 28, 3, 7]\n",
    "\n",
    "# Portfolio value at start of 2020\n",
    "port_val_start = get_port_val_by_date('2020-01-02', port_shares, port_list)\n",
    "print(\"Portfolio Value at Start of 2020 : $%2.2f\" % (port_val_start))\n",
    "\n",
    "# Portfolio value at end of 2020\n",
    "port_val_end = get_port_val_by_date('2020-12-31', port_shares, port_list)\n",
    "print(\"Portfolio Value at End of 2020 : $%2.2f\" % (port_val_end))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Return on Investment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ROI = $\\frac{Final Value - Initial Value}{Initial Value}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Portfolio ROI at End of 2020 : 38.97 %\n",
      "S&P ROI at End of 2020 : 13.26 %\n"
     ]
    }
   ],
   "source": [
    "# Rate of return for portfolio\n",
    "roi_port = (port_val_end - port_val_start) / port_val_end\n",
    "print(\"Portfolio ROI at End of 2020 : %2.2f %%\" % (roi_port * 100))\n",
    "\n",
    "# S&P ROI\n",
    "sp_df = get_df_from_csv('GSPC')\n",
    "sp_val_start = get_prices_on_date(sp_df, '2020-01-02')\n",
    "sp_val_end = get_prices_on_date(sp_df, '2020-12-31')\n",
    "sp_roi = (sp_val_end - sp_val_start) / sp_val_end\n",
    "print(\"S&P ROI at End of 2020 : %2.2f %%\" % (sp_roi * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find Daily Return for Whole Portfolio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find the daily return for the whole portfolio, I must multiply the daily price by the number of shares for each security. Then sum those values for all stocks per day. This creates a portfolio list of daily prices. Then I can calculate the daily return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_port_daily_return(sdate, edate, shares, tickers):\n",
    "    # Merge all daily prices for all stocks into 1 dataframe\n",
    "    mult_df = merge_df_by_column_name('Adj Close',  sdate, \n",
    "                                  edate, *port_list)\n",
    "    \n",
    "    # Get the number of stocks in portfolio\n",
    "    num_cols = len(mult_df.columns)\n",
    "    \n",
    "    # Multiply each stock column by the number of shares\n",
    "    i = 0\n",
    "    while i < num_cols:\n",
    "        mult_df[tickers[i]] = mult_df[tickers[i]].apply(lambda x: x * shares[i])\n",
    "        i += 1\n",
    "        \n",
    "    # Create a new column with the sums of all stocks named Total\n",
    "    mult_df['Total'] = mult_df.iloc[:, 0:num_cols].sum(axis=1)\n",
    "    \n",
    "    # Add column for portfolio daily return\n",
    "    mult_df['daily_return'] = (mult_df['Total'] / mult_df['Total'].shift(1)) - 1\n",
    "    \n",
    "    return mult_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "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>GNRC</th>\n",
       "      <th>CPRT</th>\n",
       "      <th>ODFL</th>\n",
       "      <th>AMD</th>\n",
       "      <th>PAYC</th>\n",
       "      <th>CHTR</th>\n",
       "      <th>MKC</th>\n",
       "      <th>PG</th>\n",
       "      <th>PGR</th>\n",
       "      <th>NEM</th>\n",
       "      <th>CCI</th>\n",
       "      <th>COG</th>\n",
       "      <th>Total</th>\n",
       "      <th>daily_return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2020-01-02</th>\n",
       "      <td>2550.250053</td>\n",
       "      <td>1869.600067</td>\n",
       "      <td>2801.878052</td>\n",
       "      <td>1276.599960</td>\n",
       "      <td>271.799988</td>\n",
       "      <td>490.429993</td>\n",
       "      <td>327.721405</td>\n",
       "      <td>118.208862</td>\n",
       "      <td>341.399498</td>\n",
       "      <td>1168.771851</td>\n",
       "      <td>405.909119</td>\n",
       "      <td>115.724598</td>\n",
       "      <td>11738.293446</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>2541.749954</td>\n",
       "      <td>1853.200073</td>\n",
       "      <td>2768.464981</td>\n",
       "      <td>1263.599960</td>\n",
       "      <td>272.709991</td>\n",
       "      <td>494.470001</td>\n",
       "      <td>332.944458</td>\n",
       "      <td>117.413841</td>\n",
       "      <td>341.353569</td>\n",
