Stock Clustering Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stock Clustering in K-Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating dataframe with price of 30 different stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"data/Small Cap\"\n",
    "filenames = glob.glob(path + \"/*.csv\")\n",
    "dfs = []\n",
    "for filename in filenames:\n",
    "    dfs.append(pd.read_csv(filename))\n",
    "\n",
    "stocks = pd.DataFrame()\n",
    "for df in dfs:\n",
    "    stocks[df[\"Symbol\"][0]] = df[\"Close Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>BAJAJELEC</th>\n",
       "      <th>BOMDYEING</th>\n",
       "      <th>CENTURYPLY</th>\n",
       "      <th>IDFC</th>\n",
       "      <th>JETAIRWAYS</th>\n",
       "      <th>NCC</th>\n",
       "      <th>PVR</th>\n",
       "      <th>RAYMOND</th>\n",
       "      <th>VIPIND</th>\n",
       "      <th>WELSPUNIND</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>341.15</td>\n",
       "      <td>82.20</td>\n",
       "      <td>266.65</td>\n",
       "      <td>63.70</td>\n",
       "      <td>527.50</td>\n",
       "      <td>97.35</td>\n",
       "      <td>1524.95</td>\n",
       "      <td>772.80</td>\n",
       "      <td>199.20</td>\n",
       "      <td>90.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>347.00</td>\n",
       "      <td>83.40</td>\n",
       "      <td>266.10</td>\n",
       "      <td>64.25</td>\n",
       "      <td>534.15</td>\n",
       "      <td>100.40</td>\n",
       "      <td>1539.00</td>\n",
       "      <td>785.00</td>\n",
       "      <td>200.90</td>\n",
       "      <td>90.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>349.85</td>\n",
       "      <td>82.25</td>\n",
       "      <td>264.85</td>\n",
       "      <td>63.35</td>\n",
       "      <td>528.90</td>\n",
       "      <td>101.45</td>\n",
       "      <td>1530.30</td>\n",
       "      <td>783.65</td>\n",
       "      <td>199.65</td>\n",
       "      <td>88.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>334.10</td>\n",
       "      <td>78.95</td>\n",
       "      <td>260.35</td>\n",
       "      <td>61.50</td>\n",
       "      <td>522.70</td>\n",
       "      <td>97.00</td>\n",
       "      <td>1515.05</td>\n",
       "      <td>746.95</td>\n",
       "      <td>193.30</td>\n",
       "      <td>86.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>336.20</td>\n",
       "      <td>77.15</td>\n",
       "      <td>254.05</td>\n",
       "      <td>61.15</td>\n",
       "      <td>520.40</td>\n",
       "      <td>95.45</td>\n",
       "      <td>1513.55</td>\n",
       "      <td>723.10</td>\n",
       "      <td>195.95</td>\n",
       "      <td>85.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BAJAJELEC  BOMDYEING  CENTURYPLY   IDFC  JETAIRWAYS     NCC      PVR  \\\n",
       "0     341.15      82.20      266.65  63.70      527.50   97.35  1524.95   \n",
       "1     347.00      83.40      266.10  64.25      534.15  100.40  1539.00   \n",
       "2     349.85      82.25      264.85  63.35      528.90  101.45  1530.30   \n",
       "3     334.10      78.95      260.35  61.50      522.70   97.00  1515.05   \n",
       "4     336.20      77.15      254.05  61.15      520.40   95.45  1513.55   \n",
       "\n",
       "   RAYMOND  VIPIND  WELSPUNIND  \n",
       "0   772.80  199.20       90.25  \n",
       "1   785.00  200.90       90.65  \n",
       "2   783.65  199.65       88.85  \n",
       "3   746.95  193.30       86.90  \n",
       "4   723.10  195.95       85.05  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"data/Mid Cap\"\n",
    "filenames = glob.glob(path + \"/*.csv\")\n",
    "dfs = []\n",
    "for filename in filenames:\n",
    "    dfs.append(pd.read_csv(filename))\n",
    "\n",
    "for df in dfs:\n",
    "    stocks[df[\"Symbol\"][0]] = df[\"Close Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BAJAJELEC</th>\n",
       "      <th>BOMDYEING</th>\n",
       "      <th>CENTURYPLY</th>\n",
       "      <th>IDFC</th>\n",
       "      <th>JETAIRWAYS</th>\n",
       "      <th>NCC</th>\n",
       "      <th>PVR</th>\n",
       "      <th>RAYMOND</th>\n",
       "      <th>VIPIND</th>\n",
       "      <th>WELSPUNIND</th>\n",
       "      <th>ADANIPOWER</th>\n",
       "      <th>CASTROLIND</th>\n",
       "      <th>DHFL</th>\n",
       "      <th>EXIDEIND</th>\n",
       "      <th>MINDTREE</th>\n",
       "      <th>RBLBANK</th>\n",
       "      <th>RELINFRA</th>\n",
       "      <th>SUNTV</th>\n",
       "      <th>TATAPOWER</th>\n",
       "      <th>VOLTAS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>341.15</td>\n",
       "      <td>82.20</td>\n",
       "      <td>266.65</td>\n",
       "      <td>63.70</td>\n",
       "      <td>527.50</td>\n",
       "      <td>97.35</td>\n",
       "      <td>1524.95</td>\n",
       "      <td>772.80</td>\n",
       "      <td>199.20</td>\n",
       "      <td>90.25</td>\n",
       "      <td>30.25</td>\n",
       "      <td>449.85</td>\n",
       "      <td>431.4</td>\n",
       "      <td>245.80</td>\n",
       "      <td>509.45</td>\n",
       "      <td>563.90</td>\n",
       "      <td>605.40</td>\n",
       "      <td>912.20</td>\n",
       "      <td>83.55</td>\n",
       "      <td>431.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>347.00</td>\n",
       "      <td>83.40</td>\n",
       "      <td>266.10</td>\n",
       "      <td>64.25</td>\n",
       "      <td>534.15</td>\n",
       "      <td>100.40</td>\n",
       "      <td>1539.00</td>\n",
       "      <td>785.00</td>\n",
       "      <td>200.90</td>\n",
       "      <td>90.65</td>\n",
       "      <td>32.85</td>\n",
