Sharpe_Ratio.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To Invest or Not to Invest\n",
    "\n",
    "It goes without saying that every investors expect return benefit from their investment. But returns are only part of the story, because with risks - there maybe a range of possible outcomes. How does one compare different investments that may deliver similar results on average, but exhibit different levels of risks?\n",
    "\n",
    "In 1966 William Sharpe introduced the reward-to-variability ratio, which compares the expected returns for two invesment opportunities and calculates the additional return per unit of risk an investor could obtain by choosing one over the other. In particular, it looks at the difference in returns for two investments and compares the average difference to the standard deviation (as a measure of risk) of this difference. A higher Sharpe ratio means that the reward will be higher for a given amount of risk. \n",
    "\n",
    "The Sharpe ratio has been one of the most popular risk/return measures in finance, not least because it's so simple to use. It also helped that Professor Sharpe won a Nobel Memorial Prize in Economics in 1990 for his work on the capital asset pricing model (CAPM).\n",
    "\n",
    "The Sharpe ratio is usually calculated for a portfolio and uses the risk-free interest rate as benchmark. We will simplify our example and use stocks instead of a portfolio. We will also use a stock index as benchmark rather than the risk-free interest rate because both are readily available at daily frequencies and we do not have to get into converting interest rates from annual to daily frequency\n",
    "\n",
    "This Notebook will guide you on how to apply the Sharpe ratio for the stocks of two tech giants Facebook and Amazon. As benchmark we will use the S&P 500 that measures the performance of the 500 largest stocks in the US. When we use a stock index instead of the risk-free rate, the result is called the Information Ratio and is used to benchmark the return on active portfolio management because it tells you how much more return for a given unit of risk your portfolio manager earned relative to just putting your money into a low-cost index fund."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing required modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Settings to produce nice plots in a Jupyter notebook\n",
    "plt.style.use('fivethirtyeight')\n",
    "%matplotlib inline\n",
    "\n",
    "# Reading in the data\n",
    "stock_data = pd.read_csv('datasets/stock_data.csv', parse_dates = True, index_col = 'Date').dropna()\n",
    "benchmark_data = pd.read_csv('datasets/benchmark_data.csv', parse_dates = True, index_col = 'Date').dropna() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amazon</th>\n",
       "      <th>Facebook</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>2016-01-04</th>\n",
       "      <td>636.989990</td>\n",
       "      <td>102.220001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>633.789978</td>\n",
       "      <td>102.730003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>632.650024</td>\n",
       "      <td>102.970001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>607.940002</td>\n",
       "      <td>97.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>607.049988</td>\n",
       "      <td>97.330002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Amazon    Facebook\n",