       "      <td>1158.225388</td>\n",
       "      <td>408.143738</td>\n",
       "      <td>116.060427</td>\n",
       "      <td>11668.336382</td>\n",
       "      <td>-0.005960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>2531.250000</td>\n",
       "      <td>1869.799957</td>\n",
       "      <td>2746.578781</td>\n",
       "      <td>1258.139984</td>\n",
       "      <td>278.089996</td>\n",
       "      <td>497.450012</td>\n",
       "      <td>334.939789</td>\n",
       "      <td>117.576675</td>\n",
       "      <td>347.554169</td>\n",
       "      <td>1169.853424</td>\n",
       "      <td>404.705795</td>\n",
       "      <td>117.000742</td>\n",
       "      <td>11672.939325</td>\n",
       "      <td>0.000394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>2572.500038</td>\n",
       "      <td>1867.400055</td>\n",
       "      <td>2754.457840</td>\n",
       "      <td>1254.500000</td>\n",
       "      <td>279.489990</td>\n",
       "      <td>499.160004</td>\n",
       "      <td>329.188568</td>\n",
       "      <td>116.848701</td>\n",
       "      <td>344.899635</td>\n",
       "      <td>1169.042191</td>\n",
       "      <td>397.858521</td>\n",
       "      <td>117.672400</td>\n",
       "      <td>11703.017942</td>\n",
       "      <td>0.002577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>2500.749969</td>\n",
       "      <td>1880.200043</td>\n",
       "      <td>2767.443634</td>\n",
       "      <td>1243.580048</td>\n",
       "      <td>283.119995</td>\n",
       "      <td>500.769989</td>\n",
       "      <td>327.428009</td>\n",
       "      <td>117.346779</td>\n",
       "      <td>346.937981</td>\n",
       "      <td>1137.943588</td>\n",
       "      <td>399.520248</td>\n",
       "      <td>113.172350</td>\n",
       "      <td>11618.212633</td>\n",
       "      <td>-0.007246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-24</th>\n",
       "      <td>5829.999924</td>\n",
       "      <td>2474.199982</td>\n",
       "      <td>4354.264801</td>\n",
       "      <td>2387.059937</td>\n",
       "      <td>462.130005</td>\n",
       "      <td>656.489990</td>\n",
       "      <td>369.677307</td>\n",
       "      <td>135.173843</td>\n",
       "      <td>467.704163</td>\n",
       "      <td>1664.569366</td>\n",
       "      <td>455.012192</td>\n",
       "      <td>116.410582</td>\n",
       "      <td>19372.692091</td>\n",
       "      <td>0.012335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>5825.999832</td>\n",
       "      <td>2534.400024</td>\n",
       "      <td>4280.906738</td>\n",
       "      <td>2381.599960</td>\n",
       "      <td>463.589996</td>\n",
       "      <td>651.900024</td>\n",
       "      <td>369.479523</td>\n",
       "      <td>136.116104</td>\n",
       "      <td>466.800270</td>\n",
       "      <td>1646.404465</td>\n",
       "      <td>461.660065</td>\n",
       "      <td>113.249495</td>\n",
       "      <td>19332.106497</td>\n",
       "      <td>-0.002095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>5534.249878</td>\n",
       "      <td>2518.399963</td>\n",
       "      <td>4263.775299</td>\n",
       "      <td>2356.120071</td>\n",
       "      <td>447.589996</td>\n",
       "      <td>664.979980</td>\n",
       "      <td>375.016571</td>\n",
       "      <td>135.860901</td>\n",
       "      <td>464.754562</td>\n",
       "      <td>1643.927292</td>\n",
       "      <td>460.360016</td>\n",
       "      <td>110.500713</td>\n",
       "      <td>18975.535244</td>\n",
       "      <td>-0.018445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>5729.750061</td>\n",
       "      <td>2530.800018</td>\n",
       "      <td>4252.573547</td>\n",
       "      <td>2399.540024</td>\n",
       "      <td>447.910004</td>\n",
       "      <td>653.489990</td>\n",
       "      <td>375.334137</td>\n",
       "      <td>135.222931</td>\n",
       "      <td>463.422470</td>\n",
       "      <td>1662.092300</td>\n",
       "      <td>462.073746</td>\n",
       "      <td>113.180775</td>\n",
       "      <td>19225.390003</td>\n",
       "      <td>0.013167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>5685.250092</td>\n",