       "      <td>443.00</td>\n",
       "      <td>1009.9</td>\n",
       "      <td>244.70</td>\n",
       "      <td>511.30</td>\n",
       "      <td>562.60</td>\n",
       "      <td>611.05</td>\n",
       "      <td>894.70</td>\n",
       "      <td>83.85</td>\n",
       "      <td>432.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>349.85</td>\n",
       "      <td>82.25</td>\n",
       "      <td>264.85</td>\n",
       "      <td>63.35</td>\n",
       "      <td>528.90</td>\n",
       "      <td>101.45</td>\n",
       "      <td>1530.30</td>\n",
       "      <td>783.65</td>\n",
       "      <td>199.65</td>\n",
       "      <td>88.85</td>\n",
       "      <td>33.10</td>\n",
       "      <td>439.55</td>\n",
       "      <td>1055.2</td>\n",
       "      <td>243.20</td>\n",
       "      <td>506.35</td>\n",
       "      <td>564.25</td>\n",
       "      <td>605.25</td>\n",
       "      <td>887.05</td>\n",
       "      <td>85.35</td>\n",
       "      <td>430.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>334.10</td>\n",
       "      <td>78.95</td>\n",
       "      <td>260.35</td>\n",
       "      <td>61.50</td>\n",
       "      <td>522.70</td>\n",
       "      <td>97.00</td>\n",
       "      <td>1515.05</td>\n",
       "      <td>746.95</td>\n",
       "      <td>193.30</td>\n",
       "      <td>86.90</td>\n",
       "      <td>31.90</td>\n",
       "      <td>433.95</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>239.85</td>\n",
       "      <td>492.90</td>\n",
       "      <td>552.15</td>\n",
       "      <td>578.65</td>\n",
       "      <td>871.35</td>\n",
       "      <td>83.75</td>\n",
       "      <td>414.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>336.20</td>\n",
       "      <td>77.15</td>\n",
       "      <td>254.05</td>\n",
       "      <td>61.15</td>\n",
       "      <td>520.40</td>\n",
       "      <td>95.45</td>\n",
       "      <td>1513.55</td>\n",
       "      <td>723.10</td>\n",
       "      <td>195.95</td>\n",
       "      <td>85.05</td>\n",
       "      <td>32.40</td>\n",
       "      <td>437.10</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>238.15</td>\n",
       "      <td>497.50</td>\n",
       "      <td>555.45</td>\n",
       "      <td>569.55</td>\n",
       "      <td>852.40</td>\n",
       "      <td>84.15</td>\n",
       "      <td>415.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BAJAJELEC  BOMDYEING  CENTURYPLY   IDFC  JETAIRWAYS     NCC      PVR  \\\n",
       "0     341.15      82.20      266.65  63.70      527.50   97.35  1524.95   \n",
       "1     347.00      83.40      266.10  64.25      534.15  100.40  1539.00   \n",
       "2     349.85      82.25      264.85  63.35      528.90  101.45  1530.30   \n",
       "3     334.10      78.95      260.35  61.50      522.70   97.00  1515.05   \n",
       "4     336.20      77.15      254.05  61.15      520.40   95.45  1513.55   \n",
       "\n",
       "   RAYMOND  VIPIND  WELSPUNIND  ADANIPOWER  CASTROLIND    DHFL  EXIDEIND  \\\n",
       "0   772.80  199.20       90.25       30.25      449.85   431.4    245.80   \n",
       "1   785.00  200.90       90.65       32.85      443.00  1009.9    244.70   \n",
       "2   783.65  199.65       88.85       33.10      439.55  1055.2    243.20   \n",
       "3   746.95  193.30       86.90       31.90      433.95  1070.0    239.85   \n",
       "4   723.10  195.95       85.05       32.40      437.10  1050.0    238.15   \n",
       "\n",
       "   MINDTREE  RBLBANK  RELINFRA   SUNTV  TATAPOWER  VOLTAS  \n",
       "0    509.45   563.90    605.40  912.20      83.55  431.85  \n",
       "1    511.30   562.60    611.05  894.70      83.85  432.45  \n",
       "2    506.35   564.25    605.25  887.05      85.35  430.20  \n",
       "3    492.90   552.15    578.65  871.35      83.75  414.10  \n",
       "4    497.50   555.45    569.55  852.40      84.15  415.75  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"data/Large Cap\"\n",
    "filenames = glob.glob(path + \"/*.csv\")\n",
    "dfs = []\n",
    "for filename in filenames:\n",
    "    dfs.append(pd.read_csv(filename))\n",
    "\n",
    "for df in dfs:\n",
    "    stocks[df[\"Symbol\"][0]] = df[\"Close Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>BAJAJELEC</th>\n",
       "      <th>BOMDYEING</th>\n",
       "      <th>CENTURYPLY</th>\n",
       "      <th>IDFC</th>\n",
       "      <th>JETAIRWAYS</th>\n",
       "      <th>NCC</th>\n",
       "      <th>PVR</th>\n",
       "      <th>RAYMOND</th>\n",
       "      <th>VIPIND</th>\n",
       "      <th>WELSPUNIND</th>\n",
       "      <th>...</th>\n",
       "      <th>AXISBANK</th>\n",
       "      <th>CIPLA</th>\n",
       "      <th>HDFCBANK</th>\n",
       "      <th>HINDUNILVR</th>\n",
       "      <th>INFRATEL</th>\n",
       "      <th>INFY</th>\n",
       "      <th>ITC</th>\n",
       "      <th>LT</th>\n",
       "      <th>RELIANCE</th>\n",
       "      <th>TCS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>341.15</td>\n",
       "      <td>82.20</td>\n",
       "      <td>266.65</td>\n",
       "      <td>63.70</td>\n",
       "      <td>527.50</td>\n",
       "      <td>97.35</td>\n",
       "      <td>1524.95</td>\n",
       "      <td>772.80</td>\n",
       "      <td>199.20</td>\n",
       "      <td>90.25</td>\n",
       "      <td>...</td>\n",
       "      <td>500.1</td>\n",
       "      <td>569.00</td>\n",
       "      <td>1553.40</td>\n",
       "      <td>983.25</td>\n",
       "      <td>371.05</td>\n",
       "      <td>951.55</td>\n",
       "      <td>277.95</td>\n",
       "      <td>1742.40</td>\n",
       "      <td>1344.10</td>\n",
