nifty-fifty-datascienceproject.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File written to NiftyFile.csv\n",
      "File written to Volatility_12042019\n",
      "data gathering complete\n",
      "select the company to be analyzed    \n",
      "1     ==>   Vodafone Idea Ltd.\n",
      "2     ==>   Tata Motors Ltd.\n",
      "3     ==>   DLF Ltd.\n",
      "4     ==>   Indiabulls Housing Finance Ltd.\n",
      "5     ==>   Yes Bank Ltd.\n",
      "6     ==>   Zee Entertainment Enterprises Ltd.\n",
      "7     ==>   Tata Motors Ltd DVR\n",
      "8     ==>   Hindustan Petroleum Corporation Ltd.\n",
      "9     ==>   Steel Authority of India Ltd.\n",
      "10     ==>   Vedanta Ltd.\n",
      "11     ==>   Motherson Sumi Systems Ltd.\n",
      "12     ==>   ICICI Prudential Life Insurance Company Ltd.\n",
      "13     ==>   InterGlobe Aviation Ltd.\n",
      "14     ==>   Bajaj Holdings & Investment Ltd.\n",
      "15     ==>   Indian Oil Corporation Ltd.\n",
      "16     ==>   Bharti Airtel Ltd.\n",
      "17     ==>   Bharat Petroleum Corporation Ltd.\n",
      "18     ==>   Ashok Leyland Ltd.\n",
      "19     ==>   General Insurance Corporation of India\n",
      "20     ==>   Bank of Baroda\n",
      "21     ==>   Bharat Heavy Electricals Ltd.\n",
      "22     ==>   The New India Assurance Company Ltd.\n",
      "23     ==>   Page Industries Ltd.\n",
      "1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd0f11a8278>"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 7200x3600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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C7DERkTUey3KQ05o1a1BUVIRZs2YBAK5fvw6tVou9e/di8eLFyMrKAgCEhoZiwoQJwvPz8/Mxffp0FBQUwNfXF46O9zfuZsyYgYCAAHTv3h3Tpk3DlStXEBoaiv79+2Pbtm2mxxMnTkS/fv3q5xcmosfSY7lbSU6DBw9Geno67t69CwDYuXMnevXqhXXr1qGyshJpaWnYunUrduzYgQMHDgjPX7hwIbp27Yr09HTMnTsXR44cEZZp3bo1Fi5ciDZt2iA1NRVRUVFmj1kMRGQtloON+fj4oG3btqYP/pSUFAwZMgTff/89hg0bBgcHB7i5uaF///74/vvvhecfPnwYw4YNAwC0atUK3bp1q9f8RESADXYr3bp1CzNnzsTp06eh0WgQExODoKAgYbnDhw8jKioK/v7+AICGDRti27Zt1r68Iul0OuzYsQN+fn4oLi5Gly5d5I5ERPRIrN5ySExMhJubG/bs2YN169Zh9uzZKC0ttbhs1W6P1NTUx7YYACAkJAQ//PADPvnkE+h0Ojg4OKBbt2748ssvIUkSSkpKkJ6eju7duwvPffnll/Hll18CAC5dumRx64KIqK5ZXQ4ZGRkICwsDAPj7+yMgIACZmZlWB