ML project clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>DATE</th>\n",
       "      <th>SP500</th>\n",
       "      <th>NASDAQ.AAL</th>\n",
       "      <th>NASDAQ.AAPL</th>\n",
       "      <th>NASDAQ.ADBE</th>\n",
       "      <th>NASDAQ.ADI</th>\n",
       "      <th>NASDAQ.ADP</th>\n",
       "      <th>NASDAQ.ADSK</th>\n",
       "      <th>NASDAQ.AKAM</th>\n",
       "      <th>NASDAQ.ALXN</th>\n",
       "      <th>...</th>\n",
       "      <th>NYSE.WYN</th>\n",
       "      <th>NYSE.XEC</th>\n",
       "      <th>NYSE.XEL</th>\n",
       "      <th>NYSE.XL</th>\n",
       "      <th>NYSE.XOM</th>\n",
       "      <th>NYSE.XRX</th>\n",
       "      <th>NYSE.XYL</th>\n",
       "      <th>NYSE.YUM</th>\n",
       "      <th>NYSE.ZBH</th>\n",
       "      <th>NYSE.ZTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1491226200</td>\n",
       "      <td>2363.6101</td>\n",
       "      <td>42.3300</td>\n",
       "      <td>143.6800</td>\n",
       "      <td>129.6300</td>\n",
       "      <td>82.040</td>\n",
       "      <td>102.2300</td>\n",
       "      <td>85.2200</td>\n",
       "      <td>59.760</td>\n",
       "      <td>121.52</td>\n",
       "      <td>...</td>\n",
       "      <td>84.370</td>\n",
       "      <td>119.035</td>\n",
       "      <td>44.40</td>\n",
       "      <td>39.88</td>\n",
       "      <td>82.03</td>\n",
       "      <td>7.36</td>\n",
       "      <td>50.22</td>\n",
       "      <td>63.86</td>\n",
       "      <td>122.000</td>\n",
       "      <td>53.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1491226260</td>\n",
       "      <td>2364.1001</td>\n",
       "      <td>42.3600</td>\n",
       "      <td>143.7000</td>\n",
       "      <td>130.3200</td>\n",
       "      <td>82.080</td>\n",
       "      <td>102.1400</td>\n",
       "      <td>85.6500</td>\n",
       "      <td>59.840</td>\n",
       "      <td>121.48</td>\n",
       "      <td>...</td>\n",
       "      <td>84.370</td>\n",
       "      <td>119.035</td>\n",
       "      <td>44.11</td>\n",
       "      <td>39.88</td>\n",
       "      <td>82.03</td>\n",
       "      <td>7.38</td>\n",
       "      <td>50.22</td>\n",
       "      <td>63.74</td>\n",
       "      <td>121.770</td>\n",
       "      <td>53.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1491226320</td>\n",
       "      <td>2362.6799</td>\n",
       "      <td>42.3100</td>\n",
       "      <td>143.6901</td>\n",
       "      <td>130.2250</td>\n",
       "      <td>82.030</td>\n",
       "      <td>102.2125</td>\n",
       "      <td>85.5100</td>\n",
       "      <td>59.795</td>\n",
       "      <td>121.93</td>\n",
       "      <td>...</td>\n",
       "      <td>84.585</td>\n",
       "      <td>119.260</td>\n",
       "      <td>44.09</td>\n",
       "      <td>39.98</td>\n",
       "      <td>82.02</td>\n",
       "      <td>7.36</td>\n",
       "      <td>50.12</td>\n",
       "      <td>63.75</td>\n",
       "      <td>121.700</td>\n",
       "      <td>53.365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1491226380</td>\n",
       "      <td>2364.3101</td>\n",
       "      <td>42.3700</td>\n",
       "      <td>143.6400</td>\n",
       "      <td>130.0729</td>\n",
       "      <td>82.000</td>\n",
       "      <td>102.1400</td>\n",
       "      <td>85.4872</td>\n",
       "      <td>59.620</td>\n",
       "      <td>121.44</td>\n",
       "      <td>...</td>\n",
       "      <td>84.460</td>\n",