       "      <td>2365.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>347.00</td>\n",
       "      <td>83.40</td>\n",
       "      <td>266.10</td>\n",
       "      <td>64.25</td>\n",
       "      <td>534.15</td>\n",
       "      <td>100.40</td>\n",
       "      <td>1539.00</td>\n",
       "      <td>785.00</td>\n",
       "      <td>200.90</td>\n",
       "      <td>90.65</td>\n",
       "      <td>...</td>\n",
       "      <td>501.5</td>\n",
       "      <td>565.60</td>\n",
       "      <td>1583.00</td>\n",
       "      <td>1000.40</td>\n",
       "      <td>376.55</td>\n",
       "      <td>955.00</td>\n",
       "      <td>283.45</td>\n",
       "      <td>1740.70</td>\n",
       "      <td>1356.30</td>\n",
       "      <td>2429.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>349.85</td>\n",
       "      <td>82.25</td>\n",
       "      <td>264.85</td>\n",
       "      <td>63.35</td>\n",
       "      <td>528.90</td>\n",
       "      <td>101.45</td>\n",
       "      <td>1530.30</td>\n",
       "      <td>783.65</td>\n",
       "      <td>199.65</td>\n",
       "      <td>88.85</td>\n",
       "      <td>...</td>\n",
       "      <td>502.8</td>\n",
       "      <td>562.35</td>\n",
       "      <td>1559.65</td>\n",
       "      <td>1009.40</td>\n",
       "      <td>389.90</td>\n",
       "      <td>952.80</td>\n",
       "      <td>281.65</td>\n",
       "      <td>1755.45</td>\n",
       "      <td>1353.10</td>\n",
       "      <td>2455.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>334.10</td>\n",
       "      <td>78.95</td>\n",
       "      <td>260.35</td>\n",
       "      <td>61.50</td>\n",
       "      <td>522.70</td>\n",
       "      <td>97.00</td>\n",
       "      <td>1515.05</td>\n",
       "      <td>746.95</td>\n",
       "      <td>193.30</td>\n",
       "      <td>86.90</td>\n",
       "      <td>...</td>\n",
       "      <td>492.0</td>\n",
       "      <td>560.10</td>\n",
       "      <td>1557.15</td>\n",
       "      <td>990.25</td>\n",
       "      <td>378.90</td>\n",
       "      <td>961.75</td>\n",
       "      <td>277.90</td>\n",
       "      <td>1722.60</td>\n",
       "      <td>1327.35</td>\n",
       "      <td>2536.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>336.20</td>\n",
       "      <td>77.15</td>\n",
       "      <td>254.05</td>\n",
       "      <td>61.15</td>\n",
       "      <td>520.40</td>\n",
       "      <td>95.45</td>\n",
       "      <td>1513.55</td>\n",
       "      <td>723.10</td>\n",
       "      <td>195.95</td>\n",
       "      <td>85.05</td>\n",
       "      <td>...</td>\n",
       "      <td>501.7</td>\n",
       "      <td>564.95</td>\n",
       "      <td>1600.00</td>\n",
       "      <td>1008.00</td>\n",
       "      <td>378.95</td>\n",
       "      <td>957.95</td>\n",
       "      <td>286.20</td>\n",
       "      <td>1724.55</td>\n",
       "      <td>1318.85</td>\n",
       "      <td>2507.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   BAJAJELEC  BOMDYEING  CENTURYPLY   IDFC  JETAIRWAYS     NCC      PVR  \\\n",
       "0     341.15      82.20      266.65  63.70      527.50   97.35  1524.95   \n",
       "1     347.00      83.40      266.10  64.25      534.15  100.40  1539.00   \n",
       "2     349.85      82.25      264.85  63.35      528.90  101.45  1530.30   \n",
       "3     334.10      78.95      260.35  61.50      522.70   97.00  1515.05   \n",
       "4     336.20      77.15      254.05  61.15      520.40   95.45  1513.55   \n",
       "\n",
       "   RAYMOND  VIPIND  WELSPUNIND  ...  AXISBANK   CIPLA  HDFCBANK  HINDUNILVR  \\\n",
       "0   772.80  199.20       90.25  ...     500.1  569.00   1553.40      983.25   \n",
       "1   785.00  200.90       90.65  ...     501.5  565.60   1583.00     1000.40   \n",
       "2   783.65  199.65       88.85  ...     502.8  562.35   1559.65     1009.40   \n",
       "3   746.95  193.30       86.90  ...     492.0  560.10   1557.15      990.25   \n",
       "4   723.10  195.95       85.05  ...     501.7  564.95   1600.00     1008.00   \n",
       "\n",
       "   INFRATEL    INFY     ITC       LT  RELIANCE      TCS  \n",
       "0    371.05  951.55  277.95  1742.40   1344.10  2365.10  \n",
       "1    376.55  955.00  283.45  1740.70   1356.30  2429.15  \n",
       "2    389.90  952.80  281.65  1755.45   1353.10  2455.35  \n",
       "3    378.90  961.75  277.90  1722.60   1327.35  2536.20  \n",
       "4    378.95  957.95  286.20  1724.55   1318.85  2507.15  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>BAJAJELEC</th>\n",
       "      <th>BOMDYEING</th>\n",
       "      <th>CENTURYPLY</th>\n",
       "      <th>IDFC</th>\n",
       "      <th>JETAIRWAYS</th>\n",
       "      <th>NCC</th>\n",
       "      <th>PVR</th>\n",
       "      <th>RAYMOND</th>\n",
       "      <th>VIPIND</th>\n",
       "      <th>WELSPUNIND</th>\n",
       "      <th>...</th>\n",
       "      <th>AXISBANK</th>\n",
       "      <th>CIPLA</th>\n",
       "      <th>HDFCBANK</th>\n",
       "      <th>HINDUNILVR</th>\n",
       "      <th>INFRATEL</th>\n",
       "      <th>INFY</th>\n",
       "      <th>ITC</th>\n",
       "      <th>LT</th>\n",
       "      <th>RELIANCE</th>\n",
       "      <th>TCS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.017148</td>\n",
       "      <td>0.014599</td>\n",
       "      <td>-0.002063</td>\n",
       "      <td>0.008634</td>\n",
       "      <td>0.012607</td>\n",
       "      <td>0.031330</td>\n",
       "      <td>0.009213</td>\n",
       "      <td>0.015787</td>\n",
       "      <td>0.008534</td>\n",
