Stock Market Portfolio Capstone Project .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "start=datetime.datetime(2015,1,1)\n",
    "end=datetime.datetime(2020,1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "AAPL=web.DataReader(\"AAPL\",'yahoo',start,end)\n",
    "CISCO=web.DataReader(\"CSCO\",'yahoo',start,end)\n",
    "IBM=web.DataReader(\"IBM\",'yahoo',start,end)\n",
    "AMZN=web.DataReader(\"AMZN\",'yahoo',start,end)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Normalize Prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalize return for all stocks in the list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for stock_df in (AAPL,CISCO,IBM,AMZN):\n",
    "    stock_df['Normalized Return']=stock_df['Close']/stock_df.iloc[0]['Close']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.  Creating a Portfolio\n",
    "#### Portfolio Allocation\n",
    "Our initial position is $1000000.\n",
    "We will assume we would like to allocate our money as follows:\n",
    "* 30% in Apple\n",
    "* 20% in Google/Alphabet\n",
    "* 40% in Amazon\n",
    "* 10% in IBM\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "for stock_df, allo in zip((AAPL,CISCO,IBM,AMZN),[.3,.2,.4,.1]):\n",
    "    stock_df['Allocation']=stock_df['Normalized Return']*allo\n",
    "for stock_df in (AAPL,CISCO,IBM,AMZN):\n",
    "    stock_df['Position Values']=stock_df['Allocation']*1000000 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Normed Return</th>\n",
       "      <th>Normalized Return</th>\n",
       "      <th>Allocation</th>\n",
       "      <th>Position Values</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>113.129997</td>\n",
       "      <td>110.209999</td>\n",
       "      <td>112.820000</td>\n",
       "      <td>110.379997</td>\n",
       "      <td>41403400.0</td>\n",
       "      <td>100.905785</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>300000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>111.440002</td>\n",
       "      <td>107.349998</td>\n",
       "      <td>111.389999</td>\n",
       "      <td>109.330002</td>\n",
       "      <td>53204600.0</td>\n",
       "      <td>99.945885</td>\n",
       "      <td>0.990487</td>\n",
       "      <td>0.990487</td>\n",
       "      <td>0.297146</td>\n",
       "      <td>297146.234512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>108.650002</td>\n",
       "      <td>105.410004</td>\n",
       "      <td>108.290001</td>\n",
       "      <td>106.250000</td>\n",
       "      <td>64285500.0</td>\n",
       "      <td>97.130241</td>\n",
       "      <td>0.962584</td>\n",
       "      <td>0.962584</td>\n",
       "      <td>0.288775</td>\n",
       "      <td>288775.147610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>107.430000</td>\n",
       "      <td>104.629997</td>\n",
       "      <td>106.540001</td>\n",
       "      <td>106.260002</td>\n",
       "      <td>65797100.0</td>\n",
       "      <td>97.139420</td>\n",
       "      <td>0.962674</td>\n",
       "      <td>0.962674</td>\n",
       "      <td>0.288802</td>\n",
       "      <td>288802.332253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>108.199997</td>\n",
       "      <td>106.699997</td>\n",
       "      <td>107.199997</td>\n",
       "      <td>107.750000</td>\n",
       "      <td>40105900.0</td>\n",
       "      <td>98.501518</td>\n",
       "      <td>0.976173</td>\n",
       "      <td>0.976173</td>\n",
       "      <td>0.292852</td>\n",
       "      <td>292851.973223</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close      Volume  \\\n",
       "Date                                                                     \n",
       "2014-12-31  113.129997  110.209999  112.820000  110.379997  41403400.0   \n",
       "2015-01-02  111.440002  107.349998  111.389999  109.330002  53204600.0   \n",
       "2015-01-05  108.650002  105.410004  108.290001  106.250000  64285500.0   \n",
       "2015-01-06  107.430000  104.629997  106.540001  106.260002  65797100.0   \n",
       "2015-01-07  108.199997  106.699997  107.199997  107.750000  40105900.0   \n",
       "\n",
       "             Adj Close  Normed Return  Normalized Return  Allocation  \\\n",
       "Date                                                                   \n",
       "2014-12-31  100.905785       1.000000           1.000000    0.300000   \n",
       "2015-01-02   99.945885       0.990487           0.990487    0.297146   \n",
       "2015-01-05   97.130241       0.962584           0.962584    0.288775   \n",
       "2015-01-06   97.139420       0.962674           0.962674    0.288802   \n",
       "2015-01-07   98.501518       0.976173           0.976173    0.292852   \n",
       "\n",
       "            Position Values  \n",
       "Date                         \n",
       "2014-12-31    300000.000000  \n",
       "2015-01-02    297146.234512  \n",
       "2015-01-05    288775.147610  \n",
       "2015-01-06    288802.332253  \n",
       "2015-01-07    292851.973223  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AAPL.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total Portfolio Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL Positions</th>\n",
       "      <th>CISCO Positions</th>\n",
       "      <th>IBM Positions</th>\n",
       "      <th>AMZN Positions</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>2014-12-31</th>\n",
       "      <td>300000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>400000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>297146.234512</td>\n",
       "      <td>198490.301317</td>\n",
       "      <td>404038.880809</td>\n",
       "      <td>99410.337666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>288775.147610</td>\n",
       "      <td>194536.303111</td>\n",
       "      <td>397681.356469</td>\n",
       "      <td>97370.709360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>288802.332253</td>\n",