       "Date                              \n",
       "2016-01-04  636.989990  102.220001\n",
       "2016-01-05  633.789978  102.730003\n",
       "2016-01-06  632.650024  102.970001\n",
       "2016-01-07  607.940002   97.919998\n",
       "2016-01-08  607.049988   97.330002"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data at first glance\n",
    "Let's take a look the data to find out how many observations and variables we have at our disposal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stocks\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 252 entries, 2016-01-04 to 2016-12-30\n",
      "Data columns (total 2 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Amazon    252 non-null    float64\n",
      " 1   Facebook  252 non-null    float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 5.9 KB\n",
      "\n",
      "Benchmarks\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 252 entries, 2016-01-04 to 2016-12-30\n",
      "Data columns (total 1 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   S&P 500  252 non-null    float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 3.9 KB\n"
     ]
    }
   ],
   "source": [
    "# Display summary for stock_data\n",
    "print('Stocks\\n')\n",
    "stock_data.info()\n",
    "\n",
    "# Display summary for benchmark_data\n",
    "print('\\nBenchmarks\\n')\n",
    "benchmark_data.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary of daily prices for Amazon and Facebook\n",
    "<p>Before we compare an investment in either Facebook or Amazon with the index of the 500 largest companies in the US, let's visualize the data, so we better understand what we're dealing with.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amazon</th>\n",
       "      <th>Facebook</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>252.000000</td>\n",
       "      <td>252.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>699.523135</td>\n",
       "      <td>117.035873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>92.362312</td>\n",
       "      <td>8.899858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>482.070007</td>\n",
       "      <td>94.160004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>606.929993</td>\n",
       "      <td>112.202499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>727.875000</td>\n",
       "      <td>117.765000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>767.882492</td>\n",
       "      <td>123.902503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>844.359985</td>\n",
       "      <td>133.279999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Amazon    Facebook\n",
       "count  252.000000  252.000000\n",
       "mean   699.523135  117.035873\n",
       "std     92.362312    8.899858\n",
       "min    482.070007   94.160004\n",
       "25%    606.929993  112.202499\n",
       "50%    727.875000  117.765000\n",
       "75%    767.882492  123.902503\n",
       "max    844.359985  133.279999"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the stock_data\n",
    "plt.plot(stock_data)\n",
    "plt.title('Stock Data')\n",
    "plt.legend(['Amazon', 'Facebook'])\n",
    "\n",
    "# summarize the stock_data\n",
    "stock_data.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary daily values for the S&P 500\n",