1OzRo0aoXfv3khNTcXgwYMBAJMnT4YkSRg4cCBGjBiBQYMGoUePHsJz33vvPRw+fBj9+vXDggULEBgYWN/xiYis362k1+vh6+treuzt7Q2DwWBx2ezsbOh0Ojg5OWHUqFHQ6XQWlzMajTAajWbTHrROS+riXq1V663tbfkWLVqERYsWmR43btwYS5Yssbjs559/bvrZ09MTSUlJFper/vzAwEB89dVXD3xMRGSNGstBp9NBr9dbnHfo0KFav1CHDh1w4MABNGnSBJcuXcLYsWPh6elpcddKUlISEhISar3u36qr+6rW5f1ayX6o7UbzZJ9qLIeUlJSHzvfx8UFubi48PDwAAHl5eRZ3hbi5uZl+btWqFfr06YNjx45ZLIeIiAhhq8JgMCA8PLymuESKp7YbzZN9svqYg1arRXJyMoB7u42ysrLwyiuvCMtduXIFkiQBAIqKinDw4EG0a9fO4jrd3d3h5+dn9sfLy8vaqEREVEtWH3OIjIzEjBkzEBwcDEdHR8TFxZm2EuLj49GyZUuMHDkSu3fvxpYtW+Dk5ISKigoMHjwYffr0sfoXqCJJEq88+hBVxUxEVBtWl4OrqytWrVplcV71awGNHj0ao0ePtvblLHJxcUFhYSHv6fAAVTf7cXFxkTsKEanEY3FtJT8/P1y+fBkFBQVyR1GsqtuEEhHVxmNRDg0aNODtL4mIbIjXViIiIgHLgYiIBCwHIiISqOaYQ0XFvVt0PsplNIiI7F3VZ2bVZ2htqaYcqs5E4rekiYgeXUFBAZ588slaL+8gqeTbUbdv38apU6fQokULaDS2uVRA1SU5Nm/erOhvYDOnbTGnbakhpxoyAnWTs6KiAgUFBQgICHik7zqpZsvBxcWlzm6a4+XlpYrvADCnbTGnbakhpxoyArbP+ShbDFV4QJqIiAQsByIiErAciIhIoJk/f/58uUPIydnZGYGBgXB2dpY7ykMxp20xp22pIacaMgLKyamas5WIiKj+cLcSEREJWA5ERCRgORARkYDlQEREAtV8Q9qe3LlzB5988gl27twJvV4PjUaDtm3bIjIyEr169ZI7nolacgJAeno60tLSzHKOGzcO7dq1kzsa1QG1jE0l57Srs5WU/EZUN3PmTDg5OaFHjx7IyMiAv78//vSnP2HDhg0YOnSoYi4+qJacy5Ytw/nz5/HnP/8Ze/fuRadOneDh4YEvvvgC06dPh1arlTsiAPWMTzXkVMvYVHROyY7MmDFDmj17trR7925pypQpUnx8vJSeni7pdDpp06ZNcsczee2110w/37lzRxoxYoQkSZJUVFQk9e3bV65YArXk1Gq1UkVFhSRJklRaWiqNHj1akiRJys3NlQYMGCBnNDNqGZ9qyKmWsanknHa1W+nEiRNIT08HAAQFBWHMmDF4++230b17d4SFhSnmfxMajQZlZWVwdnZGSUkJysrKAABNmzZFgwYNZE53n1pyOjndH+YODg4oLi4GAPj4+MgVySK1jE815FTL2FRyTrsqByW/EdUNGDAAw4cPR5cuXXDw4EHTP7arV6/KnMycWnJ269YNUVFR6N69O/bt24egoCAAQElJySPfAKUuqWV8qiGnWsamknPa1TGH9evXIz093eyNGDNmDK5evYqxY8ciLS1N7ogmBw8exLlz59ChQwe8/PLLcsd5IDXkrKysxPbt2005hwwZAgcHB5SXl6O4uBjNmzeXOyIA9YxPteRUw9gElJvTrsoBUO4bQQSoZ3yqJSdZQdYjHvTIZs+eLXeEWlFLztWrV8sdgeqZWsam3Dn5Jbj/N2fOHLkj1Mp//vMfuSPUilpybt++Xe4ItaKW8amGnGoZm3LntKsD0g8j9xtRXbdu3SxOlyTJdKaNEqgl5+uvv25xuiRJKCwsrOc0v4+SxufDKCWnWsamknPaVTko+Y2oTpIkfPrpp2jSpIkwfeTIkTKlEqklZ3Z2NlasWIFGjRqZTZckCVOmTJEplUgt41MNOdUyNpWc067KQclvRHUBAQG4fv26xUs7eHp6ypDIMrXkfPbZZ+Hm5obOnTsL85Ry6iWgnvGphpxqGZuKzlmPxzdkFxkZKR06dMjivKFDh9ZzmgcrKyuT7t69K3eMGqklp8FgkIqLiy3Oq/rmtBKoZXyqIadaxqaSc9rVqazl5eXQaDTQaDRyRyESqGV8qiUnWceuykEtysrKsHHjRmRkZMBgMAAAvL29odVqERkZCRcXF5kT3qOWnACQmpqKjIwM5OXlAbifMzQ0FA4ODjKnI1tTy9hUck67KgclvxHVTZ06Fa6urhgxYoTp+j96vR5bt25FSUkJPvzwQ5kT3qOWnAsXLsTFixcxfPhweHt7AwDy8vLwr3/9C08++STmzp0rc8J71DI+1ZBTLWNTyTntqhyU/EZU17dvX3zzzTePPK++qSVnSEgIdu/eLUyvrKyEVqu1OE8OahmfasiplrGp5Jx2dbbS6dOnhb9sDw8PLFy4EH379pUplcjR0RGXLl1Cq1atzKbn5OQoaheIWnICQFFREZo1a2Y27caNG1DS/43UMj7VkFMtY1PJOe2qHJT8RlT37rvvYuTIkQgICICvry8AIDc3F6dOnUJcXJzM6e5TS87x48cjNDQUwcHBpv/p5ubm4ttvv8XkyZNlTnefWsanGnKqZWwqOadd7Vbat28f5s6d+8A3Qil3sQKAmzdvIjMz0+wA6iuvvILGjRvLnMycWnJeunQJu3fvNssZEhIifMDJSS3jUy051TI2lZrTrsoBUO4bQQSoZ3yqJSf9fnZXDmpQXl6OxMRE6PV69O7dG6+++qpp3oIFCxRzcTO15ASAr7/+Gnq9Hq+++qrZt1HXr1+PiRMnypiM6oJaxqaSc9rVVVnLy8uxdu1azJkzB999953ZvAULFsgTyoL58+fj559/RuvWrbF8+XIsWrTINO/YsWMyJjOnlpwffPABtm7diqtXr2LChAn49NNPTfN27dolX7DfUMv4VENOtYxNJee0q3JQ8htRXVZWFlauXImxY8di+/btyM3NxaxZsyBJkqLOrlFLzgMHDiApKQmzZ8/Gjh07kJGRgYSEBABQVE61jE815FTL2FRyTrsqByW/EdVVv6+xi4sLVq9ejVu3buHdd99FZWWljMnMqSUncP8Ce82bN0diYiIOHz6MFStWKObsGkA941MNOdUyNpWc067KQclvRHVPPPEEzp07Z3qs0WhMH2Tnz5+XMZk5teR0c3NDTk6O2eMNGzbg5MmT+Pnnn2VMZk4t41MNOdUyNhWdsz6u7qcUY8aMkc6ePWs2raKiQpo2bZrUrl07mVKJLl68KF2+fFmYXllZKX333XcyJLJMLTmPHTsmnT9/XpheVlYmbdq0SYZElqllfKohp1rGppJz2tXZStnZ2WjQoIHp3OwqkiQhMzMTPXv2lClZzQ4dOoTu3bvLHaNGasl54cIFtGnTRu4YZtQyPtWS87fUMjaVktOuysESpbwR1f3yyy/CtMjISHz88ceQJAlt27aVIZVILTlv3bolTOvfvz/S09MhSZJwhzglUeL4tERpOdUyNpWc067KQclvRHXt2rWDr6+v2cG9/Px8eHp6wsHBAd9++62M6e5TU04HBweLB0sdHBxw9uxZGVKJ1DI+1ZBTTWNTqTntqhyU/EZUl5CQgBMnTiA2NtZ0LaBevXph3759Miczp5acM2fOhKOjI2bOnAk3NzcAysyplvGphpxqGZuKzllPxzYUYfXq1dL48eOl3Nxc07SgoCAZEz3Y6dOnpbCwMOmLL76QJEmSevXqJXMiy9SSc9++fdLQoUOlAwcOSJKkzJxqGZ9qyamWsanUnHZ1Kuvf/vY3TJkyBVOnTsWWLVsAQFHnuVf37LPP4rPPPkNubi7++te/4s6dO3JHskgtOYOCgrBhwwbs2LEDMTExZqdjKoVaxqdacqplbCo1p13tVqpSXl6OVatW4dSpU/j111+RmZkpd6SH+umnn3DkyBFERUXJHeWh1JIzPT0dP/zwA+bNmyd3FIvUMj7VkhNQz9hUUk67LIcqSnojLLl+/brpNoxeXl74wx/+IHMiy9SSEwCMRiMAwN3dXeYkNVP6+Kyi5JxqGZtKzGmX5aDEN6K6nJwczJkzB2fOnEHLli0BAAUFBWjfvj1iY2Ph7+8vb8D/p5ac165dw/Lly5GRkWE6iOro6AitVotp06bBw8ND5oTmlD4+qyg5p1rGpqJzyni8o97973//k9544w2pS5cuUr9+/aR+/fpJXbt2ld544w3p4sWLcsczCQsLk1JTU6WKigrTtIqKCmnHjh3S8OHDZUxmTi05x40bJ61Zs0a6du2aaVphYaH0z3/+Uxo3bpyMycypZXyqIadaxqaSc9pVOSj5jaiub9++v2tefXsccoaEhNRjkodTy/hUQ87HYWzKndOuzlYqKirCoEGD4Oh4/9d2dHREaGgobty4IWMyc82aNcPOnTvNziOXJAlff/21ovaVqyWns7Mzjh8/Lkw/duwYGjZsKEMiy9QyPtWQUy1jU8k5nWR99XpW9Ub079/fdOqdJElIS0uT/Y2obsmSJZg3bx7i4uLg6ekJSZJw5coVtGvXDkuWLJE7nsn777+P+fPnm3IC974MpbScsbGxmD59Opydnc3ueVxWVoZly5bJnO4+tYxPNeSs+jcUGxsLLy8vAMocm7/NKUkS8vPz0b59e9lz2tUB6ezsbMybNw9nz54VPszmz5+P1q1by5zQ3LVr15CXl4ebN2/C2dkZrVu3Nn3DV0mqcgL37iWstAO8wL0Pr1OnTply+vj4oEOHDoo6P18t41MtOQHx35C/v79iCqy66jn9/Pzg7e0tdyT7KocqSv8wmzt3Lt555x14eHjg6NGjeOutt+Dh4YHCwkJ88MEH+Mtf/iJ3RABAYGAgBg4ciNdff93svsxKc/DgQfz5z38GABQXFyMuLg7Hjx/Hs88+i7lz5+KJJ56QOaE5pY/PKkrOuWfPHsTExKBly5ZYtmwZoqOj0ahRIxQWFuL9999Hr1695I4I4N4W7Lx583Dw4EEA906xvn37NkaOHImpU6fKu9uzvg9yKE1RUZHcEQQDBw40/TxmzBjpxIkTkiRJ0q+//irpdDq5YgmCgoKkRYsWSS+//LI0ePBg6fPPP1fk3+fgwYNNP8fGxkrz5s2T/vvf/0r/+Mc/pOjoaBmTqdu1a9ekM2fOSGfOnDE7E0wJQkNDpXPnzklHjhyRXnrpJeno0aOSJEnSL7/8IoWGhsqc7r7Ro0dLqampUlFRkfTZZ59J8fHx0tWrV6VZs2ZJ8+fPlzWbXR2QPnfuHIYMGYLXX38dFy5cQFRUFHr06IGePXsq5sqcAFBWVmb6ubS0FJ06dQIAPPXUU4r5aj0ANG3aFLNmzUJmZiYmTpyIzMxMvPrqq5gyZYrpf0JKIFXbOD569Cjee+89PP3005gyZQouXLggY7LaGzhwoNwRTHJychAREYGQkBBMmzYN06ZNQ0hICCIiIpDSC3vMAAAF8UlEQVSdnS13PJNnnnkGXbt2RePGjfHiiy8CgOLu4XHjxg0MGjQITZs2xZgxY5CZmYnmzZtjwYIFsv8bsqsD0gsXLsSbb76J4uJijB8/HlOmTMFHH32Effv2YenSpfj000/ljggA6NatG5YsWYLo6GgEBgYiPT0d/fr1w8GDB9GsWTO54wkaNGgArVYLrVaL/Px8pKSkYMGCBdi1a5fc0QDcu8zDhQsXIEkSHBwcTPeTBmB2xo3cLF0Ku8r169frMcnDTZ8+HaNGjcInn3xi+vurrKxEWloaYmJikJycLHPCe9d6unDhAoxGI27evImffvoJzz//PC5evKio62o5OTkhJycHf/zjH3Hq1CnTbiRHR0c4Ocn78WxX5VBaWorevXsDAOLj4zFo0CAA9y6Ru2rVKjmjmZk1axaWLVuGHj16oFmzZvj4448xffp0BAYGYvHixXLHM5EsHK7y9PTEpEmTMGnSJBkSWXb79m1ERUWZ8lZdXrqkpERR5TBgwADhUthVioqKZEhkWdWprNVVncq6du1amVKZe/vttzFy5Eg4Ojpi5cqViI+PR0FBAQwGA+bPny93PJO3334bw4cPR4sWLVBQUICVK1cCAK5evWra2pGNrDu16ln1fY3vvfee2bzq+/mVorS0VDp79qx0+vRpxe3TlSTJ4r1v1eTmzZtSTk6O3DFMevXqJRkMBovzevToUc9pHiwsLExKS0uTKisrTdMqKyul1NRUadiwYTIme7C7d+9KWVlZUkFBgdxRBDdu3JBOnjwpFRcXyx3FjF1tOfj6+qKkpARubm5YuHChabrBYFDkrSJdXV0VfRbQb+8hrDaNGjVCq1at5I5hEhISgtzcXNPpodUFBwfLkMiy334PB1Dmdwiq02g0CAgIkDuGRe7u7ujYsaPcMQR2eSrrb928eRO3b99W1Kl4REqn5FNZyXrK2eEqI1dXV0RERMgdg+iBlHS2UhUPDw906NABHTp0MBWDEnPS72NXu5XUcjYI2Se1jM8H5ZQkSVE5yTp2VQ5qORuE7JNaxqdacpJ17KocfH198cUXX1g84NezZ08ZEhHdp5bxqZacZB27OuZQdTaIJUo6G4Tsk1rGp1pyknV4thIREQnsasuBiIhqh+VAREQClgMREQns6mwlotro1asXrl69Co1GA41Gg7Zt2yI0NBRhYWE1Xqjv8uXL6N27N06fPi37VTWJrMHRS2TBunXr0L17dxQXF+PIkSNYtGgRTp48iffff1/uaET1