       "      <td>2545.000000</td>\n",
       "      <td>4286.836426</td>\n",
       "      <td>2384.459976</td>\n",
       "      <td>452.250000</td>\n",
       "      <td>661.549988</td>\n",
       "      <td>379.462189</td>\n",
       "      <td>136.567596</td>\n",
       "      <td>470.415878</td>\n",
       "      <td>1648.330917</td>\n",
       "      <td>470.346680</td>\n",
       "      <td>111.875097</td>\n",
       "      <td>19232.344839</td>\n",
       "      <td>0.000362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>253 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   GNRC         CPRT         ODFL          AMD        PAYC  \\\n",
       "Date                                                                         \n",
       "2020-01-02  2550.250053  1869.600067  2801.878052  1276.599960  271.799988   \n",
       "2020-01-03  2541.749954  1853.200073  2768.464981  1263.599960  272.709991   \n",
       "2020-01-06  2531.250000  1869.799957  2746.578781  1258.139984  278.089996   \n",
       "2020-01-07  2572.500038  1867.400055  2754.457840  1254.500000  279.489990   \n",
       "2020-01-08  2500.749969  1880.200043  2767.443634  1243.580048  283.119995   \n",
       "...                 ...          ...          ...          ...         ...   \n",
       "2020-12-24  5829.999924  2474.199982  4354.264801  2387.059937  462.130005   \n",
       "2020-12-28  5825.999832  2534.400024  4280.906738  2381.599960  463.589996   \n",
       "2020-12-29  5534.249878  2518.399963  4263.775299  2356.120071  447.589996   \n",
       "2020-12-30  5729.750061  2530.800018  4252.573547  2399.540024  447.910004   \n",
       "2020-12-31  5685.250092  2545.000000  4286.836426  2384.459976  452.250000   \n",
       "\n",
       "                  CHTR         MKC          PG         PGR          NEM  \\\n",
       "Date                                                                      \n",
       "2020-01-02  490.429993  327.721405  118.208862  341.399498  1168.771851   \n",
       "2020-01-03  494.470001  332.944458  117.413841  341.353569  1158.225388   \n",
       "2020-01-06  497.450012  334.939789  117.576675  347.554169  1169.853424   \n",
       "2020-01-07  499.160004  329.188568  116.848701  344.899635  1169.042191   \n",
       "2020-01-08  500.769989  327.428009  117.346779  346.937981  1137.943588   \n",
       "...                ...         ...         ...         ...          ...   \n",
       "2020-12-24  656.489990  369.677307  135.173843  467.704163  1664.569366   \n",
       "2020-12-28  651.900024  369.479523  136.116104  466.800270  1646.404465   \n",
       "2020-12-29  664.979980  375.016571  135.860901  464.754562  1643.927292   \n",
       "2020-12-30  653.489990  375.334137  135.222931  463.422470  1662.092300   \n",
       "2020-12-31  661.549988  379.462189  136.567596  470.415878  1648.330917   \n",
       "\n",
       "                   CCI         COG         Total  daily_return  \n",
       "Date                                                            \n",
       "2020-01-02  405.909119  115.724598  11738.293446           NaN  \n",
       "2020-01-03  408.143738  116.060427  11668.336382     -0.005960  \n",
       "2020-01-06  404.705795  117.000742  11672.939325      0.000394  \n",
       "2020-01-07  397.858521  117.672400  11703.017942      0.002577  \n",
       "2020-01-08  399.520248  113.172350  11618.212633     -0.007246  \n",
       "...                ...         ...           ...           ...  \n",
       "2020-12-24  455.012192  116.410582  19372.692091      0.012335  \n",
       "2020-12-28  461.660065  113.249495  19332.106497     -0.002095  \n",
       "2020-12-29  460.360016  110.500713  18975.535244     -0.018445  \n",
       "2020-12-30  462.073746  113.180775  19225.390003      0.013167  \n",
       "2020-12-31  470.346680  111.875097  19232.344839      0.000362  \n",
       "\n",
       "[253 rows x 14 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tot_port_df = get_port_daily_return('2020-01-02', '2020-12-31', \n",
    "                                    port_shares, port_list)\n",