    "<p>Let's also take a closer look at the value of the S&amp;P 500, our benchmark.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>S&amp;P 500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>252.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2094.651310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>101.427615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1829.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2047.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2104.105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2169.075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2271.720000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           S&P 500\n",
       "count   252.000000\n",
       "mean   2094.651310\n",
       "std     101.427615\n",
       "min    1829.080000\n",
       "25%    2047.060000\n",
       "50%    2104.105000\n",
       "75%    2169.075000\n",
       "max    2271.720000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the benchmark_data\n",
    "plt.plot(benchmark_data)\n",
    "plt.title('Benchmark Data')\n",
    "\n",
    "# summarize the benchmark_data\n",
    "benchmark_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The inputs for the Sharpe Ratio: Starting with Daily Stock Returns\n",
    "<p>The Sharpe Ratio uses the difference in returns between the two investment opportunities under consideration.</p>\n",
    "<p>However, our data show the historical value of each investment, not the return. To calculate the return, we need to calculate the percentage change in value from one day to the next. We'll also take a look at the summary statistics because these will become our inputs as we calculate the Sharpe Ratio. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Amazon</th>\n",
       "      <th>Facebook</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>251.000000</td>\n",
       "      <td>251.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000818</td>\n",
       "      <td>0.000626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.018383</td>\n",
       "      <td>0.017840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.076100</td>\n",
       "      <td>-0.058105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.007211</td>\n",
       "      <td>-0.007220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000857</td>\n",
       "      <td>0.000879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.009224</td>\n",
       "      <td>0.008108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.095664</td>\n",
       "      <td>0.155214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Amazon    Facebook\n",
       "count  251.000000  251.000000\n",
       "mean     0.000818    0.000626\n",
       "std      0.018383    0.017840\n",
       "min     -0.076100   -0.058105\n",
       "25%     -0.007211   -0.007220\n",
       "50%      0.000857    0.000879\n",
       "75%      0.009224    0.008108\n",
       "max      0.095664    0.155214"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate daily stock_data returns\n",
    "stock_returns = stock_data.pct_change()\n",
    "\n",
    "# plot the daily returns\n",
    "plt.plot(stock_returns)\n",
    "plt.title('Stock returns')\n",
    "plt.legend(['Amazon', 'Facebook'])\n",
    "\n",
    "# summarize the daily returns\n",
    "stock_returns.describe()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Daily S&P 500 returns\n",