       "      <td>0.004432</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002799</td>\n",
       "      <td>-0.005975</td>\n",
       "      <td>0.019055</td>\n",
       "      <td>0.017442</td>\n",
       "      <td>0.014823</td>\n",
       "      <td>0.003626</td>\n",
       "      <td>0.019788</td>\n",
       "      <td>-0.000976</td>\n",
       "      <td>0.009077</td>\n",
       "      <td>0.027081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.008213</td>\n",
       "      <td>-0.013789</td>\n",
       "      <td>-0.004697</td>\n",
       "      <td>-0.014008</td>\n",
       "      <td>-0.009829</td>\n",
       "      <td>0.010458</td>\n",
       "      <td>-0.005653</td>\n",
       "      <td>-0.001720</td>\n",
       "      <td>-0.006222</td>\n",
       "      <td>-0.019857</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002592</td>\n",
       "      <td>-0.005746</td>\n",
       "      <td>-0.014750</td>\n",
       "      <td>0.008996</td>\n",
       "      <td>0.035453</td>\n",
       "      <td>-0.002304</td>\n",
       "      <td>-0.006350</td>\n",
       "      <td>0.008474</td>\n",
       "      <td>-0.002359</td>\n",
       "      <td>0.010786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.045019</td>\n",
       "      <td>-0.040122</td>\n",
       "      <td>-0.016991</td>\n",
       "      <td>-0.029203</td>\n",
       "      <td>-0.011722</td>\n",
       "      <td>-0.043864</td>\n",
       "      <td>-0.009965</td>\n",
       "      <td>-0.046832</td>\n",
       "      <td>-0.031806</td>\n",
       "      <td>-0.021947</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021480</td>\n",
       "      <td>-0.004001</td>\n",
       "      <td>-0.001603</td>\n",
       "      <td>-0.018972</td>\n",
       "      <td>-0.028212</td>\n",
       "      <td>0.009393</td>\n",
       "      <td>-0.013314</td>\n",
       "      <td>-0.018713</td>\n",
       "      <td>-0.019030</td>\n",
       "      <td>0.032928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.006286</td>\n",
       "      <td>-0.022799</td>\n",
       "      <td>-0.024198</td>\n",
       "      <td>-0.005691</td>\n",
       "      <td>-0.004400</td>\n",
       "      <td>-0.015979</td>\n",
       "      <td>-0.000990</td>\n",
       "      <td>-0.031930</td>\n",
       "      <td>0.013709</td>\n",
       "      <td>-0.021289</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019715</td>\n",
       "      <td>0.008659</td>\n",
       "      <td>0.027518</td>\n",
       "      <td>0.017925</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>-0.003951</td>\n",
       "      <td>0.029867</td>\n",
       "      <td>0.001132</td>\n",
       "      <td>-0.006404</td>\n",
       "      <td>-0.011454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   BAJAJELEC  BOMDYEING  CENTURYPLY      IDFC  JETAIRWAYS       NCC       PVR  \\\n",
       "0        NaN        NaN         NaN       NaN         NaN       NaN       NaN   \n",
       "1   0.017148   0.014599   -0.002063  0.008634    0.012607  0.031330  0.009213   \n",
       "2   0.008213  -0.013789   -0.004697 -0.014008   -0.009829  0.010458 -0.005653   \n",
       "3  -0.045019  -0.040122   -0.016991 -0.029203   -0.011722 -0.043864 -0.009965   \n",
       "4   0.006286  -0.022799   -0.024198 -0.005691   -0.004400 -0.015979 -0.000990   \n",
       "\n",
       "    RAYMOND    VIPIND  WELSPUNIND  ...  AXISBANK     CIPLA  HDFCBANK  \\\n",
       "0       NaN       NaN         NaN  ...       NaN       NaN       NaN   \n",
       "1  0.015787  0.008534    0.004432  ...  0.002799 -0.005975  0.019055   \n",
       "2 -0.001720 -0.006222   -0.019857  ...  0.002592 -0.005746 -0.014750   \n",
       "3 -0.046832 -0.031806   -0.021947  ... -0.021480 -0.004001 -0.001603   \n",
       "4 -0.031930  0.013709   -0.021289  ...  0.019715  0.008659  0.027518   \n",
       "\n",
       "   HINDUNILVR  INFRATEL      INFY       ITC        LT  RELIANCE       TCS  \n",
       "0         NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "1    0.017442  0.014823  0.003626  0.019788 -0.000976  0.009077  0.027081  \n",
       "2    0.008996  0.035453 -0.002304 -0.006350  0.008474 -0.002359  0.010786  \n",
       "3   -0.018972 -0.028212  0.009393 -0.013314 -0.018713 -0.019030  0.032928  \n",
       "4    0.017925  0.000132 -0.003951  0.029867  0.001132 -0.006404 -0.011454  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns = stocks.pct_change()\n",
    "returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STOCK          Annual Return %\n",
      "\n",
      "BAJAJELEC       31.605318\n",
      "BOMDYEING       32.762148\n",
      "CENTURYPLY     -18.233834\n",
      "IDFC           -20.048904\n",
      "JETAIRWAYS     -48.101880\n",
      "NCC              7.714186\n",
      "PVR             10.853008\n",
      "RAYMOND          7.002052\n",
      "VIPIND          47.966397\n",
      "WELSPUNIND     -20.805802\n",
      "ADANIPOWER      30.527262\n",
      "CASTROLIND     -43.455745\n",
      "DHFL          2785.158723\n",
      "EXIDEIND        -5.148401\n",
      "MINDTREE        39.256977\n",
      "RBLBANK         11.349668\n",
      "RELINFRA       -75.007376\n",
      "SUNTV          -20.570430\n",
      "TATAPOWER       -9.094974\n",
      "VOLTAS          19.103016\n",
      "AXISBANK        23.524575\n",
      "CIPLA            2.197553\n",
      "HDFCBANK        16.687232\n",
      "HINDUNILVR      29.497061\n",
      "INFRATEL       -12.418651\n",
      "INFY            -2.267179\n",
      "ITC              6.401437\n",
      "LT              -8.590369\n",
      "RELIANCE         9.512101\n",
      "TCS              8.046956\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "ann_ret = returns.mean()*252*100\n",