    "tot_port_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find Portfolio Beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_port_beta(port_df, sdate, edate):\n",
    "    # Will hold data for S&P and my portfolio\n",
    "    mult_df = pd.DataFrame()\n",
    "    \n",
    "    # Mask defining the dates worth of data that we want\n",
    "    port_mask = (port_df.index >= sdate) & (port_df.index <= edate)\n",
    "    \n",
    "    # Get S&P Dataframe\n",
    "    sp_df = get_df_from_csv('GSPC')\n",
    "    \n",
    "    sp_mask = (sp_df.index >= sdate) & (sp_df.index <= edate)\n",
    "    \n",
    "    # Add S&P daily return to dataframe\n",
    "    mult_df['GSPC'] = sp_df.loc[sp_mask]['daily_return']\n",
    "    \n",
    "    # Add the portfolio daily return data\n",
    "    mult_df['Portfolio'] = port_df.loc[port_mask]['daily_return']\n",
    "    \n",
    "    # Provides the covariance between the securities\n",
    "    cov = mult_df.cov() * 252\n",
    "    \n",
    "    # Get the covariance of the stocks and the market\n",
    "    cov_vs_market = cov.iloc[0,1]\n",
    "    \n",
    "    # Get annualized variance of the S&P\n",
    "    sp_var = mult_df['GSPC'].var() * 252\n",
    "    \n",
    "    # Beta is normally calculated over a 5 year period which is why you may see a difference\n",
    "    beta = cov_vs_market / sp_var\n",
    "    return beta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating Alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alpha provides a measure of how well a portfolio has performed. The CAPM assumes an Alpha of 0. Good portfolios have a positive Alpha, while poor have negative. \n",
    "\n",
    "Alpha = R – Rf – beta (Rm-Rf)\n",
    "\n",
    "* R represents the portfolio return\n",
    "* Rf represents the risk-free rate of return\n",
    "* Beta represents the systematic risk of a portfolio\n",
    "* Rm represents the market return, per a benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9132983282237106"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "port_beta = find_port_beta(tot_port_df, '2020-01-02', '2020-12-31')\n",
    "port_beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Portfolio Alpha : 26.74 %\n"
     ]
    }
   ],
   "source": [
    "port_alpha = roi_port - risk_free_rate - (port_beta * (sp_roi - risk_free_rate))\n",
    "print(\"Portfolio Alpha : %2.2f %%\" % (port_alpha * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means our portfolio outperformed the market in 2020 by 26.74%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Old Data for Portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "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>GNRC</th>\n",
       "      <th>CPRT</th>\n",
       "      <th>ODFL</th>\n",
       "      <th>AMD</th>\n",
       "      <th>PAYC</th>\n",
       "      <th>CHTR</th>\n",
       "      <th>MKC</th>\n",
       "      <th>PG</th>\n",
       "      <th>PGR</th>\n",
       "      <th>NEM</th>\n",
       "      <th>CCI</th>\n",
       "      <th>COG</th>\n",
       "      <th>Total</th>\n",
       "      <th>daily_return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2017-01-03</th>\n",
       "      <td>1048.499966</td>\n",
       "      <td>557.200012</td>\n",
       "      <td>1251.128479</td>\n",
       "      <td>297.180008</td>\n",
       "      <td>46.470001</td>\n",
       "      <td>285.769989</td>\n",
       "      <td>173.972595</td>\n",
       "      <td>73.476089</td>\n",
       "      <td>150.439453</td>\n",
       "      <td>879.123833</td>\n",
       "      <td>222.305031</td>\n",
       "      <td>145.098261</td>\n",
       "      <td>5130.663717</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>1050.000000</td>\n",
       "      <td>564.599991</td>\n",
       "      <td>1256.318550</td>\n",
       "      <td>297.180008</td>\n",
       "      <td>47.340000</td>\n",
       "      <td>291.549988</td>\n",
       "      <td>173.470673</td>\n",
       "      <td>73.737885</td>\n",
       "      <td>151.794395</td>\n",
       "      <td>887.501060</td>\n",
       "      <td>223.636803</td>\n",
       "      <td>145.293098</td>\n",
       "      <td>5162.422451</td>\n",