    "<p>For the S&amp;P 500, calculating daily returns works just the same way, we just need to make sure we select it as a <code>Series</code> using single brackets <code>[]</code> and not as a <code>DataFrame</code> to facilitate the calculations in the next step.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    251.000000\n",
       "mean       0.000458\n",
       "std        0.008205\n",
       "min       -0.035920\n",
       "25%       -0.002949\n",
       "50%        0.000205\n",
       "75%        0.004497\n",
       "max        0.024760\n",
       "Name: S&P 500, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate daily benchmark_data returns\n",
    "sp_returns = benchmark_data.iloc[:,0].pct_change()\n",
    "sp_returns.head()\n",
    "\n",
    "# plot the daily returns\n",
    "plt.plot(sp_returns)\n",
    "plt.title('S&P returns')\n",
    "\n",
    "# summarize the daily returns\n",
    "sp_returns.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating Excess Returns for Amazon and Facebook vs. S&P 500\n",
    "<p>Next, we need to calculate the relative performance of stocks vs. the S&amp;P 500 benchmark. This is calculated as the difference in returns between <code>stock_returns</code> and <code>sp_returns</code> for each day.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Amazon</th>\n",
       "      <th>Facebook</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>251.000000</td>\n",
       "      <td>251.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000360</td>\n",
       "      <td>0.000168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.016126</td>\n",
       "      <td>0.015439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.100860</td>\n",
       "      <td>-0.051958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.006229</td>\n",
       "      <td>-0.005663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000698</td>\n",
       "      <td>-0.000454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.007351</td>\n",
       "      <td>0.005814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.100728</td>\n",
       "      <td>0.149686</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Amazon    Facebook\n",
       "count  251.000000  251.000000\n",
       "mean     0.000360    0.000168\n",
       "std      0.016126    0.015439\n",
       "min     -0.100860   -0.051958\n",
       "25%     -0.006229   -0.005663\n",
       "50%      0.000698   -0.000454\n",
       "75%      0.007351    0.005814\n",
       "max      0.100728    0.149686"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate the difference in daily returns\n",
    "excess_returns = stock_returns.sub(sp_returns, axis=0)\n",
    "\n",
    "# plot the excess_returns\n",
    "plt.plot(excess_returns)\n",
    "\n",
    "\n",
    "# summarize the excess_returns\n",
    "excess_returns.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Sharpe Ratio, Step 1: The Average Difference in Daily Returns Stocks vs S&P 500\n",
    "<p>Now we can finally start computing the Sharpe Ratio. First we need to calculate the average of the <code>excess_returns</code>. This tells us how much more or less the investment yields per day compared to the benchmark.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Mean of the Return Difference'}>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate the mean of excess_returns \n",
    "avg_excess_return = excess_returns.mean()\n",
    "\n",
    "# plot avg_excess_returns\n",
    "avg_excess_return.plot.bar(title='Mean of the Return Difference')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Sharpe Ratio, Step 2: Standard Deviation of the Return Difference\n",