       "      <td>119.260</td>\n",
       "      <td>44.25</td>\n",
       "      <td>39.99</td>\n",
       "      <td>82.02</td>\n",
       "      <td>7.35</td>\n",
       "      <td>50.16</td>\n",
       "      <td>63.88</td>\n",
       "      <td>121.700</td>\n",
       "      <td>53.380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1491226440</td>\n",
       "      <td>2364.8501</td>\n",
       "      <td>42.5378</td>\n",
       "      <td>143.6600</td>\n",
       "      <td>129.8800</td>\n",
       "      <td>82.035</td>\n",
       "      <td>102.0600</td>\n",
       "      <td>85.7001</td>\n",
       "      <td>59.620</td>\n",
       "      <td>121.60</td>\n",
       "      <td>...</td>\n",
       "      <td>84.470</td>\n",
       "      <td>119.610</td>\n",
       "      <td>44.11</td>\n",
       "      <td>39.96</td>\n",
       "      <td>82.03</td>\n",
       "      <td>7.36</td>\n",
       "      <td>50.20</td>\n",
       "      <td>63.91</td>\n",
       "      <td>121.695</td>\n",
       "      <td>53.240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 502 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         DATE      SP500  NASDAQ.AAL  NASDAQ.AAPL  NASDAQ.ADBE  NASDAQ.ADI  \\\n",
       "0  1491226200  2363.6101     42.3300     143.6800     129.6300      82.040   \n",
       "1  1491226260  2364.1001     42.3600     143.7000     130.3200      82.080   \n",
       "2  1491226320  2362.6799     42.3100     143.6901     130.2250      82.030   \n",
       "3  1491226380  2364.3101     42.3700     143.6400     130.0729      82.000   \n",
       "4  1491226440  2364.8501     42.5378     143.6600     129.8800      82.035   \n",
       "\n",
       "   NASDAQ.ADP  NASDAQ.ADSK  NASDAQ.AKAM  NASDAQ.ALXN  ...  NYSE.WYN  NYSE.XEC  \\\n",
       "0    102.2300      85.2200       59.760       121.52  ...    84.370   119.035   \n",
       "1    102.1400      85.6500       59.840       121.48  ...    84.370   119.035   \n",
       "2    102.2125      85.5100       59.795       121.93  ...    84.585   119.260   \n",
       "3    102.1400      85.4872       59.620       121.44  ...    84.460   119.260   \n",
       "4    102.0600      85.7001       59.620       121.60  ...    84.470   119.610   \n",
       "\n",
       "   NYSE.XEL  NYSE.XL  NYSE.XOM  NYSE.XRX  NYSE.XYL  NYSE.YUM  NYSE.ZBH  \\\n",
       "0     44.40    39.88     82.03      7.36     50.22     63.86   122.000   \n",
       "1     44.11    39.88     82.03      7.38     50.22     63.74   121.770   \n",
       "2     44.09    39.98     82.02      7.36     50.12     63.75   121.700   \n",
       "3     44.25    39.99     82.02      7.35     50.16     63.88   121.700   \n",
       "4     44.11    39.96     82.03      7.36     50.20     63.91   121.695   \n",
       "\n",
       "   NYSE.ZTS  \n",
       "0    53.350  \n",
       "1    53.350  \n",
       "2    53.365  \n",
       "3    53.380  \n",
       "4    53.240  \n",
       "\n",
       "[5 rows x 502 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data_stocks.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41266, 502)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41266 entries, 0 to 41265\n",
      "Columns: 502 entries, DATE to NYSE.ZTS\n",
      "dtypes: float64(501), int64(1)\n",
      "memory usage: 158.0 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excersise 1:\n",
    "\n",