       "      <td>194464.410739</td>\n",
       "      <td>389104.973696</td>\n",
       "      <td>95147.415092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>292851.973223</td>\n",
       "      <td>196261.678911</td>\n",
       "      <td>386561.956351</td>\n",
       "      <td>96155.955392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AAPL Positions  CISCO Positions  IBM Positions  AMZN Positions\n",
       "Date                                                                      \n",
       "2014-12-31   300000.000000    200000.000000  400000.000000   100000.000000\n",
       "2015-01-02   297146.234512    198490.301317  404038.880809    99410.337666\n",
       "2015-01-05   288775.147610    194536.303111  397681.356469    97370.709360\n",
       "2015-01-06   288802.332253    194464.410739  389104.973696    95147.415092\n",
       "2015-01-07   292851.973223    196261.678911  386561.956351    96155.955392"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_pos_vals=[AAPL['Position Values'],CISCO['Position Values'],\n",
    "              IBM['Position Values'],AMZN['Position Values']]\n",
    "portfolio_val=pd.concat(all_pos_vals,axis=1)\n",
    "portfolio_val.columns=['AAPL Positions','CISCO Positions','IBM Positions','AMZN Positions']\n",
    "portfolio_val.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL Positions</th>\n",
       "      <th>CISCO Positions</th>\n",
       "      <th>IBM Positions</th>\n",
       "      <th>AMZN Positions</th>\n",
       "      <th>Total Pos</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>2014-12-31</th>\n",
       "      <td>300000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>400000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>297146.234512</td>\n",
       "      <td>198490.301317</td>\n",
       "      <td>404038.880809</td>\n",
       "      <td>99410.337666</td>\n",
       "      <td>999085.754304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>288775.147610</td>\n",
       "      <td>194536.303111</td>\n",
       "      <td>397681.356469</td>\n",
       "      <td>97370.709360</td>\n",
       "      <td>978363.516549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>288802.332253</td>\n",
       "      <td>194464.410739</td>\n",
       "      <td>389104.973696</td>\n",
       "      <td>95147.415092</td>\n",
       "      <td>967519.131779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>292851.973223</td>\n",
       "      <td>196261.678911</td>\n",
       "      <td>386561.956351</td>\n",
       "      <td>96155.955392</td>\n",
       "      <td>971831.563877</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AAPL Positions  CISCO Positions  IBM Positions  AMZN Positions  \\\n",
       "Date                                                                         \n",
       "2014-12-31   300000.000000    200000.000000  400000.000000   100000.000000   \n",
       "2015-01-02   297146.234512    198490.301317  404038.880809    99410.337666   \n",
       "2015-01-05   288775.147610    194536.303111  397681.356469    97370.709360   \n",
       "2015-01-06   288802.332253    194464.410739  389104.973696    95147.415092   \n",
       "2015-01-07   292851.973223    196261.678911  386561.956351    96155.955392   \n",
       "\n",
       "                 Total Pos  \n",
       "Date                        \n",
       "2014-12-31  1000000.000000  \n",
       "2015-01-02   999085.754304  \n",
       "2015-01-05   978363.516549  \n",
       "2015-01-06   967519.131779  \n",
       "2015-01-07   971831.563877  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_val['Total Pos']=portfolio_val.sum(axis=1)\n",
    "portfolio_val.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Each Portfolio Value')"
      ]
     },
     "execution_count": 80,
     "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"
    },
    {
     "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": [
    "portfolio_val['Total Pos'].plot()\n",
    "plt.title('Total Portfolio Value')\n",
    "portfolio_val.drop('Total Pos',axis=1).plot(kind='line')\n",
    "plt.title('Each Portfolio Value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Portfolio Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Daily Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_val['Daily Return'] = portfolio_val['Total Pos'].pct_change(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cumulative Return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our return 1072480.6092575414 !\n"
     ]
    }
   ],
   "source": [
    "cum_ret = 1000000 * (portfolio_val['Total Pos'][-1]/portfolio_val['Total Pos'][0] -1 )\n",
    "print('Our cumulative return {} !'.format(cum_ret))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average Daily Return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0006497053666039799"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_val['Daily Return'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sharpe Ratio (Industry Standard for Performance Indicator)\n",
    "**Sharpe Ratio is a measure for calculating risk-adjusted return\n",
    "\n",
    "Sharpe ratio = (Mean portfolio return − Risk-free rate)/Standard deviation of portfolio return\n",
    "\n",
    "Annualized Sharpe Ratio = K-value * SR\n",
    "\n",
    "K-values for various sampling rates:\n",
    "* Daily = sqrt(252)\n",
    "* Weekly = sqrt(52)\n",
    "* Monthly = sqrt(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our sharpe ratio is 0.05483664956578312 and Annualized sharpe ratio is 0.8705048248983613\n"
     ]
    }
   ],
   "source": [
    "SR = portfolio_val['Daily Return'].mean()/portfolio_val['Daily Return'].std()\n",
    "ASR = (252**0.5)*SR\n",
    "print('Our sharpe ratio is {x} and Annualized sharpe ratio is {y}'.format(x=SR,y=ASR))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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