overnight returns for the Amazon and Apple stock.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "52730901",
   "metadata": {},
   "source": [
    "  Usually we think, Stock market movement majorly occurs during day but it is the other way around.So here we will analyse overnight returns for the amazon and apple stocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3f393eaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "## importing important libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import yfinance as yf ## for accessing stock data\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b3950ab2",
   "metadata": {},
   "outputs": [],
   "source": [
    "## timing of investment\n",
    "start_date = datetime.date(2000,1, 11)\n",
    "end_date = datetime.datetime.today()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d4b9a8c",
   "metadata": {},
   "source": [
    "We access Apple and amazon data using yfinance library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8af0c54c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "amazon = yf.download('AMZN', start= start_date, end= end_date)\n",
    "apple = yf.download('AAPL', start= start_date, end= end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "487a1ba4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>72.5625</td>\n",
       "      <td>72.6250</td>\n",
       "      <td>65.5625</td>\n",
       "      <td>69.1875</td>\n",
       "      <td>69.1875</td>\n",
       "      <td>14757900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>66.8750</td>\n",
       "      <td>70.0000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>66.7500</td>\n",
       "      <td>66.7500</td>\n",
       "      <td>10532700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>67.8750</td>\n",
       "      <td>68.0000</td>\n",
       "      <td>63.0000</td>\n",
       "      <td>63.5625</td>\n",
       "      <td>63.5625</td>\n",
       "      <td>10804500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-13</th>\n",
       "      <td>64.9375</td>\n",
       "      <td>67.1875</td>\n",
       "      <td>63.1250</td>\n",
       "      <td>65.9375</td>\n",
       "      <td>65.9375</td>\n",
       "      <td>10448100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-14</th>\n",
       "      <td>66.7500</td>\n",
       "      <td>68.6250</td>\n",
       "      <td>64.0000</td>\n",
       "      <td>64.2500</td>\n",
       "      <td>64.2500</td>\n",
       "      <td>6853600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close  Adj Close    Volume\n",
       "Date                                                               \n",
       "2000-01-10  72.5625  72.6250  65.5625  69.1875    69.1875  14757900\n",
       "2000-01-11  66.8750  70.0000  65.0000  66.7500    66.7500  10532700\n",
       "2000-01-12  67.8750  68.0000  63.0000  63.5625    63.5625  10804500\n",
       "2000-01-13  64.9375  67.1875  63.1250  65.9375    65.9375  10448100\n",
       "2000-01-14  66.7500  68.6250  64.0000  64.2500    64.2500   6853600"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## visualization of datasets\n",
    "amazon.head()\n",
    "#apple_data()  ##remove # to see apple  data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e5cfe0c",
   "metadata": {},
   "source": [
    "Now we will calculate calculate the normalized return which is equal to ratio of adjusted closing prices of two consecutive days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ce1e632e",
   "metadata": {},
   "outputs": [],
   "source": [
    "## the normalized return \n",
    "apple['norm_return'] = apple['Adj Close'] / apple.iloc[0]['Adj Close']\n",
    "amazon['norm_return'] = amazon['Adj Close'] / amazon.iloc[0]['Adj Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "59434229",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2000-01-10      1.000000\n",
       "2000-01-11      0.948849\n",
       "2000-01-12      0.891944\n",
       "2000-01-13      0.989769\n",
       "2000-01-14      1.027493\n",
       "                 ...    \n",
       "2022-01-05    233.762522\n",
       "2022-01-06    229.860245\n",
       "2022-01-07    230.087430\n",
       "2022-01-10    230.114164\n",
       "2022-01-11    233.976350\n",
       "Name: norm_return, Length: 5538, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## visualozing data\n",
    "apple.norm_return\n",
    "#amazon.norm_return   ##remove # to see amazon data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e864e012",
   "metadata": {},
   "source": [
    "Here we will allocate 50% of investment in apple and amazon(diversifying our portfolio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0402fbf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "allocation = 0.5\n",
    "apple['allocation'] = apple['norm_return']* allocation\n",
    "amazon['allocation']= amazon['norm_return']*allocation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6fdb653c",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Suppose we invest 25000$ each in both the stocks.\n",
    "Investment = 50000\n",
    "## our daily_sum will be allocated size * investment in each stock\n",
    "apple['daily_sum'] = apple['allocation']*(Investment)\n",
    "amazon['daily_sum'] = amazon['allocation']*(Investment)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "925fd08a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#cleaning the data\n",
    "amazon.drop(['High','Low','Close', 'Volume'],axis=1,inplace=True)\n",
    "apple.drop(['High','Low','Close', 'Volume'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "12d25a9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Open</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>norm_return</th>\n",
       "      <th>allocation</th>\n",
       "      <th>daily_sum</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>0.910714</td>\n",
       "      <td>0.748281</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>25000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>0.856585</td>\n",
       "      <td>0.710005</td>\n",
       "      <td>0.948849</td>\n",
       "      <td>0.474425</td>\n",
       "      <td>23721.225263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>0.848214</td>\n",
       "      <td>0.667424</td>\n",
       "      <td>0.891944</td>\n",
       "      <td>0.445972</td>\n",
       "      <td>22298.598749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-13</th>\n",
       "      <td>0.843610</td>\n",
       "      <td>0.740625</td>\n",
       "      <td>0.989769</td>\n",
       "      <td>0.494885</td>\n",
       "      <td>24744.234299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-14</th>\n",
       "      <td>0.892857</td>\n",