gruViB6iSZMm6N27Nz788EOkpKTg559/xnfffYfBgwfjxRdfRM+ePbF69WrT8qNHjwYAdO3aFS+88AKOHz8OANi+fTtee+01dO3aFZGRkQ88FZRIKVgORLXQqVMneHl54ccff0SjRo2wdOlS/Pjjj1i/fj22bNmCvXv3AgA2bdoEAPjhhx9w/PhxvPDCC9i7dy/Wr1+PhIQEfP/99+jcuTP+/ve/y/nrENWI5UBUSy1btsSNGzcQGBiIZ555Bo6OjmjXrh369++PI0eOPPB5W7duRVRUFNq0aQMnJydMmjQJZ8+e5dYDKRqPORDVUn5+Ppo2bYoTJ05g+fLlOH/+PO7cuYPy8nJotdoHPk+v12Px4sVYunSpaZokScjPz1f9PTHo8cVyIKqFkydPIj8/H507d8abb76J0aNHY+PGjXB2dsaiRYtMVyN1cHAQnuvt7Y1JkyYJt9YkUjLuViJ6iJKSEuzfvx9Tp07FoEGD8Mwzz6C0tBRNmzaFs7MzTp48iZ07d5qW9/DwgKOjIy5dumSaNmLECHz00Uc4f/48AKC4uBgZGRn1/rsQPQpeW4noN6p/z8HR0RFt27bFoEGDMGLECGg0GuzatQtLly5FUVERXnrpJfj6+sJoNGL58uUAgPj4eGzZsgV3797Fxo0b8fzzz2PHjh1ITExEbm4umjRpgu7du/O0WFI0lgMREQm4W4mIiAQsByIiErAciIhIwHIgIiIBy4GIiAQsByIiErAciIhIwHIgIiIBy4GIiAT/BzZW2cDbKoMZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 455.35x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "import requests\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "##########################################################################################\n",
    "timestr = time.strftime(\"%Y%m%d\")\n",
    "mydate=time.strftime(\"%d%m%Y\")\n",
    "s = mydate\n",
    "date = datetime.strptime(s, \"%d%m%Y\")\n",
    "if datetime.now().strftime('%A')=='Saturday':\n",
    "    modified_date = date + timedelta(days=-1)\n",
    "elif datetime.now().strftime('%A')=='Sunday': \n",
    "    modified_date = date + timedelta(days=-2)\n",
    "else:\n",
    "    modified_date=datetime.now()\n",
    "correct_date=datetime.strftime(modified_date, \"%d%m%Y\")\n",
    "\n",
    "def data_Downloader(url,filename):\n",
    "    data=requests.get(url)\n",
    "    if data.status_code==200:\n",
    "        datacsv=data.text\n",
    "    \n",
    "        with open(filename,'w') as handler:\n",
    "            handler.write(datacsv)\n",
    "        print('File written to '+ filename)\n",
    "        return 'success'\n",
    "    else:\n",
    "        return 'failed'\n",
    "\n",
    "niftyfileUrl='https://www.nseindia.com/content/indices/ind_nifty100list.csv'\n",
    "\n",
    "NiftyFile='NiftyFile.csv'\n",
    "niftyout=data_Downloader(niftyfileUrl,NiftyFile)\n",
    "\n",
    "volatility='https://www.nseindia.com/archives/nsccl/volt/CMVOLT_'+ correct_date +'.CSV'\n",