       "      <td>0.006190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>1027.999973</td>\n",
       "      <td>564.000015</td>\n",
       "      <td>1241.469070</td>\n",
       "      <td>292.239994</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>296.170013</td>\n",
       "      <td>170.068909</td>\n",
       "      <td>74.226555</td>\n",
       "      <td>151.752033</td>\n",
       "      <td>928.373077</td>\n",
       "      <td>223.431931</td>\n",
       "      <td>147.046730</td>\n",
       "      <td>5163.778301</td>\n",
       "      <td>0.000263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>1017.749977</td>\n",
       "      <td>566.800003</td>\n",
       "      <td>1241.324806</td>\n",
       "      <td>294.319992</td>\n",
       "      <td>47.490002</td>\n",
       "      <td>298.160004</td>\n",
       "      <td>169.901672</td>\n",
       "      <td>74.200378</td>\n",
       "      <td>152.556515</td>\n",
       "      <td>899.178818</td>\n",
       "      <td>218.975624</td>\n",
       "      <td>148.670479</td>\n",
       "      <td>5129.328270</td>\n",
       "      <td>-0.006671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>1007.250023</td>\n",
       "      <td>565.299988</td>\n",
       "      <td>1234.548836</td>\n",
       "      <td>298.739994</td>\n",
       "      <td>48.400002</td>\n",
       "      <td>296.260010</td>\n",
       "      <td>167.559433</td>\n",
       "      <td>73.650635</td>\n",
       "      <td>151.243935</td>\n",
       "      <td>897.655685</td>\n",
       "      <td>219.564629</td>\n",
       "      <td>143.474499</td>\n",
       "      <td>5103.647667</td>\n",
       "      <td>-0.005007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-24</th>\n",
       "      <td>5829.999924</td>\n",
       "      <td>2474.199982</td>\n",
       "      <td>4354.264801</td>\n",
       "      <td>2387.059937</td>\n",
       "      <td>462.130005</td>\n",
       "      <td>656.489990</td>\n",
       "      <td>369.677307</td>\n",
       "      <td>135.173843</td>\n",
       "      <td>467.704163</td>\n",
       "      <td>1664.569366</td>\n",
       "      <td>455.012192</td>\n",
       "      <td>116.410582</td>\n",
       "      <td>19372.692091</td>\n",
       "      <td>0.012335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>5825.999832</td>\n",
       "      <td>2534.400024</td>\n",
       "      <td>4280.906738</td>\n",
       "      <td>2381.599960</td>\n",
       "      <td>463.589996</td>\n",
       "      <td>651.900024</td>\n",
       "      <td>369.479523</td>\n",
       "      <td>136.116104</td>\n",
       "      <td>466.800270</td>\n",
       "      <td>1646.404465</td>\n",
       "      <td>461.660065</td>\n",
       "      <td>113.249495</td>\n",
       "      <td>19332.106497</td>\n",
       "      <td>-0.002095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>5534.249878</td>\n",
       "      <td>2518.399963</td>\n",
       "      <td>4263.775299</td>\n",
       "      <td>2356.120071</td>\n",
       "      <td>447.589996</td>\n",
       "      <td>664.979980</td>\n",
       "      <td>375.016571</td>\n",
       "      <td>135.860901</td>\n",
       "      <td>464.754562</td>\n",
       "      <td>1643.927292</td>\n",
       "      <td>460.360016</td>\n",
       "      <td>110.500713</td>\n",
       "      <td>18975.535244</td>\n",
       "      <td>-0.018445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>5729.750061</td>\n",
       "      <td>2530.800018</td>\n",
       "      <td>4252.573547</td>\n",
       "      <td>2399.540024</td>\n",
       "      <td>447.910004</td>\n",
       "      <td>653.489990</td>\n",
       "      <td>375.334137</td>\n",
       "      <td>135.222931</td>\n",
       "      <td>463.422470</td>\n",
       "      <td>1662.092300</td>\n",
       "      <td>462.073746</td>\n",
       "      <td>113.180775</td>\n",
       "      <td>19225.390003</td>\n",
       "      <td>0.013167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>5685.250092</td>\n",
       "      <td>2545.000000</td>\n",
       "      <td>4286.836426</td>\n",
       "      <td>2384.459976</td>\n",
       "      <td>452.250000</td>\n",
       "      <td>661.549988</td>\n",
       "      <td>379.462189</td>\n",
       "      <td>136.567596</td>\n",
       "      <td>470.415878</td>\n",
       "      <td>1648.330917</td>\n",
       "      <td>470.346680</td>\n",
       "      <td>111.875097</td>\n",
       "      <td>19232.344839</td>\n",
       "      <td>0.000362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1007 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   GNRC         CPRT         ODFL          AMD        PAYC  \\\n",
       "Date                                                                         \n",
       "2017-01-03  1048.499966   557.200012  1251.128479   297.180008   46.470001   \n",
       "2017-01-04  1050.000000   564.599991  1256.318550   297.180008   47.340000   \n",
       "2017-01-05  1027.999973   564.000015  1241.469070   292.239994   47.000000   \n",
       "2017-01-06  1017.749977   566.800003  1241.324806   294.319992   47.490002   \n",
       "2017-01-09  1007.250023   565.299988  1234.548836   298.739994   48.400002   \n",
       "...                 ...          ...          ...          ...         ...   \n",
       "2020-12-24  5829.999924  2474.199982  4354.264801  2387.059937  462.130005   \n",
       "2020-12-28  5825.999832  2534.400024  4280.906738  2381.599960  463.589996   \n",
       "2020-12-29  5534.249878  2518.399963  4263.775299  2356.120071  447.589996   \n",
       "2020-12-30  5729.750061  2530.800018  4252.573547  2399.540024  447.910004   \n",
       "2020-12-31  5685.250092  2545.000000  4286.836426  2384.459976  452.250000   \n",
       "\n",
       "                  CHTR         MKC          PG         PGR          NEM  \\\n",
       "Date                                                                      \n",
       "2017-01-03  285.769989  173.972595   73.476089  150.439453   879.123833   \n",
       "2017-01-04  291.549988  173.470673   73.737885  151.794395   887.501060   \n",
       "2017-01-05  296.170013  170.068909   74.226555  151.752033   928.373077   \n",
       "2017-01-06  298.160004  169.901672   74.200378  152.556515   899.178818   \n",
       "2017-01-09  296.260010  167.559433   73.650635  151.243935   897.655685   \n",
       "...                ...         ...         ...         ...          ...   \n",
       "2020-12-24  656.489990  369.677307  135.173843  467.704163  1664.569366   \n",
       "2020-12-28  651.900024  369.479523  136.116104  466.800270  1646.404465   \n",
       "2020-12-29  664.979980  375.016571  135.860901  464.754562  1643.927292   \n",
       "2020-12-30  653.489990  375.334137  135.222931  463.422470  1662.092300   \n",
       "2020-12-31  661.549988  379.462189  136.567596  470.415878  1648.330917   \n",
       "\n",
       "                   CCI         COG         Total  daily_return  \n",
       "Date                                                            \n",
       "2017-01-03  222.305031  145.098261   5130.663717           NaN  \n",
       "2017-01-04  223.636803  145.293098   5162.422451      0.006190  \n",
       "2017-01-05  223.431931  147.046730   5163.778301      0.000263  \n",
       "2017-01-06  218.975624  148.670479   5129.328270     -0.006671  \n",
       "2017-01-09  219.564629  143.474499   5103.647667     -0.005007  \n",
       "...                ...         ...           ...           ...  \n",
       "2020-12-24  455.012192  116.410582  19372.692091      0.012335  \n",
       "2020-12-28  461.660065  113.249495  19332.106497     -0.002095  \n",
       "2020-12-29  460.360016  110.500713  18975.535244     -0.018445  \n",
       "2020-12-30  462.073746  113.180775  19225.390003      0.013167  \n",
       "2020-12-31  470.346680  111.875097  19232.344839      0.000362  \n",
       "\n",
       "[1007 rows x 14 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remember These\n",
    "# port_list = [\"GNRC\", \"CPRT\", \"ODFL\", \"AMD\", \"PAYC\", \"CHTR\", \"MKC\", \n",
    "#              \"PG\", \"PGR\", \"NEM\", \"CCI\", \"COG\"]\n",
    "\n",
    "# port_shares = [25, 20, 22, 26, 1, 1, 4, 1, 5, 28, 3, 7]\n",
    "\n",
    "tot_port_df = get_port_daily_return('2017-01-03', '2020-12-31', \n",