       "      <td>0.768853</td>\n",
       "      <td>1.027493</td>\n",
       "      <td>0.513747</td>\n",
       "      <td>25687.330991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open  Adj Close  norm_return  allocation     daily_sum\n",
       "Date                                                                  \n",
       "2000-01-10  0.910714   0.748281     1.000000    0.500000  25000.000000\n",
       "2000-01-11  0.856585   0.710005     0.948849    0.474425  23721.225263\n",
       "2000-01-12  0.848214   0.667424     0.891944    0.445972  22298.598749\n",
       "2000-01-13  0.843610   0.740625     0.989769    0.494885  24744.234299\n",
       "2000-01-14  0.892857   0.768853     1.027493    0.513747  25687.330991"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#visualizing the numerical data\n",
    "amazon.head()\n",
    "apple.head()   ##remove # to see apple data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "be7c4f22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## now we will visualize plot data to see how our individual stocks performed.\n",
    "plt.style.use('seaborn')\n",
    "apple['daily_sum'].plot(figsize=(10,8), label = 'apple', color = 'r')\n",
    "amazon['daily_sum'].plot(figsize=(10,8), label = 'amazon')\n",
    "plt.legend()\n",
    "plt.ylabel('daily_sum($)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a256abe2",
   "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_daily_sum</th>\n",
       "      <th>apple_daily_sum</th>\n",
       "      <th>total_sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>1.187765e+06</td>\n",
       "      <td>5.844063e+06</td>\n",
       "      <td>7.031828e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-06</th>\n",
       "      <td>1.179794e+06</td>\n",
       "      <td>5.746506e+06</td>\n",
       "      <td>6.926300e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-07</th>\n",
       "      <td>1.174735e+06</td>\n",
       "      <td>5.752186e+06</td>\n",
       "      <td>6.926921e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-10</th>\n",
       "      <td>1.167017e+06</td>\n",
       "      <td>5.752854e+06</td>\n",
       "      <td>6.919871e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-11</th>\n",
       "      <td>1.195028e+06</td>\n",
       "      <td>5.849409e+06</td>\n",
       "      <td>7.044437e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            amazon_daily_sum  apple_daily_sum     total_sum\n",
       "Date                                                       \n",
       "2022-01-05      1.187765e+06     5.844063e+06  7.031828e+06\n",
       "2022-01-06      1.179794e+06     5.746506e+06  6.926300e+06\n",
       "2022-01-07      1.174735e+06     5.752186e+06  6.926921e+06\n",
       "2022-01-10      1.167017e+06     5.752854e+06  6.919871e+06\n",
       "2022-01-11      1.195028e+06     5.849409e+06  7.044437e+06"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## visulazing data for our portfoilo(new dataframe), ( by adding daily_sum of both stocks)\n",
    "daily_sum = [amazon['daily_sum'], apple['daily_sum']]\n",
    "portfolio = pd.concat(daily_sum, axis=1)\n",
    "portfolio.columns = ['amazon_daily_sum', 'apple_daily_sum']\n",
    "portfolio['total_sum']  = amazon['daily_sum']+ apple['daily_sum']\n",
    "portfolio.tail()\n",
    "\n",
    "## here we can see amazon went down a bit but peroformed well thats why diversification of our portfolio is very important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "99597b97",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## now we will visualize plot data to see how our portfolio performed.\n",
    "plt.style.use('fivethirtyeight')\n",
    "portfolio['total_sum'].plot(figsize=(8,6), label = 'Portfolio Equity=7000000')\n",
    "plt.legend()\n",
    "plt.ylabel('Portfolio')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "877428a4",
   "metadata": {},
   "source": [
    "Now we will calculate sharpe ratio : ( average return - risk free rate) / volatility risk,\n",
    "for that we need to know total daily return rate.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "da5b2896",
   "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_daily_sum</th>\n",
       "      <th>apple_daily_sum</th>\n",
       "      <th>total_sum</th>\n",
       "      <th>total return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-10</th>\n",
       "      <td>25000.000000</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-13</th>\n",
       "      <td>25108.063235</td>\n",
       "      <td>25534.108845</td>\n",
       "      <td>50642.172080</td>\n",
       "      <td>0.012843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-14</th>\n",
       "      <td>24817.858114</td>\n",
       "      <td>25189.312833</td>\n",
       "      <td>50007.170948</td>\n",
       "      <td>-0.012539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-15</th>\n",
       "      <td>24719.354512</td>\n",
       "      <td>25081.364324</td>\n",
       "      <td>49800.718836</td>\n",
       "      <td>-0.004128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-16</th>\n",
       "      <td>24930.700356</td>\n",
       "      <td>25395.546731</td>\n",
       "      <td>50326.247087</td>\n",
       "      <td>0.010553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            amazon_daily_sum  apple_daily_sum     total_sum  total return\n",
       "Date                                                                     \n",
       "2020-01-10      25000.000000     25000.000000  50000.000000           NaN\n",
       "2020-01-13      25108.063235     25534.108845  50642.172080      0.012843\n",
       "2020-01-14      24817.858114     25189.312833  50007.170948     -0.012539\n",
       "2020-01-15      24719.354512     25081.364324  49800.718836     -0.004128\n",
       "2020-01-16      24930.700356     25395.546731  50326.247087      0.010553"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio['total return'] = portfolio['total_sum'].pct_change(1)\n",
    "portfolio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a7e9c021",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.07978321245637708\n"
     ]
    }
   ],
   "source": [
    "## using sharpe ratio formula \n",
    "Sharpe_Ratio = portfolio['total return'].mean() / portfolio['total return'].std()\n",
    "print(Sharpe_Ratio)\n",
    "## this is daily Sharpe ratio , for annually sharpe ratio we need to multiply it with total trading days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4d1bdd3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10240fce",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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