    "<p>It looks like there was quite a bit of a difference between average daily returns for Amazon and Facebook.</p>\n",
    "<p>Next, we calculate the standard deviation of the <code>excess_returns</code>. This shows us the amount of risk an investment in the stocks implies as compared to an investment in the S&amp;P 500.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Standard Deviation of the Return Difference'}>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TM2UmP/QBBxeWpwIPDqJOs+1+OZLvAjuBF5eIx8zMOliZxLQG6JI0XdIE4Eygu6FON7Aoz86bC2yNiFbDeABfBU4CkHQYMAH48Z4Eb2ZmnadtYoqIHcBS4HrgPuDaiFgraYmkJbnaKmA90At8Gjivv72ka4BvA4dL6pN0Ti5aARySp5GvBM5qNoxnZmZjS6kr2EbEKlLyKa5bXvg/gPMHaLtwgPXbgbeUjtTMzMYE//KDmZnVSqkzJjOzVvy9vWp06vf2fMZkZma14sRkZma14sRkZma14sRkZma14sRkZma14sRkZma14sRkZma14sRkZma14sRkZma14sRkZma14sRkZma14sRkZma14sRkZma14sRkZma14sRkZma1UioxSZonaZ2kXknLmpRL0qW5/G5JMwtlKyRtyZdQb7bt90oKSS8e/GGYmVmnaJuYJI0DLgPmAzOAhZJmNFSbD3Tl27nA5YWyK4B5A2z7YOAU4Ed7GriZmXWmMmdMs4HeiFgfEduBlcCChjoLgCsjWQ1MkjQZICJuBR4bYNt/B7wPiEFFb2ZmHafMpdWnABsLy33AnBJ1pgCbB9qopNOATRFxl6SWAfT09JQIs24mVh3AmDQ6+0oncH+vwmjt711dXS3LyySmZlmj8QynTJ1nK0sTgYuA3ymx/7YHUUu3bao6gjFpVPaVTuD+XolO7e9lhvL6gIMLy1OBBwdRp+hQYDpwl6QNuf4dkg4qEY+ZmXWwMolpDdAlabqkCcCZQHdDnW5gUZ6dNxfYGhEDDuNFxH9HxAERMS0ippES28yIeGhwh2FmZp2ibWKKiB3AUuB64D7g2ohYK2mJpCW52ipgPdALfBo4r7+9pGuAbwOHS+qTdM4wH4OZmXWQMp8xERGrSMmnuG554f8Azh+g7cIS259WJg4zM+t8/uUHMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrlVKJSdI8Sesk9Upa1qRcki7N5XdLmlkoWyFpi6R7Gtr8jaT7c/2vSJo05KMxM7NRr21ikjQOuAyYD8wAFkqa0VBtPtCVb+cClxfKrgDmNdn0jcCREXE08ABw4Z4Gb2ZmnafMGdNsoDci1kfEdmAlsKChzgLgykhWA5MkTQaIiFuBxxo3GhE3RMSOvLgamDrYgzAzs84xvkSdKcDGwnIfMKdEnSnA5pJx/DHw+YEKe3p6Sm6mTiZWHcCYNDr7Sidwf6/CaO3vXV1dLcvLJCY1WReDqNN849JFwA7g6oHqtDuIWrptU9URjEmjsq90Avf3SnRqfy+TmPqAgwvLU4EHB1FnN5LOAl4HnBwRpRKZmZl1tjKfMa0BuiRNlzQBOBPobqjTDSzKs/PmAlsjouUwnqR5wPuB0yJi2yBiNzOzDtQ2MeUJCkuB64H7gGsjYq2kJZKW5GqrgPVAL/Bp4Lz+9pKuAb4NHC6pT9I5ueiTwK8AN0q6U9Ly4TooMzMbvcoM5RERq0jJp7hueeH/AM4foO3CAda/vHyYZmY2VviXH8zMrFacmMzMrFacmMzMrFacmMzMrFacmMzMrFacmMzMrFacmMzMrFacmMzMrFacmMzMrFacmMzMrFacmMzMrFacmMzMrFacmMzMrFacmMzMrFacmMzMrFacmMzMrFZKJSZJ8yStk9QraVmTckm6NJffLWlmoWyFpC2S7mlos7+kGyX15L/7Df1wzMxstGubmCSNAy4D5gMzgIWSZjRUmw905du5wOWFsiuAeU02vQy4OSK6gJvzspmZjXFlzphmA70RsT4itgMrgQUNdRYAV0ayGpgkaTJARNwKPNZkuwuAz+X/PwecPoj4zcysw4wvUWcKsLGw3AfMKVFnCrC5xXYPjIjNABGxWdIBA1Xs6ekpEWbdTKw6gDFpdPaVTuD+XoXR2t+7urpalpdJTGqyLgZRZ9DaHUQt3bap6gjGpFHZVzqB+3slOrW/lxnK6wMOLixPBR4cRJ1GD/cP9+W/W0rEYmZmHa5MYloDdEmaLmkCcCbQ3VCnG1iUZ+fNBbb2D9O10A2clf8/C7huD+I2M7MO1TYxRcQOYClwPXAfcG1ErJW0RNKSXG0VsB7oBT4NnNffXtI1wLeBwyX1STonF30UOEVSD3BKXjYzszGuzGdMRMQqUvIprlte+D+A8wdou3CA9Y8CJ5eO1MzMxgT/8oOZmdWKE5OZmdWKE5OZmdWKE5OZmdWKE5OZmdWKE5OZmdWKE5OZmdWKE5OZmdWKE5OZmdWKE5OZmdWKE5OZmdWKE5OZmdWKE5OZmdWKE5OZmdWKE5OZmdWKE5OZmdWKE5OZmdVKqcQkaZ6kdZJ6JS1rUi5Jl+byuyXNbNdW0islrZZ0p6TvSZo9PIdkZmajWdvEJGkccBkwH5gBLJQ0o6HafKAr384FLi/R9q+BD0XEK4G/yMtmZjbGlTljmg30RsT6iNgOrAQWNNRZAFwZyWpgkqTJbdoG8KL8/77Ag0M8FjMz6wDjS9SZAmwsLPcBc0rUmdKm7Z8B10v6GClBvnqgAHp6ekqEWTcTqw5gTBqdfaUTuL9XYbT2966urpblZRKTmqyLknVatX078K6I+JKkNwKfBX67WQDtDqKWbttUdQRj0qjsK53A/b0Sndrfywzl9QEHF5ansvuw20B1WrU9C/hy/v8LpGE/MzMb48okpjVAl6TpkiYAZwLdDXW6gUV5dt5cYGtEbG7T9kHgtfn/k4DReU5qZmbDqu1QXkTskLQUuB4YB6yIiLWSluTy5cAq4FSgF9gGnN2qbd7024BPSBoPPEWazWdmZmNcmc+YiIhVpORTXLe88H8A55dtm9ffBhy7J8GamVnn8y8/mJlZrTgxmZlZrTgxmZlZrTgxmZlZrTgxmZlZrTgxmZlZrTgxmZlZrTgxmZlZrTgxmZlZrTgxmZlZrTgxmZlZrTgxmZlZrTgxmZlZrTgxmZlZrTgxmZlZrTgxmZlZrTgxmZlZrZRKTJLmSVonqVfSsiblknRpLr9b0swybSW9I5etlfTXQz8cMzMb7dpeWl3SOOAy4BSgD1gjqTsi7i1Umw905dsc4HJgTqu2kk4EFgBHR8TTkg4YzgMzM7PRqcwZ02ygNyLWR8R2YCUpoRQtAK6MZDUwSdLkNm3fDnw0Ip4GiIgtw3A8ZmY2yrU9YwKmABsLy32ks6J2daa0aXsY8BpJFwNPAe+NiDXNAujp6SkRZt1MrDqAMWl09pVO4P5ehdHa37u6ulqWl0lMarIuStZp1XY8sB8wF5gFXCvpkIho3Hbbg6il2zZVHcGYNCr7Sidwf69Ep/b3MompDzi4sDwVeLBknQkt2vYBX86J6LuSdgIvBh4pHb2ZmXWcMp8xrQG6JE2XNAE4E+huqNMNLMqz8+YCWyNic5u2XwVOApB0GCmJ/XioB2RmZqNb2zOmiNghaSlwPTAOWBERayUtyeXLgVXAqUAvsA04u1XbvOkVwApJ9wDbgbOaDeOZmdnYUmYoj4hYRUo+xXXLC/8HcH7Ztnn9duAtexKsmZl1Pv/yg5mZ1YoTk5mZ1YoTk5mZ1YoTk5mZ1YoTk5mZ1YoTk5mZ1YoTk5mZ1YoTk5mZ1YoTk5mZ1YoTk5mZ1YoTk5mZ1YoTk5mZ1YoTk5mZ1YoTk5mZ1YoTk5mZ1YoTk5mZ1UqpxCRpnqR1knolLWtSLkmX5vK7Jc3cg7bvlRSSXjy0QzEzs07QNjFJGgdcBswHZgALJc1oqDYf6Mq3c4HLy7SVdDBwCvCjIR+JmZl1hDJnTLOB3ohYny+HvhJY0FBnAXBlJKuBSZIml2j7d8D7gBjqgZiZWWcYX6LOFGBjYbkPmFOizpRWbSWdBmyKiLsktQygp6enRJh1M7HqAMak0dlXOoH7exVGa3/v6upqWV4mMTXLGo1nOAPVabpe0kTgIuB3Suy/7UHU0m2bqo5gTBqVfaUTuL9XolP7e5mhvD7g4MLyVODBknUGWn8oMB24S9KGvP4OSQftSfBmZtZ5yiSmNUCXpOmSJgBnAt0NdbqBRXl23lxga0RsHqhtRPx3RBwQEdMiYhopgc2MiIeG68DMzGx0ajuUFxE7JC0FrgfGASsiYq2kJbl8ObAKOBXoBbYBZ7dqOyJHYmZmHaHMZ0xExCpS8imuW174P4Dzy7ZtUmdamTjMzKzz+ZcfzMysVpyYzMysVpyYzMysVpyYzMysVpyYzMysVpyYzMysVpyYzMysVpyYzMysVpyYzMysVpyYzMysVpyYzMysVpyYzMysVpyYzMysVpyYzMysVpyYzMysVpyYzMysVpyYzMysVkolJknzJK2T1CtpWZNySbo0l98taWa7tpL+RtL9uf5XJE0aliMyM7NRrW1ikjQOuAyYD8wAFkqa0VBtPtCVb+cCl5doeyNwZEQcDTwAXDjkozEzs1GvzBnTbKA3ItZHxHZgJbCgoc4C4MpIVgOTJE1u1TYiboiIHbn9amDqMByPmZmNcuNL1JkCbCws9wFzStSZUrItwB8Dnx8ogJ6enhJh1s3EqgMYk0ZnX+kE7u9VGK39vaurq2V5mcSkJuuiZJ22bSVdBOwArh4ogHYHUUu3bao6gjFpVPaVTuD+XolO7e9lElMfcHBheSrwYMk6E1q1lXQW8Drg5IhoTHZmZjYGlfmMaQ3QJWm6pAnAmUB3Q51uYFGenTcX2BoRm1u1lTQPeD9wWkRsG6bjMTOzUa7tGVNE7JC0FLgeGAesiIi1kpbk8uXAKuBUoBfYBpzdqm3e9CeB5wM3SgJYHRFLhvPgzMxs9CkzlEdErCIln+K65YX/Azi/bNu8/uV7FKmZmY0J/uUHMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrFScmMzOrlVKJSdI8Sesk9Upa1qRcki7N5XdLmtmuraT9Jd0oqSf/3W94DsnMzEaztolJ0jjgMmA+MANYKGlGQ7X5QFe+nQtcXqLtMuDmiOgCbs7LZmY2xo0vUWc20BsR6wEkrQQWAPcW6iwAroyIAFZLmiRpMjCtRdsFwAm5/eeAbwLvH+Lx1MYTZ0+pOgSz54z7uw2nMkN5U4CNheW+vK5MnVZtD4yIzQD57wHlwzYzs05VJjGpybooWadMWzMzs18qM5TXBxxcWJ4KPFiyzoQWbR+WNDkiNudhvy3FDe67777NkpqZmXW4MmdMa4AuSdMlTQDOBLob6nQDi/LsvLnA1jw816ptN3BW/v8s4LohHouZmXWAtmdMEbFD0lLgemAcsCIi1kpaksuXA6uAU4FeYBtwdqu2edMfBa6VdA7wI+CMYT0yMzMblZQm0pmZmdVDmc+YbAyQNAV4GYU+ERG3VheR2ciRNC0iNjSsmxURayoKyQp8xmRI+ivgTaTvlz2TV0dEnFZdVGYjR9IdwOsjYlNefi3wyYg4qtrIDJyYDJC0Djg6Ip6uOhaz54KkWcCngNcDM4FLSIlqY8uG9pzwUJ4BrAeeBzgx2ZgQEWskvRO4AXgKOCUiHqk4LMucmAzSTMo7Jd1MITlFxDurC8ls+En6Grt+yX8isBX4rCQ8fF0PTkwG6Ttljd9NM+tEH6s6AGvPnzEZAPkL0IflxXUR8Ysq4zEbaZIOBGblxe9GxJZW9e254wsFGpJOAHpIlyj5FPCApOOrjMlsJEl6I/Bd0hf73wh8R9IfVhuV9fMZkyHp+8AfRcS6vHwYcE1EHFttZGYjQ9JdpAkPW/LyS4CbIuIV1UZm4DMmS57Xn5QAIuIB0iw9s061V8PQ3aP49bA2PPnBAL4n6bPAVXn5zcD3K4zHbKR9Q9L1wDV5+U2k3/y0GvBQniHp+cD5wHGka2jdCnzKX7i1TibpDRT6fER8peKQLHNiMiS9FfhqRPy0sO51EfH1CsMyG1F5Vt5s0veaPCuvRjymagD/AHxL0m8U1v1lVcGYjbTCrLw/xLPyasefMRnAD4FzgC9K+mBEfIE0vGHWqS4CZjXOygO+WGlUBjgxWRIRcUf+heVrJM0hXdjRrFN5Vl6N+YEwgM0AEfFj4HdJY+5HVhqR2cj6hqTrJS2WtBj4VzwrrzY8+cHMxiTPyqsvD+VZ//j6+4EZwAv610fESZUFZTbybiddGHMn4CvX1oiH8gzgauA+YDrwIWADfqJaB5P0J6RZeb9Pmpm3WtIfVxuV9fNQniHp+xFxrKS7I+LovO6WiHht1bGZjYR81eZXR8SjeflXgdsj4vBqIzPwUJ4l/Ze42Czp94AHgakVxmM20vqAnxaWfwr4suo14cRkAB+RtC/wHtKXbV8EvKvakMyGn6R35383kb5Uex1pFuoC0tCe1YCH8sxszJD0f1uVR8SHnqtYbGBOTIak6cA7gGkUzqIj4rSqYjJ7Lkh6YUT8vOo4bFceyjOArwKfBb5Gmjpr1tEkvYrU5/cBXirpFcCfRsR51UZm4DMmAyR9JyLmVB2H2XNF0ndI08S7I+KYvO6eiPAvntSAz5gM4BN57P0G4JfXYIqIO6oLyWxkRcRGaZffKn6mqlhsV05MBnAU8FbgJJ4dyou8bNaJNkp6NRCSJgDvJH3J3GrAQ3mGpPuBoyNie9WxmD0XJL0Y+ATw26TfyrsBuKD/C7dWLZ8xGcBdwCTAV/C0MSH/kv6bq47DmvNv5RnAgcD9+TIA3fl2XdVBmY0USZ+TNKmwvJ+kFRWGZAU+YzKA4pcORboUwMKKYjF7LhwdEU/0L0TE45KOqTAeK/AZkxERtwBbgd8DrgBOBpZXGZPZCNtL0n79C5L2x2/Ua8MPxBgm6TDgTNLZ0aPA50kTYk6sNDCzkfdx4HZJX8zLZwAXVxiPFXhW3hgmaSfwLeCciOjN69ZHxCHVRmY28iQdAZxIGr6+OSLurTgky3zGNLb9AemM6T8kfQNYSXqSmnW8iFgr6RHyVZslvTQiflRxWIbPmIz0Q5bA6aQhvZOAzwFfiYgbqozLbKRIOo00nPdrpK9JvAy4LyKOqDQwA5yYrEH+EPgM4E0R4V9+sI4k6S7Sm7CbIuIYSScCCyPi3IpDM5yYzGwMkvS9iPjNnKCOiYidkr4bEbOrjs38GZOZjU1PSNoHuBW4WtIWYEfFMVnmMyYzGzP6Jzjkz1WfJH2X883AvsDV/q28enBiMrMxQ9IdETEz//+liPiDqmOy3fmXH8xsLCl+HcLf16spJyYzG0tigP+tRjyUZ2ZjhqRngJ+Tzpz2Brb1FwERES+qKjZ7lhOTmZnViofyzMysVpyYzMysVpyYzMysVpyYzMysVv4XcJNkchuF1qIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate the standard deviations\n",
    "sd_excess_return = excess_returns.std()\n",
    "\n",
    "# plot the standard deviations\n",
    "sd_excess_return.plot.bar(title = 'Standard Deviation of the Return Difference')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Putting it all together\n",
    "<p>Now we just need to compute the ratio of <code>avg_excess_returns</code> and <code>sd_excess_returns</code>. The result is now finally the <em>Sharpe ratio</em> and indicates how much more (or less) return the investment opportunity under consideration yields per unit of risk.</p>\n",
    "<p>The Sharpe Ratio is often <em>annualized</em> by multiplying it by the square root of the number of periods. We have used daily data as input, so we'll use the square root of the number of trading days (5 days, 52 weeks, minus a few holidays): √252</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Annual Sharpe Ratio'}>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate the daily sharpe ratio\n",
    "daily_sharpe_ratio = avg_excess_return.div(sd_excess_return)\n",
    "\n",
    "# annualize the sharpe ratio\n",
    "annual_factor = np.sqrt(252)\n",
    "annual_sharpe_ratio = daily_sharpe_ratio.mul(annual_factor)\n",
    "\n",
    "# plot the annualized sharpe ratio\n",
    "annual_sharpe_ratio.plot.bar(title = 'Annual Sharpe Ratio')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "<p>Given the two Sharpe ratios, which investment should we go for? In 2016, Amazon had a Sharpe ratio twice as high as Facebook. This means that an investment in Amazon returned twice as much compared to the S&amp;P 500 for each unit of risk an investor would have assumed. In other words, in risk-adjusted terms, the investment in Amazon would have been more attractive.</p>\n",
    "<p>This difference was mostly driven by differences in return rather than risk between Amazon and Facebook. The risk of choosing Amazon over FB (as measured by the standard deviation) was only slightly higher so that the higher Sharpe ratio for Amazon ends up higher mainly due to the higher average daily returns for Amazon. </p>\n",
    "<p>When faced with investment alternatives that offer both different returns and risks, the Sharpe Ratio helps to make a decision by adjusting the returns by the differences in risk and allows an investor to compare investment opportunities on equal terms, that is, on an 'apples-to-apples' basis.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
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}