    "\n",
    "volatilityFile='Volatility_'+correct_date\n",
    "\n",
    "volout=data_Downloader(volatility,volatilityFile)\n",
    "\n",
    "if niftyout=='success' and volout=='success':\n",
    "    print( 'data gathering complete')\n",
    "else:\n",
    "    print('can not gather data , exiting ')\n",
    "    sys.exit()\n",
    "#reading both files\n",
    "df1 = pd.read_csv(NiftyFile, header=0 )\n",
    "df2 = pd.read_csv(volatilityFile, header=0)\n",
    "#merging both files\n",
    "df = pd.merge(df1, df2, on='Symbol')\n",
    "df = df.sort_values(by=['Underlying Annualised Volatility (F) = E*Sqrt(365)', 'Previous Day Underlying Volatility (D)','Current Day Underlying Daily Volatility (E) = Sqrt(0.94*D*D + 0.06*C*C)'])\n",
    "df.loc[:,'Previous Day Underlying Volatility (D)'] *= 100\n",
    "df.loc[:,'Current Day Underlying Daily Volatility (E) = Sqrt(0.94*D*D + 0.06*C*C)'] *= 100\n",
    "df.loc[:,'Underlying Annualised Volatility (F) = E*Sqrt(365)'] *= 100\n",
    "df=df.loc[df['Current Day Underlying Daily Volatility (E) = Sqrt(0.94*D*D + 0.06*C*C)'] >= 2]\n",
    "df = df.sort_values('Current Day Underlying Daily Volatility (E) = Sqrt(0.94*D*D + 0.06*C*C)',ascending=[False])\n",
    "df.rename(columns={'Previous Day Underlying Volatility (D)': 'previous_volatality', 'Current Day Underlying Daily Volatility (E) = Sqrt(0.94*D*D + 0.06*C*C)': 'current_volatality','Underlying Annualised Volatility (F) = E*Sqrt(365)':'annual volatality','Underlying Previous Day Close Price (B)':'previous close','Underlying Close Price (A)':'current close','Company Name':'company_name'}, inplace=True)\n",
    "df['Date']=s\n",
    "df1=df[['company_name','Date','current close','previous close','previous_volatality','current_volatality']]\n",
    "df1['close_diff']=df1['current close']-df1['previous close']\n",
    "df1['voldiff']=df1['current_volatality']-df1['previous_volatality']\n",
    "completedf=df1.drop(['current close','previous close','previous_volatality','current_volatality'],axis=1)\n",
    "#saving the data in a file \n",
    "mainfile='completeData.csv'\n",
    "def check_availability(element, collection: iter):\n",
    "    return True\n",
    "if os.path.isfile(mainfile) and os.stat(mainfile).st_size != 0:\n",