    "print(\"STOCK          Annual Return %\\n\")\n",
    "print(ann_ret)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STOCK          Annual Volatility %\n",
      "\n",
      "BAJAJELEC      40.241355\n",
      "BOMDYEING      60.286945\n",
      "CENTURYPLY     37.526871\n",
      "IDFC           35.398952\n",
      "JETAIRWAYS     62.485368\n",
      "NCC            44.139329\n",
      "PVR            31.019078\n",
      "RAYMOND        36.383449\n",
      "VIPIND         43.206910\n",
      "WELSPUNIND     39.907054\n",
      "ADANIPOWER     61.053220\n",
      "CASTROLIND     44.646585\n",
      "DHFL          842.419659\n",
      "EXIDEIND       26.298513\n",
      "MINDTREE       34.148514\n",
      "RBLBANK        25.873220\n",
      "RELINFRA       54.757563\n",
      "SUNTV          36.541396\n",
      "TATAPOWER      30.829677\n",
      "VOLTAS         30.834649\n",
      "AXISBANK       27.789564\n",
      "CIPLA          25.418277\n",
      "HDFCBANK       25.682114\n",
      "HINDUNILVR     19.396979\n",
      "INFRATEL       30.602300\n",
      "INFY           41.896335\n",
      "ITC            22.386344\n",
      "LT             31.432282\n",
      "RELIANCE       43.909085\n",
      "TCS            43.138713\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "ann_vol = returns.std()*np.sqrt(252)*100\n",
    "print(\"STOCK          Annual Volatility %\\n\")\n",
    "print(ann_vol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Return</th>\n",
       "      <th>Volatility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>0.316053</td>\n",
       "      <td>0.402414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BOMDYEING</td>\n",
       "      <td>0.327621</td>\n",
       "      <td>0.602869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CENTURYPLY</td>\n",
       "      <td>-0.182338</td>\n",
       "      <td>0.375269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>IDFC</td>\n",
       "      <td>-0.200489</td>\n",
       "      <td>0.353990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>JETAIRWAYS</td>\n",
       "      <td>-0.481019</td>\n",
       "      <td>0.624854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NCC</td>\n",
       "      <td>0.077142</td>\n",
       "      <td>0.441393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PVR</td>\n",
       "      <td>0.108530</td>\n",
       "      <td>0.310191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RAYMOND</td>\n",
       "      <td>0.070021</td>\n",
       "      <td>0.363834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VIPIND</td>\n",
       "      <td>0.479664</td>\n",
       "      <td>0.432069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WELSPUNIND</td>\n",
       "      <td>-0.208058</td>\n",
       "      <td>0.399071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ADANIPOWER</td>\n",
       "      <td>0.305273</td>\n",
       "      <td>0.610532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CASTROLIND</td>\n",
       "      <td>-0.434557</td>\n",
       "      <td>0.446466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DHFL</td>\n",
       "      <td>27.851587</td>\n",
       "      <td>8.424197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>EXIDEIND</td>\n",
       "      <td>-0.051484</td>\n",
       "      <td>0.262985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MINDTREE</td>\n",
       "      <td>0.392570</td>\n",
       "      <td>0.341485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RBLBANK</td>\n",
       "      <td>0.113497</td>\n",
       "      <td>0.258732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RELINFRA</td>\n",
       "      <td>-0.750074</td>\n",
       "      <td>0.547576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SUNTV</td>\n",
       "      <td>-0.205704</td>\n",
       "      <td>0.365414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TATAPOWER</td>\n",
       "      <td>-0.090950</td>\n",
       "      <td>0.308297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VOLTAS</td>\n",
       "      <td>0.191030</td>\n",
       "      <td>0.308346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AXISBANK</td>\n",
       "      <td>0.235246</td>\n",
       "      <td>0.277896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CIPLA</td>\n",
       "      <td>0.021976</td>\n",
       "      <td>0.254183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HDFCBANK</td>\n",
       "      <td>0.166872</td>\n",
       "      <td>0.256821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HINDUNILVR</td>\n",
       "      <td>0.294971</td>\n",
       "      <td>0.193970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>INFRATEL</td>\n",
       "      <td>-0.124187</td>\n",
       "      <td>0.306023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>INFY</td>\n",
       "      <td>-0.022672</td>\n",
       "      <td>0.418963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ITC</td>\n",
       "      <td>0.064014</td>\n",
       "      <td>0.223863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LT</td>\n",
       "      <td>-0.085904</td>\n",
       "      <td>0.314323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RELIANCE</td>\n",
       "      <td>0.095121</td>\n",
       "      <td>0.439091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TCS</td>\n",
       "      <td>0.080470</td>\n",
       "      <td>0.431387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Return  Volatility\n",
       "BAJAJELEC    0.316053    0.402414\n",
       "BOMDYEING    0.327621    0.602869\n",
