PYTHON_VAR_NOTEBOOK.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python VaR jupyter notebook\n",
    "\n",
    "Allows calculation of **historical VaR** for a variable range of user defined US equities. The lookback period is manually user defined. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import quandl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## User defined inputs below\n",
    "**tickers** is a user defined list containing the ticker symbols to be downloaded\n",
    "**start_date** defines the beginning of the lookback period for the historical VaR calculation\n",
    "**var_level** defines the quantile. For example, a VaR95 would have a var_level of 5\n",
    "**auth_token** defines your quandl API key, this is necessary for downloading the relevant stock data from quandl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tickers = [\"aapl\", \"tsla\", \"goog\", \"amzn\", \"fb\"]\n",
    "start_date = \"2017-01-01\"\n",
    "var_level = 5\n",
    "auth_token = \"your quandl API key here\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Importing data and correctly formatting the dataframe\n",
    "######################################################\n",
    "def get_data(tickers,start_date):\n",
    "    \n",
    "    #Importing data from quandl\n",
    "    quandl.ApiConfig.api_key = auth_token\n",
    "    df = quandl.get_table('WIKI/PRICES', \n",
    "                          ticker=tickers,\n",
    "                          qopts = { 'columns': ['ticker', 'date', 'adj_close'] },\n",
    "                          date = { 'gte': start_date}, \n",
    "                          paginate = True)\n",
    "\n",
    "    #Reformatting the dataframe and calculating returns\n",
    "    df = df.pivot_table(index='date', columns='ticker', values='adj_close', aggfunc='sum')\n",
    "    for column in df:\n",
    "        df[column] = df[column].pct_change()\n",
    "    df.dropna(axis=0, how='any', thresh=None, subset=None, inplace=True)\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Function for computing single asset historical VaR for each asset present in a dataframe\n",
    "def get_single_asset_var(df,var_level):\n",
    "    for column in df:\n",
    "        sorted_returns = sorted(df[column])\n",
    "   \n",
    "        var_result = np.percentile(sorted_returns, var_level)\n",
    "    \n",
    "        #Output histogram\n",
    "        plt.hist(sorted_returns,normed=True)\n",
    "        plt.xlabel('Returns')\n",
    "        plt.ylabel('Frequency')\n",
    "        plt.title(r'Histogram of Asset Returns', fontsize=18, fontweight='bold')\n",
    "        plt.axvline(x=var_result, color='r', linestyle='--', label='95% Confidence VaR: ' + \"{0:.2f}%\".format(var_result * 100))\n",
    "        plt.legend(loc='upper right', fontsize = 'x-small')\n",
    "        plt.show()  \n",
    "        print(column)\n",
    "        print(var_result)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Function for generating a correlation matrix for several assets contained in a dataframe\n",
    "def get_correlation_matrix(df):\n",
    "    global portfolio_correlation_matrix\n",
    "    portfolio_correlation_matrix = df.corr()\n",
    "    \n",
    "    plt.matshow(df.corr())\n",
    "    plt.show()\n",
    "    return portfolio_correlation_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ticker</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>FB</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>TSLA</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>2017-01-04</th>\n",
       "      <td>-0.001119</td>\n",
       "      <td>0.004657</td>\n",
       "      <td>0.015660</td>\n",
       "      <td>0.000967</td>\n",
       "      <td>0.046085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>0.005085</td>\n",
       "      <td>0.030732</td>\n",
       "      <td>0.016682</td>\n",
       "      <td>0.009048</td>\n",
       "      <td>-0.001057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>0.011148</td>\n",
       "      <td>0.019912</td>\n",
       "      <td>0.022707</td>\n",
       "      <td>0.015277</td>\n",
       "      <td>0.009967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>0.009160</td>\n",
       "      <td>0.001168</td>\n",
       "      <td>0.012074</td>\n",
       "      <td>0.000620</td>\n",
       "      <td>0.009912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>0.001008</td>\n",
       "      <td>-0.001280</td>\n",
       "      <td>-0.004404</td>\n",
       "      <td>-0.002306</td>\n",
       "      <td>-0.006097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ticker          AAPL      AMZN        FB      GOOG      TSLA\n",
       "date                                                        \n",
       "2017-01-04 -0.001119  0.004657  0.015660  0.000967  0.046085\n",
       "2017-01-05  0.005085  0.030732  0.016682  0.009048 -0.001057\n",
       "2017-01-06  0.011148  0.019912  0.022707  0.015277  0.009967\n",
       "2017-01-09  0.009160  0.001168  0.012074  0.000620  0.009912\n",
       "2017-01-10  0.001008 -0.001280 -0.004404 -0.002306 -0.006097"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = get_data(tickers,start_date)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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a2Xigg5mdBkxBN6kREVknVNpr6JroXsWfEtoJLnX3yYlGJiIiqag3EZhZK2BK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\nilFjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ca39d608d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL\n",
      "-0.0175664879346\n"
     ]
    },
    {
     "data": {
      "image/png": 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qZmsJNYOdon6i9+7uXyhodCIiUnD1JgJ3375YgYiISDIa8jwCSdqQIUlHICJl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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ca39810828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AMZN\n",
      "-0.0161928244393\n"
     ]
    },
    {
     "data": {
      "image/png": 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kcSk0Eaxxdye6FLWZbVW8kKRJ6tYtDCJScgpNBPeb2WSgtZmdAUxHN6kREWkS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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ca39cff2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FB\n",
      "-0.0185872662247\n"
     ]
    },
    {
     "data": {
      "image/png": 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iIlIuhR4RALQFPnP3W82sysx2cfe/JxWYVIC+fdOOQERKoNDuoz8n9BzaA7gV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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ca39db04e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GOOG\n",
      "-0.0201598108028\n"
     ]
    },
    {
     "data": {
      "image/png": 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170qRfKW6Xcf16wdnnQWffgq9e2++fuDAMGXw+OOPM3ToUA499FCOPfZY+vbtS+vWrVm/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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ca39d855f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TSLA\n",
      "-0.0347304239166\n"
     ]
    }
   ],
   "source": [
    "get_single_asset_var(df,var_level)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<matplotlib.figure.Figure at 0x2ca39d60b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ticker</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>FB</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ticker</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>AAPL</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.473742</td>\n",
       "      <td>0.493233</td>\n",
       "      <td>0.551452</td>\n",
       "      <td>0.306193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMZN</th>\n",
       "      <td>0.473742</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.549617</td>\n",
       "      <td>0.604353</td>\n",
       "      <td>0.247711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>0.493233</td>\n",
       "      <td>0.549617</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.691376</td>\n",
       "      <td>0.268537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>0.551452</td>\n",
       "      <td>0.604353</td>\n",
       "      <td>0.691376</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.272385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSLA</th>\n",
       "      <td>0.306193</td>\n",
       "      <td>0.247711</td>\n",
       "      <td>0.268537</td>\n",
       "      <td>0.272385</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ticker      AAPL      AMZN        FB      GOOG      TSLA\n",
       "ticker                                                  \n",
       "AAPL    1.000000  0.473742  0.493233  0.551452  0.306193\n",
       "AMZN    0.473742  1.000000  0.549617  0.604353  0.247711\n",
       "FB      0.493233  0.549617  1.000000  0.691376  0.268537\n",
       "GOOG    0.551452  0.604353  0.691376  1.000000  0.272385\n",
       "TSLA    0.306193  0.247711  0.268537  0.272385  1.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_correlation_matrix(df)"
   ]
  }
 ],
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