    "    with open(mainfile,'a') as handler:\n",
    "        if not check_availability(int(completedf.Date.unique()[0]),list(pd.read_csv(mainfile).Date.unique())):\n",
    "            completedf.to_csv(handler,sep=',',index=False,header=None)\n",
    "elif os.path.isfile(mainfile) and os.stat(mainfile).st_size == 0:\n",
    "    with open(mainfile,'a') as handler:\n",
    "         completedf.to_csv(handler,sep=',',index=False)\n",
    "elif not os.path.isfile(mainfile):\n",
    "    with open(mainfile,'a') as handler:\n",
    "         completedf.to_csv(handler,sep=',',index=False)\n",
    "else:\n",
    "        print('data already exist')\n",
    "#reading the main file now for analysis\n",
    "maindataFrameAnalysis=pd.read_csv(mainfile,header=0)\n",
    "company_names=maindataFrameAnalysis.company_name.unique()\n",
    "company_names=list(company_names)\n",
    "print('select the company to be analyzed    ')\n",
    "for index,values in enumerate(list(company_names)):\n",
    "    print (str(index+1 )+  '     ==>   '  +values)\n",
    "company=int(input().strip())-1\n",
    "company_selected=company_names[company]\n",
    "companydf=maindataFrameAnalysis.loc[maindataFrameAnalysis['company_name']==company_selected]\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(100, 50))\n",
    "sns.set(style=\"ticks\", color_codes=True)\n",
    "companydf.plot(x='Date',y='close_diff',kind='bar')\n",
    "companydf.plot(x='Date',y='voldiff',kind='bar')\n",
    "#linear regression plot\n",
    "#removing date column\n",
    "#showing the relation between volatility difference and the closing difference from previous date.\n",
    "#this is a linear regresssion plot to show the stock market value for a company real time data gathering\n",
    "\n",
    "\n",
    "nondatecompanydf=companydf.drop(['Date'],axis=1)\n",
    "g = sns.pairplot(nondatecompanydf, kind=\"reg\")\n",
    "sns.regplot(companydf.close_diff,companydf.voldiff)\n",
    "sns.lmplot(x='close_diff',y='voldiff',data=companydf, hue=\"Date\")\n",
    "sns.distplot(companydf.close_diff)\n",
    "sns.distplot(companydf.voldiff,bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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