       "CENTURYPLY  -0.182338    0.375269\n",
       "IDFC        -0.200489    0.353990\n",
       "JETAIRWAYS  -0.481019    0.624854\n",
       "NCC          0.077142    0.441393\n",
       "PVR          0.108530    0.310191\n",
       "RAYMOND      0.070021    0.363834\n",
       "VIPIND       0.479664    0.432069\n",
       "WELSPUNIND  -0.208058    0.399071\n",
       "ADANIPOWER   0.305273    0.610532\n",
       "CASTROLIND  -0.434557    0.446466\n",
       "DHFL        27.851587    8.424197\n",
       "EXIDEIND    -0.051484    0.262985\n",
       "MINDTREE     0.392570    0.341485\n",
       "RBLBANK      0.113497    0.258732\n",
       "RELINFRA    -0.750074    0.547576\n",
       "SUNTV       -0.205704    0.365414\n",
       "TATAPOWER   -0.090950    0.308297\n",
       "VOLTAS       0.191030    0.308346\n",
       "AXISBANK     0.235246    0.277896\n",
       "CIPLA        0.021976    0.254183\n",
       "HDFCBANK     0.166872    0.256821\n",
       "HINDUNILVR   0.294971    0.193970\n",
       "INFRATEL    -0.124187    0.306023\n",
       "INFY        -0.022672    0.418963\n",
       "ITC          0.064014    0.223863\n",
       "LT          -0.085904    0.314323\n",
       "RELIANCE     0.095121    0.439091\n",
       "TCS          0.080470    0.431387"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual = pd.DataFrame()\n",
    "annual[\"Return\"] = returns.mean()*252\n",
    "annual[\"Volatility\"] = returns.std() * np.sqrt(252)\n",
    "annual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimal Clusters using Elbow Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Elbow curve')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UfHDZybVasXGPtuzt8LsUAAAAAFmIoOaDBXNqJDH9EQAAAEBqBDUfTJ9Yotl15foDX34NAAAAIAWCmk8WzKnVyk17tam53e9SAAAAAGQZgppPFtYnpj+yqAgAAACAgQhqPplaWayTp1RoCdMfAQAAAAxAUPPRwvpard7cog272vwuBQAAAEAWIaj5aD7THwEAAACkQFDzUd24Ip121DgtZvojAAAAgCQENZ8tqK/Vuq379MbOVr9LAQAAAJAlCGo+6/3yaxYVAQAAANCLoOaz6opCnT59PEENAAAAQB+CWhZYWF+rV7fv1+vb9/tdCgAAAIAsQFDLApfOrpaZWFQEAAAAgCSCWlaYXF6oM4+u1OJVW+Sc87scAAAAAD4jqGWJBfW1emNnm15l+iMAAACQ8whqWeLS2dUKmbR4JdMfAQAAgFw3ZFAzs3vMbIeZrRmkvdHMWszsJe/n65kvc+ybWFqgs4+dqCWrtzL9EQAAAMhx6dxRu0/SJUP0edY5d4r3c8vwy8pNC+pr9NauNr28ZZ/fpQAAAADw0ZBBzTn3jKTmUagl510yq1rhkLH6IwAAAJDjMvUZtbPMbKWZPWJmszJ0zZwzviRf58yYqCWrWf0RAAAAyGWWTiAws+mSFjvnZqdoK5cUd861mtl8ST90zh03yHVukHSDJFVVVc1dtGjRMEoPntbWVpWWlh6yz7NNPbp7Tbe+flahjqkIj1JlSEc644fsxNgFG+MXbIxfsDF+wcb4Zb958+atcM41pGqLDPfizrl9SdsPm9n/NbOJzrldKfreKelOSWpoaHCNjY3DffpAWbp0qYZ6zae29+j+dU9oW16trm88aXQKQ1rSGT9kJ8Yu2Bi/YGP8go3xCzbGL9iGPfXRzKrNzLztM7xr7h7udXNVRXGe3nncJC1ZxeqPAAAAQK5KZ3n+X0h6XtIJZtZkZh8zsxvN7Eavy/slrTGzlZJuk3SVI2EMy4I5Ndq8t0N/37TX71IAAAAA+GDIqY/OuauHaL9d0u0Zqwi6cFaV8h8MafHKrTrtqPF+lwMAAABglGVq1UdkUHlhns47fpIeXr1V8Tg3JwEAAIBcQ1DLUpedXKNt+zq14u09fpcCAAAAYJQR1LLUBSdVqSAS0hK+/BoAAADIOQS1LFVaENG8EyZryeqtijH9EQAAAMgpBLUstqC+Rjv3d2nZhma/SwEAAAAwighqWeyCkyarMC+kxau2+F0KAAAAgFFEUMtixfkRXXBilR5ds03RWNzvcgAAAACMEoJalltYX6Ndrd3661tMfwQAAAByBUEtyzWeMFnF+WEtZvVHAAAAIGcQ1LJcUX5Y7z6pSo+u2aoepj8CAAAAOYGgFgAL6mu0p71Hz7+x2+9SAAAAAIwCgloAnH/8JJUWRFj9EQAAAMgRBLUAKMwL68KZVXrs5e3qjjL9EQAAABjrCGoBsbC+Ri0dPfrz+l1+lwIAAABghBHUAuLc4yaqrDDC6o8AAABADiCoBURBJKyLZ1Xr8bXb1BWN+V0OAAAAgBFEUAuQhfU12t8Z1bOvMf0RAAAAGMsIagFyzoyJGlecx+qPAAAAwBhHUAuQvHBIl8yq1hNrt6uzh+mPAAAAwFhFUAuYBfU1auuOaemrO/0uBQAAAMAIIagFzFnHTFBlST7THwEAAIAxjKAWMJFwSJfMrtYf1+1QRzfTHwEAAICxiKAWQAvra9TRE9NTr+zwuxQAAAAAI4CgFkBnHj1BE0sLtGQ10x8BAACAsYigFkDhkGn+nGo99coOtXVF/S4HAAAAQIYR1AJqwZwadfbE9UemPwIAAABjDkEtoE6fXqnJZQVavJLpjwAAAMBYQ1ALqFDINH9OjZa+tlP7O3v8LgcAAABABhHUAuyyk2vUHY3ryXXb/S4FAAAAQAYR1ALs1KnjVVNRqAee36ieWNzvcgAAAABkCEEtwEIh05cvOkEvvr1XX/vtGjnn/C4JAAAAQAZE/C4Aw/O+uVO0YXebfvTUeh01oVg3zZvhd0kAAAAAhomgNgZ88cLj1bSnQ//+2KuaMr5IV5xS53dJAAAAAIaBoDYGmJm++7452trSoX/89SpVlRfqHcdM8LssAAAAAEeIz6iNEQWRsP7rww2aWlmkTz6wQut3tPpdEgAAAIAjRFAbQyqK83TfdWcoL2y69t6/aef+Lr9LAgAAAHAECGpjzNTKYt19zena1dqlj9+/XB3dMb9LAgAAAHCYCGpj0MlTx+m2q07Vqqa9+tyivysWZ9l+AAAAIEgIamPURbOq9fWFM/X42u369pJ1fpcDAAAA4DCw6uMYdt05R+vt5nbd8+e3NLWySNedc7TfJQEAAABIA0FtjPvagpnavKdDtyxeq7pxRbpoVrXfJQEAAAAYwpBTH83sHjPbYWZrBmk3M7vNzNab2SozOy3zZeJIhUOmH151qurrKvTZRX/Xyk17/S4JAAAAwBDS+YzafZIuOUT7pZKO835ukPTj4ZeFTCrKD+uua07XpLICfewny7Spud3vkgAAAAAcwpBBzTn3jKTmQ3S5QtL9LuEFSePMrCZTBSIzJpUV6N5rz1B3NK7r7lumlvYev0sCAAAAMIhMrPpYJ2lT0n6TdwxZZsbkUt350QZt3N2mT/50ubqifMcaAAAAkI3MuaG/Y8vMpkta7JybnaJtiaTvOOee8/b/KOmfnHMrUvS9QYnpkaqqqpq7aNGiYRUfNK2trSotLfW7DP1lS1R3rurSWbVh3TCnQGbmd0mBkC3jh8PH2AUb4xdsjF+wMX7Bxvhlv3nz5q1wzjWkasvEqo9NkqYm7U+RtCVVR+fcnZLulKSGhgbX2NiYgacPjqVLlyobXnOjpPLq1/Ufj7+m00+s1RcvOsHvkgIhW8YPh4+xCzbGL9gYv2Bj/IKN8Qu2TEx9fEjSR73VH98hqcU5tzUD18UIumneDH2gYYpue2q9frV809AnAAAAABg1Q95RM7NfKHETZqKZNUn6hqQ8SXLO3SHpYUnzJa2X1C7pupEqFpljZvr2e+Zoa0unbn5wtWorinTucRP9LgsAAACA0ghqzrmrh2h3km7KWEUYNXnhkP7zQ6fpA3c8r0/9dIV+/amzdGJ1ud9lAQAAADkvE1MfEWDlhXm659rTVVwQ1vX3LtP2fZ1+lwQAAADkPIIaVDuuSPdce7paOnp0/X3L1NYV9bskAAAAIKcR1CBJmlVbods/dJpe2bZfn/75i4rG4n6XBAAAAOQsghr6zDthsm65Ypb+9OpOfeOhl5XOd+wBAAAAyLxMfI8axpAPnTlNm5o7dMfTb+ioymJ98vxj/S4JAAAAyDkENRzkny4+QZv2tOs7j7yiKeOLtaC+xu+SAAAAgJxCUMNBQiHTrVeerO0tnfrCr15SdUWB5k6r9LssAAAAIGfwGTWkVJgX1p0fbVDduCJ9/CfL9dauNr9LAgAAAHIGQQ2DqizJ173Xni5Juu7ev6m5rdvnigAAAIDcQFDDIU2fWKK7rmnQlpZOfeL+5ersifldEgAAADDmEdQwpLnTKvWDD56iFRv36Eu/Wql4nGX7AQAAgJFEUENa5s+p0c3zT9SS1Vv1vcde8bscAAAAYExj1Uek7RPvPEZvN7frv55+U1PHF+vD75jmd0kAAADAmERQQ9rMTN+8bJa27O3U13+/RnXjijTvxMl+lwUAAACMOUx9xGGJhEP60dWn6qSact308xe1ZnOL3yUBAAAAYw5BDYetpCCie649XeOK8nT9fcu0eW+H3yUBAAAAYwpBDUekqrxQ9153hjq6Y7r+3mXa19njd0kAAADAmEFQwxE7obpMd3xkrt7Y2ar//dMX1ROL+10SAAAAMCYQ1DAs58yYqO+8d46eW79LNz+4Ws7xHWsAAADAcLHqI4btyoap2rSnQ7f98XUdVVmsz1xwnN8lAQAAAIFGUENGfOHdx6mpuV23PvGaplQW6T2nTvG7JAAAACCwCGrICDPTd99Xr60tnfqn/1ml6vIinXXsBL/LAgAAAAKJz6ghY/IjId3x4bmaNqFEn3xgudbv2O93SQAAAEAgEdSQURXFebr32tOVHwnr2nuXaef+Lr9LAgAAAAKHoIaMm1pZrHuubdDu1m59/CfL1N4d9bskAAAAIFAIahgR9VPG6barT9WqzS363KKXFIuzbD8AAACQLoIaRsyFM6v0jYUz9cTa7frW4rV+lwMAAAAEBqs+YkRde87R2rSnQ3c/95aOqizW9ece7XdJAAAAQNYjqGHE3Tz/JDXtade3lqxV3fgiXTyr2u+SAAAAgKzG1EeMuHDI9IMPnqqTp4zT5xb9XS9t2ut3SQAAAEBWI6hhVBTlh3XXNQ2aVFagj923TOt3tPpdEgAAAJC1CGoYNRNLC3TfdWco5pzm3/asvv/Ea+rsifldFgAAAJB1CGoYVcdOKtVjnz9Pl8yq1m1/fF0X/Z9n9NQr2/0uCwAAAMgqBDWMuqryQt129an6+cfPVF7YdP19y/WJ+5drU3O736UBAAAAWYGgBt+cPWOiHvncefrKJSfqudd36cL/87Ruf+p1dUWZDgkAAIDcRlCDr/IjIX2q8Vg9+aXzNe+EyfqPx1/TpT94Vs++vtPv0gAAAADfENSQFerGFenHH56r+647XXHn9JG7/6abfvaitrZ0+F0aAAAAMOoIasgqjSdM1qOfP09fvPB4Pbluuy649Wn919NvqCcW97s0AAAAYNQQ1JB1CvPC+uwFx+nJL56vs4+doO888orm//BZPf/Gbr9LAwAAAEYFQQ1Za2plse665nTd9dEGdfTEdPV/v6DPL/q7duzr9Ls0AAAAYEQR1JD13j2zSk984Xx99l0z9PDqbbrg1qd1z3NvKcp0SAAAAIxRaQU1M7vEzF41s/Vm9s8p2q81s51m9pL38/HMl4pcVpQf1hcvOkGPfeE8nTptvG5ZvFYLf/Sclm9o9rs0AAAAIOOGDGpmFpb0n5IulTRT0tVmNjNF1186507xfu7KcJ2AJOnoiSX6yXWn68cfOk0tHT16/x3P68u/XqldrV1+lwYAAABkTDp31M6QtN4596ZzrlvSIklXjGxZwODMTJfOqdGTXzxfN55/rH739816138s1QMvbFQs7vwuDwAAABi2dIJanaRNSftN3rGB3mdmq8zsf8xsakaqAw6hpCCif770RD36+XdqVm2F/uV3a/Se//tnrdy01+/SAAAAgGEx5w59B8LMrpR0sXPu497+RySd4Zz7TFKfCZJanXNdZnajpA84596V4lo3SLpBkqqqquYuWrQoc68kAFpbW1VaWup3GWOSc05/3RrTole71dLldP6UiN5/fL5K8y1jz8H4BRdjF2yMX7AxfsHG+AUb45f95s2bt8I515CqLZ2gdpakbzrnLvb2vypJzrnvDNI/LKnZOVdxqOs2NDS45cuXp1H+2LF06VI1Njb6XcaYtr+zRz948nXd95cNKi9M3HG7cu5UhULDD2yMX3AxdsHG+AUb4xdsjF+wMX7Zz8wGDWrpTH1cJuk4MzvazPIlXSXpoQFPUJO0e7mkdUdaLDAcZYV5+peFM7X4M+dqxuRSfeU3q/W+O/6iNZtb/C4NAAAASNuQQc05F5X0aUmPKRHAfuWce9nMbjGzy71unzWzl81spaTPSrp2pAoG0nFSTbl+9cmzdOuVJ2tTc7suv/05feP3a9TS0eN3aQAAAMCQIul0cs49LOnhAce+nrT9VUlfzWxpwPCYmd43d4rePbNKtz7+qh54YaOWrN6mm+efqPecWiezzH1+DQAAAMiktL7wGgiyiqI83XLFbD306XM1ZXyRvvirlfrgf72gV7ft97s0AAAAICWCGnLG7LoKPfips/Xd987Razv2a/5tz+pfF69Va1fU79IAAACAAxDUkFNCIdNVZxylP32pUR9omKK7//yWLrh1qR5auUVDrYAKAAAAjBaCGnLS+JJ8fee99XrwU2drUlmBPvuLv+vDd/9V63e0+l0aAAAAQFBDbjv1qPH6/U3n6ltXzNLqphZd+sNn9L1HX1F7N9MhAQAA4B+CGnJeOGT6yFnT9dSXG3X5yXX68dI3dOH3n9Gja7YxHRIAAAC+IKgBnomlBbr1Ayfr1zeepbLCiG786Qpdd98ybXYvVs8AABRxSURBVNjV5ndpAAAAyDEENWCA06dXavFnztW/LJyp5Rv26KIfPKNbH39Vb++LqScW97s8AAAA5IC0vvAayDWRcEgfO/doLayv0beXrNOPnlovSfr23x7TSTXlqp9SoTl1FZozpUIzJpUqEubfPAAAAJA5BDXgEKrKC3Xb1afqSxcdr58/9rxi5bVatblFv1nRpPuf3yhJKswLaWZNueqnjNPsugrVT6nQsZNKFQ6Zz9UDAAAgqAhqQBqmTSjRWbURNTbOlCTF405v7mrTms0tWtXUojWbW/Sr5Zt03182SJKK8sKaVVveF9zm1FXoGMIbAAAA0kRQA45AKGSaMblUMyaX6h9OrZMkxeJOb+1q1aqmFq3e3KLVTS365bL+8FacH9bs2oq+8Da7rkLHTCxRiPAGAACAAQhqQIaEQ6YZk8s0Y3KZ3nvaFEmJ8PbGzta+u26rmvbq53/bqHv+nFiUpLQgopm15ar3Pu82p65C0ycQ3gAAAHIdQQ0YQeGQ6fiqMh1fVab3z02Et2gsrvU7W7W6987b5hY98MJGdUUT4a2sIKJZdeXeYiXjNKeuQtMqiwlvAAAAOYSgBoyySDikE6vLdWJ1ua5smCpJ6onFtX5Hf3hbtblFP3l+o7qjb0mSygojieCWdOftqMpimRHeAAAAxiKCGpAF8sIhnVRTrpNqyvWB0/vD22vb9x+wYMm9f96gbu+73CqK8jS7rlxz6hJ33eqnVGjK+CLCGwAAwBhAUAOyVF44pFm1FZpVW6EPnp441h1NhLfeKZOrm1p093NvqifmJEnjivM0py6xUMms2nIdPbFE0yaUqLSAtzoAAECQ8Lc3IEDyIyHN9oLY1d6xrmhMr21r1arNe7XGC3D//cybisZd33kTSws0fUKxpk0o0bQJxZo2oVjTJ5Ro+oQSVRTn+fNiAAAAMCiCGhBwBZFw4nNrUyr6jnVFY3pjR5s27m7Tht3t3mOb/vLGLv3mxc4Dzh9XnKdpE0r6glzyY2VJPlMpAQAAfEBQA8aggkhYM2vLNbO2/KC2zp6Y3m5u14Zdbdq4u10bdiceV2zcoz+s3KKkG3EqK4ho2sSBAS6xPamsgBAHAAAwQghqQI4pzAv3fWXAQF3RmJr2dCTuwO1qTwS63W1au2WfHluz7YDplEV54QOmUfaFuYklqikv5OsEAAAAhoGgBqBPQSSsYyeV6thJpQe1RWNxbdnb6d2B659S+cbONv3plZ19q1FKic/SHVVZnGI6ZYlqxxUqEg6N5ssCAAAIHIIagLREwiEdNaFYR00oljTpgLZY3Gnbvk5t3HXgZ+I27m7Xc+t3qbOnP8RFQqaplcl34hKPR00o1tTxxcqPEOIAAAAIagCGLRwy1Y0rUt24Ip0948A255x27O866DNxG3a3afmGPWrtivb1NZOqyws1ZXyRpo4v1pTxRZpSWdy3XVPB3TgAAJAbCGoARpSZqaq8UFXlhTrzmAkHtDnntLutWxt3t+mtXe3a1NyuTXva1bSnQy+8uVtb93XKJS1uEg6ZaioKNXV8saZWFmlK8uP4Yk0uK+CzcQAAYEwgqAHwjZlpYmmBJpYWaO60yoPau6NxbW3p0KbmDjXtSYS43u0/vbpTO/d3HdA/PxxS3fiixB25yuK+O3M79sY0u7VLE/i6AQAAEBAENQBZKz8S8r6kuyRle2dPYpXK3rtwTc3tfftrVm/Vnvaevr7feuFJFeWFDwpxyXfkyosiBDkAAJAVCGoAAqswL6wZk0s1Y/LBq1RKUmtXVE172vXIM3/TuLpjk+7MdWjZW83an/T5OCnxvXGJz8QNmFZZmQh1JQX8ygQAAKODv3UAGLNKCyI6sbpc2yZH1HjO0Qe1t7T3eHfj2g8IcW/tatOzr+9SR0/sgP7ji/MOuBs3pbJYNeWFmlxeoKryQk0oyWexEwAAkBEENQA5q6I4TxXFFZpdV3FQW+9CJ017Og5Y5GRTc7te2bpfT67dccB3x0lSyKQJpQWaXJYIbpPLCjTZe6xKepxYSqADAACHRlADgBSSFzo5Zeq4g9rj8cTXDmzb16kd+zq1fX+Xdu7r1PZ9Xdqxv1PbWjq1qqlFu9u6Dli5MnFtaUJJb6Ar0OSywsRjcqgrTzx3HoEOAICcRFADgCMQCpmqKwpVXVF4yH7RWFy7Wru1fV+nduxPhLjt+7q0c39/qFuzZZ92t3YpnjLQ5WtymTe90nsceJduUhmBDgCAsYagBgAjKBIOpR3odrd1a8e+rr5Q1xfuvMe1W/ZpV4pAJ3mBri/A9d+lm5R0t25SaYHyIwQ6AACCgKAGAFkgEg71fTH4HB38mblesbjT7tauA4LcwED3yrZ92tXarViKRFdZkq/xxXkaX5yvccXedkm+xnvbycfGef24WwcAwOgjqAFAgIRDlrhzVl6YchGUXrG40+62Lu3Y16WdA0Ld3vYe7WnvVtOedq3ZnNjuisYHvVZZQUTjSgaEu2Iv3JXkHXBsXHGeKkvyVZQX5jvpAAAYBoIaAIxB4ZAlPttWdugpl706umPa096tPe3d2tveo+a2bu1t79YeL9T1hrs9bd3asKtNe9q7tb8zOuj18iOhA8JbItQdfDev97GyJF/lhXkKhQh3AABIBDUAgKSi/LCK8otUO64o7XOisbj2dvRob3u3mtt6A11SuGvrUbN37PUdrX1tqaZkSomvN6goOjjc7d/VpeVdr6ooP6zCvLAK80IqygurKC+swvywCiPhRP1JbYXePtM2AQBBRVADAByRSDjU9xUG6XLOaX9XtC/E9YW7tp6+INcb7ra2dGrd1n1qbo3qibfXp1xEZSjhkHkBLingeeEuEeZCSe39bUX5ieMFXiDsOy8vlOjX2987XhAJMdUTAJBRBDUAwKgxM5UX5qm8ME9HTShO65ylS5fq/PPPV0/MqaMnpk7vp6Mnpo7uxGNXT7xvvzPqPXp9Or22zqS2jp6YWjp6tGNf/3U6vb4Dv8g8XYUDQl9+OKSCvJDywyHlR0IqiPQ+hpUfGXAsHFKBd07y8QP6J12vMC+k/HD4oL6RkBEYAWCMIKgBALKemSk/YsqPhFRRlDeizxWNxdUZjSeCXlKA60gKh11Jga8vCCb3j8bVHY2pKxpXdzSurmhc+zuj3nZM3dFEIOzqiasrluiTCSFTUqjzwuLAUJgi+PUGvd7tvHDvjx2837sd6d9P7pMfDikvYn37XTGnaCyuMCESAA4LQQ0AgCSRcEil4ZBKC0bvj0jnnLq9wNYb7JLDXHcsEfr6jidt9wW/3mOx/uNdA68Xjau9O6q9HQOvEVdP77mxuNwRTDM9pCcekZn6g1xSAOwPfnZg2AtbXyBMdU4kbIqEEscj4cTdxEgosZ3ntUWSHlMe69vvv0ZeOPWxkImgCWBUpfWnkJldIumHksKS7nLOfXdAe4Gk+yXNlbRb0gedcxsyWyoAAGOTmakgElZBJOx3KZISX+/Q44W2nmhcPbGkfS8IJh4Tx3t/umPO69+//+prr2vqtKP7QmBP0jmJ6/Wf03v9jp6Y9nX2h9WepPOSaziSzy0OR94BwS+kcMiU1xvoesPdwPCYHChDIYXDiXPC3