    "                                    port_shares, port_list)\n",
    "tot_port_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You are going to choose different models depending upon the data you have. We will train our model on the first group of data (Training Data : 1st 80%). Then we test the model using the last part of our data (Test Date : Last 20%). All along we are modeling using real known true results. We should forecast into the future the same length of time we test for."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ARIMA Model (Auto Regression)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AutoRegressive Integrated Moving Average (ARIMA) is the basis for many other models. It focuses on trying to fit the data as well as possible by examining differences between values instead of the values themselves.\n",
    "\n",
    "ARIMA works very well when data values have a clear trend and seasonality. We can only make predictions based on the data we have. Any outside effects not in the data can't be used to make predictions. For example we could make predictions on stock prices, but since we don't know when a recession may occur that event can't be modeled.\n",
    "\n",
    "There is a seasonal (SARIMA) and a non-seasonal ARIMA. There is also SARIMAX which focuses on exogenous, or external factors. It differs from ARIMA in that it has a set of parameters (P, D, and Q) that focus on seasonality.\n",
    "\n",
    "AR (Autoregressions) refers to a model that regresses based on prior values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Time Series Example with Our Portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lags : [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14]\n",
      "Observations : 1459\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1368x936 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change frequency to day\n",
    "tot_port_df = tot_port_df.asfreq('d')\n",
    "tot_port_df.index\n",
    "\n",
    "# Delete NaNs for nontrading days\n",
    "tot_port_df = tot_port_df.fillna(method='ffill') # Fill in missing values using previous\n",
    "\n",
    "tot_port_df\n",
    "\n",
    "# Delete all unneeded columns\n",
    "del_col = [\"GNRC\", \"CPRT\", \"ODFL\", \"AMD\", \"PAYC\", \"CHTR\", \"MKC\", \n",
    "             \"PG\", \"PGR\", \"NEM\", \"CCI\", \"COG\", \"daily_return\"]\n",
    "for x in del_col:\n",
    "    tot_port_df = tot_port_df.drop([x], axis=1)\n",
    "\n",
    "# Set style for seaborn plot\n",
    "sns.set_style('darkgrid')\n",
    "# Add automatic datetime converters\n",
    "pd.plotting.register_matplotlib_converters()\n",
    "# Default figure size\n",
    "sns.mpl.rc('figure',figsize=(19, 13))\n",
    "\n",
    "# Set fig and ax\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Figure out optimum lags for this data set\n",
    "lags = ar_select_order(tot_port_df, maxlag=30)\n",
    "print(\"Lags :\", lags.ar_lags)\n",
    "\n",
    "# Create our model using whole data set\n",
    "model = AutoReg(tot_port_df['Total'], lags.ar_lags)\n",
    "model_fit = model.fit()\n",
    "\n",
    "# Define training and testing area\n",
    "print(\"Observations :\", len(tot_port_df)) # 1459 observations\n",
    "\n",
    "train_df = tot_port_df.iloc[0:1167] # First 80% \n",
    "test_df = tot_port_df.iloc[1167:] # Last 20%\n",
    "\n",
    "# Define training model for 459 days (Play with Number & Test)\n",
    "# and White's covariance estimator\n",
    "train_model = AutoReg(tot_port_df['Total'], 459).fit(cov_type=\"HC0\")\n",
    "\n",
    "# # Define start and end for prediction \n",
    "start = len(train_df)\n",
    "end = len(train_df) + len(test_df) - 1\n",
    "\n",
    "prediction = train_model.predict(start=start, end=end, dynamic=True)\n",
    "\n",
    "# Plot testing data with prediction\n",
    "ax = test_df.plot(ax=ax) # Orange\n",
    "ax = prediction.plot(ax=ax) # Green\n",
    "\n",
    "# Predict 100 days into the future\n",
    "forecast = train_model.predict(start=end, end=end+60, dynamic=True)\n",
    "ax = forecast.plot(ax=ax) # Green\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}