    "### Which are stocks are apparently similar in nature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.drop(['DATE', 'SP500'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cor = df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = pd.DataFrame(cor, columns=cor.index, index = cor.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NASDAQ.AAL    NASDAQ.AAL      1.000000\n",
       "NASDAQ.GOOG   NASDAQ.GOOGL    0.998352\n",
       "NASDAQ.DISCA  NASDAQ.DISCK    0.996855\n",
       "NASDAQ.NWS    NASDAQ.NWSA     0.996612\n",
       "NASDAQ.FOX    NASDAQ.FOXA     0.996182\n",
       "NYSE.DD       NYSE.DOW        0.986025\n",
       "NYSE.UA       NYSE.UAA        0.983613\n",
       "NYSE.DVN      NYSE.MRO        0.983269\n",
       "NYSE.CMS      NYSE.ES         0.981819\n",
       "              NYSE.DTE        0.981701\n",
       "NYSE.LMT      NYSE.RTN        0.981134\n",
       "NYSE.MA       NYSE.V          0.980574\n",
       "NYSE.DVN      NYSE.RIG        0.980150\n",
       "NASDAQ.ADBE   NYSE.MA         0.979629\n",
       "NASDAQ.ETFC   NYSE.BK         0.978342\n",
       "NYSE.DTE      NYSE.ES         0.978332\n",
       "NASDAQ.NVDA   NYSE.AON        0.977920\n",
       "NYSE.MCD      NYSE.YUM        0.977900\n",
       "NYSE.AEP      NYSE.DTE        0.977853\n",
       "NASDAQ.PYPL   NYSE.MA         0.977071\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr.stack().drop_duplicates().sort_values(ascending=False)[ :20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# These are the top 20 stocks which are similiar in performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excersise 2:\n",
    "\n",
    "### How many Unique patterns that exist in the historical stock data set, based on fluctuations in price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import sqrt\n",
    "from scipy.cluster.vq import kmeans,vq\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Returns</th>\n",
       "      <th>Volatility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NASDAQ.AAL</th>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.014326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NASDAQ.AAPL</th>\n",
       "      <td>0.000855</td>\n",
       "      <td>0.009879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NASDAQ.ADBE</th>\n",
       "      <td>0.001144</td>\n",
       "      <td>0.009637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NASDAQ.ADI</th>\n",
       "      <td>0.000198</td>\n",
       "      <td>0.012395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NASDAQ.ADP</th>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.011048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "              Returns  Volatility\n",
       "NASDAQ.AAL   0.000441    0.014326\n",
       "NASDAQ.AAPL  0.000855    0.009879\n",
       "NASDAQ.ADBE  0.001144    0.009637\n",
       "NASDAQ.ADI   0.000198    0.012395\n",
       "NASDAQ.ADP   0.000310    0.011048"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns = df.pct_change().mean()*252\n",
    "returns = pd.DataFrame(returns)\n",
    "returns.columns = ['Returns']\n",
    "returns['Volatility'] = df.pct_change().std()*sqrt(252)\n",
    "returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 2)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dta = np.asarray([np.asarray(returns.Returns), np.asarray(returns.Volatility)]).T\n",
    "dta.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dta\n",
    "distortions =[]\n",
    "for k in range(2,20):\n",