rFwqP9ayddIDoqRg8Jo6j7J18oLeY8pnyN00HOx4iqQfYYMamYWlvSfki6U1CRpmZk95Jxbm9TtY5L2OOdmmNlVkr4n6YMjUTAAABhZ4ZApHEp81m64lkY3qrHxuAxUdbB43Ckad4rGE4EvFk9Ms+zpffSO9cTiiX59j0498biisaRj3jWiMadY7/bAYwOu29+eaIsln+M9dvbEFY1F+45F4711JurqrS8Wd4lreD+jLWTqC3DJwS7W063ivz6V+G/CEoEuEjKFLNGvdz/R1vvfTkhh6932+ln/dnjAuQe0JT1P2BLBMtGuA6471Lm955j1P5+ZDmgP9V7LvJ/Qge1h884J9bf3Hg8lvd6+872+3HlFpqRzR+0MSeudc29KkpktknSFpOSgdoWkb3rb/yPpdjMz5zI+eQIAAECSFAqZ8kOmfI2tr2GIx51iLhHmor2BMn7gdix+YBBNPHr78XhfaOwNpgcG1d5wmur8/qAZizs1bd6qSVWVfQEy7vrDZCzuFHNSzOsbj8sLxbHEa/D6R+Ou7zUln9t7reT2eFyKxkf/bmmmJQKc+kJc336oNwAmgmRySOzdtr7z5O0nXysRPm3AfvJzJbc1N3fqpxuX97eHevt75yrFc4QOfc2QJU4MmckkJXYTbfKO9Z5jUt/d2uR+vdvW+3qkA15b4lKW8pyBz937XDrgeXtrluRtF+aFdf7xk0b9v4XhSCeo1UnalLTfJOnMwfo456Jm1iJpgqRdmSgSAAAgV4RCppBMiRua/k6HXbq0WY2Np4z68zrnFHdeaItr0JDXHxhd3x3WuBf44q7/eNwlpvS63mNO3vHe6ylpu/8aMeed413vgP695zsn510/nvx8fdsHnt+771x/7XK97YlH55L79///kbw/sE/MC7jJfVq6nHr2dhx0TZfcT4nXmuo5DlVTb83OG6/E46j/p5K2iaUFWv61d/tdxmFJJ6ilun87cBjS6SMzu0HSDZJUVVWlpUuXpvH0Y0dra2vOveaxhPELLsYu2Bi/YGP8gm0sj58pEYMzEoV7L5ZlWltjKi2NDdJqAx4zIzm09YaB3jukzvufeF9fHRDw4l57Ige6vuMuRd+Bx9zAa/Y9d2IrZBa4/5bTCWpNkqYm7U+RtGWQPk1mFpFUIal54IWcc3dKulOSGhoaXGNj4xGUHFxLly5Vrr3msYTxCy7GLtgYv2Bj/IKN8Qs2xi/Y0pnUvUzScWZ2tJnlS7pK0kMD+jwk6Rpv+/2SnuLzaQAAAABwZIa8o+Z95uzTkh5T4qbuPc65l83sFknLnXMPSbpb0gNmtl6JO2lXjWTRAAAAADCWpfU9as65hyU9PODY15O2OyVdmdnSAAAAACA3ja31bAEAAABgDCCoAQAAAECWIagBAAAAQJYhqAEAAABAliGoAQAAAECWIagBAAAAQJYhqAEAAABAljHnnD9PbLZT0kZfntw/EyXt8rsIHDHGL7gYu2Bj/IKN8Qs2xi/YGL/sN805NylVg29BLReZ2XLnXIPfdeDIMH7BxdgFG+MXbIxfsDF+wcb4BRtTHwEAAAAgyxDUAAAAACDLENRG151+F4BhYfyCi7ELNsYv2Bi/YGP8go3xCzA+owYAAAAAWYY7agAAAACQZQhqGWRmU83sT2a2zsxeNrPPpejTaGYtZvaS9/N1P2pFama2wcxWe2OzPEW7mdltZrbezFaZ2Wl+1ImDmdkJSe+rl8xsn5l9fkAf3n9ZxMzuMbMdZrYm6VilmT1hZq97j+MHOfcar8/rZnbN6FWNXoOM37+b2Sve78ffmtm4Qc495O9ajLxBxu+bZrY56Xfk/EHOvcTMXvX+LPzn0asavQYZv18mjd0GM3tpkHN5/wUEUx8zyMxqJNU45140szJJKyT9g3NubVKfRklfds4t9KlMHIKZbZDU4JxL+Z0j3h9an5E0X9KZkn7onDtz9CpEOswsLGmzpDOdcxuTjjeK91/WMLPzJLVKut85N9s79m+Smp1z3/X+AjjeOfeVAedVSlouqUGSU+J37Vzn3J5RfQE5bpDxu0jSU865qJl9T5IGjp/Xb4MO8bsWI2+Q8fumpFbn3H8c4rywpNckXSipSdIySVcn/10HIy/V+A1ov1VSi3PulhRtG8T7LxC4o5ZBzrmtzrkXve39ktZJqvO3KmTYFUr8UnTOuRckjfMCOrLLBZLeSA5pyD7OuWckNQ84fIWkn3jbP5H0DylOvVjSE865Zi+cPSHpkhErFCmlGj/n3OPOuai3+4KkKaNeGNIyyPsvHWdIWu+ce9M51y1pkRLvW4yiQ42fmZmkD0j6xagWhYwjqI0QM5su6VRJf03RfJaZrTSzR8xs1qgWhqE4SY+b2QozuyFFe52kTUn7TSKMZ6OrNPgfULz/sluVc26rlPjHL0mTU/ThfRgM10t6ZJC2oX7Xwj+f9qau3jPI1GPef9nvnZK2O+deH6Sd919AENRGgJmVSvqNpM875/YNaH5R0jTn3MmSfiTpd6NdHw7pHOfcaZIulXSTN7UgmaU4h/nDWcTM8iVdLunXKZp5/40NvA+znJn9f5Kikn42SJehftfCHz+WdKykUyRtlXRrij68/7Lf1Tr03TTefwFBUMswM8tTIqT9zDn34MB259w+51yrt/2wpDwzmzjKZWIQzrkt3uMOSb9VYopHsiZJU5P2p0jaMjrVIU2XSnrRObd9YAPvv0DY3jud2HvckaIP78Ms5i3uslDSh9wgH4RP43ctfOCc2+6ciznn4pL+W6nHhfdfFjOziKT3SvrlYH14/wUHQS2DvDnBd0ta55z7/iB9qr1+MrMzlBiD3aNXJQZjZiXeIjAysxJJF0laM6DbQ5I+mlj80d6hxAd1t45yqTi0Qf8lkfdfIDwkqXcVx2sk/T5Fn8ckXWRm472pWRd5x+AzM7tE0lckXe6cax+kTzq/a+GDAZ+5fo9Sj8sySceZ2dHeDIarlHjfIju8W9IrzrmmVI28/4Il4ncBY8w5kj4iaXXSkqg3SzpKkpxzd0h6v6RPmVlUUoekqwb7F0eMuipJv/X+Hh+R9HPn3KNmdqPUN34PK7Hi43pJ7ZKu86lWpGBmxUqsRPbJpGPJ48f7L4uY2S8kNUqaaGZNkr4h6buSfmVmH5P0tqQrvb4Nkm50zn3cOddsZt9S4i+MknSLc+5IFkXAMAwyfl+VVCDpCe936QvOuRvNrFbSXc65+Rrkd60PLyGnDTJ+jWZ2ihJTGTfI+12aPH7eip6fVuIfR8KS7nHOvezDS8hpqcbPOXe3UnxGm/dfcLE8PwAAAABkGaY+AgAAAECWIagBAAAAQJYhqAEAAABAliGoAQAAAECWIagBAAAAQJYhqAEAAABAliGoAQAAAECWIagBAAAAQJb5f6vC1DR8FWq2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "data = np.asarray([np.asarray(annual['Return']),np.asarray(annual['Volatility'])]).T\n",
    "\n",
    "X = data\n",
    "distorsions = []\n",
    "for k in range(2, 20):\n",
    "    k_means = KMeans(n_clusters=k)\n",
    "    k_means.fit(X)\n",
    "    distorsions.append(k_means.inertia_)\n",
    "fig = plt.figure(figsize=(15, 5))\n",
    "plt.plot(range(2, 20), distorsions)\n",
    "plt.grid(True)\n",
    "plt.title('Elbow curve')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Means Clustering with K = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "from pylab import plot,show\n",
    "from scipy.cluster.vq import kmeans,vq\n",
    "\n",
    "# computing K-Means with K = 5 (5 clusters)\n",
    "centroids,_ = kmeans(data,5)\n",
    "# assign each sample to a cluster\n",
    "idx,_ = vq(data,centroids)\n",
    "# some plotting using numpy's logical indexing\n",
    "plot(data[idx==0,0],data[idx==0,1],'ob',\n",
    "     data[idx==1,0],data[idx==1,1],'oy',\n",
    "     data[idx==2,0],data[idx==2,1],'or',\n",
    "     data[idx==3,0],data[idx==3,1],'og',\n",
    "     data[idx==4,0],data[idx==4,1],'om')\n",
    "plot(centroids[:,0],centroids[:,1],'sg',markersize=8)\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Return        DHFL\n",
      "Volatility    DHFL\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Finding the outlier\n",
    "\n",
    "print(annual.idxmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "annual.drop(\"DHFL\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "data = np.asarray([np.asarray(annual['Return']),np.asarray(annual['Volatility'])]).T\n",
    "\n",
    "# computing K-Means with K = 5 (5 clusters)\n",
    "centroids,_ = kmeans(data,5)\n",
    "# assign each sample to a cluster\n",
    "idx,_ = vq(data,centroids)\n",
    "# some plotting using numpy's logical indexing\n",
    "plot(data[idx==0,0],data[idx==0,1],'ob',\n",
    "     data[idx==1,0],data[idx==1,1],'oy',\n",
    "     data[idx==2,0],data[idx==2,1],'or',\n",
    "     data[idx==3,0],data[idx==3,1],'og',\n",
    "     data[idx==4,0],data[idx==4,1],'om')\n",
    "plot(centroids[:,0],centroids[:,1],'sg',markersize=8)\n",
    "show()"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}