    "    k_means = KMeans(n_clusters = k)\n",
    "    k_means.fit(X)\n",
    "    distortions.append(k_means.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,5))\n",
    "plt.plot(range(2,20),distortions)\n",
    "plt.grid(True)\n",
    "plt.title('Elbow-Curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Steepness is achieved at 4 or 5 clusters so we will use 5 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "centroids,_= kmeans(dta, 5)\n",
    "# Assign each sample to a cluster\n",
    "idx,_ = vq(dta, centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
    "plot(dta[idx==0,0],dta[idx==0,1],'ob',\n",
    "     dta[idx==1,0],dta[idx==1,1],'oy',\n",
    "     dta[idx==2,0],dta[idx==2,1],'or',\n",
    "     dta[idx==3,0],dta[idx==3,1],'og',\n",
    "     dta[idx==4,0],dta[idx==4,1],'om')\n",
    "plot(centroids[:,0],centroids[:,1],'sg',markersize=8)\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So it looks like we have an outlier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returns       NYSE.XRX\n",
      "Volatility    NYSE.XRX\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#identify the outlier\n",
    "print(returns.idxmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "returns.drop(['NYSE.XRX'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XSUh6z5stBr/axq3ZjGcjEscYHAOu9HxfCByPW0ZEpuMYgj5V/aanTNYdU8B9P+V3clXdoqrLVHXZ3LlzY4jb3DRSCyoo/C2fCiGMejJ4pdzzZovBr7Zxazbj2YjEMQYvAItF5CoRaQFuA3YWldkJ3CEONwLnVfWEG2X0VWC/qn7Bp85a9/Na4JmSr2IK0UgtqFJTIdSbwSvlntfzeEYpVNu4NZvxbEQijYGqjgAbgGdxBoCfVtV9IrJeRNa7xXYB/cBh4HHgHnf7B4A1wIdE5CX3tcrd9yDwYRE5hBOpVBCuavjTSC2oUlMh1JvBK+WeT9Z4RrYvy3Ndz7EntYfnup4j25et6PHzVNu4NZvxbERiZS1V1V04Ct+7bbPnswKf8an3PfzHE1DVs8DKJMIa5WWwrAWdMzsTy1tNg5fty9Lf08/Q0SFaF7XS3dtNRyY8xXSp9zzu2rilku3LcmDdAXKDzpyKoYEhDqw7ABB5TUmp9vq99b4+8FTAZiA3GI3WgipF3mq5DPLKc2hgCHRceUa1puv1nvf39I8Zgjy5wRz71+6vaA8h3/tY8AsL+PojX+fEvzlRlcH6yQoGMPwxY9Bg1HM4pR+lyFst5RukPPt7+kPr1es9Hzo65L9jlAlGrlR3UqkG1Gg8YqWjqBdsBnLjUIo7xks1Zu/uSe2ZGBQNILAit6KsY9eC57qec5R0AK2drSw/snyCOwkg1ZZiyZYlkc8k6Bz5Yyel3N+FURpxZiDbSmdGxamEL7sa/vbWRa3+im1Ra0XPM1l093ZPUPJe8j2HsB5R1PMI6n0E9kpCmMwxDiM55iYyKk6p7pg81YqQ6e7tRloK4xmkReju7a7I8SebjkwHS7YscdJH+pA3cuUo9CBDmdSAZvuy7F+7v6zfhVFdzBhMASYr/DBPOcqn2j7qYrdoI7lJ/ejIdLB021JSbYV/5VRbaszIlaPQu3u7Q48dh/wzZdR/fym9DKPymDFocmoxAFiK8skbrP2fqHzr0XtsLhbtvEjDt0zzPYTWzlYQx5/vHQ8oR6FHHTsOfj1FL7Vy0012I6nesTGDJqccf3Gp+Pmyw5SP3wBnMaW2Hqt57CQyxB00jVvWr1zQgG6+fqkDtx2ZjrGy+fPuX7M/9nHC7m/SXkalsPGLiVg0UZNTqwiaJAowKioGgDQs3bY08R81zrFLjYyJQ5JInoP3HOT45uMFz0tahNQ7U4yeGx27j0DJ0UHlXsv+O4t6V9Nh6dfCn0vgMyjxmQbJlsTYVTpKqt6xaCJjUiNoSg0bjNUyd2PnIVnLLerY1W6Zxu2ZZfuyEwwBgA4ro2cdZ3u+9Zq6NDXpvT2AgxsP+rrZDm48GHreoJ5ipYxXKa38SkZJNQs2ZtDkVGIAMA7ljE3ENUyljB2EHbsU/3dS4iqd/p5+/x5cEbnBHCNnRxKdKw5x/Od5oxR3e55KjDuEUUr0WqWipJoJMwZNQNgfudp/xDzlhJP6GawgwhSe330IMoZLdyxl+ZHlVfcPx1U6lWiRlqrIJiPIoCPTwfIjy1mRW1Hx+15KK3+yGkmNhBmDBifOH7maf8Q85XS7/QzWtHZ/D2aQwgu6D8CkGMMg4iqdsluk0yk4ZpJImSBDfnDjwYJtQc8kaPtkUUorf7IaSY2EGYMGp9wJXpWi1G73WNjnmv0ALH3SabEv3rQ4UcstyjcfZQyrFWYYV+kE9o6KN/nmAIZpl00riPhJ0tIPMtijZ0cL6izetNh30t7iTYsn1J3MsM32Ve2JtueZjEZSI2HGoMGpl4Gw9lXtExRVVLc7TGklbblVeqLb/jX7OXjPwci6cYijdPyud+mOpSzdvrRgW9C4wsi58XGEQxsPJWoghBlsb52OTAfv2fqeAnnes/U9E65nsue2nN11NtF2wx+LJmpw6iHfTrYvy8ltJwsVlcAVa68IbW1Ftea98e1+5/RGLqVnp30HMlsXtY6XHRhyUjeMOoosH+3kOylK4fjm48z8wMxJazEGXa93W2BIpPu8s33ZxAPM3b3dzoS8GHXCnkmeyZ7bUi8NokbHjEGDk3SCVzUIUqZRLbNS/8R+oYTSIjCdgtDHVFuK9lXthffHtRfeMYXA8yllK7ArHr6C7FvBLeKOGR2cvPdk7ONFPe9SImg6Mh0c2njI14iU0qiYbOVcDw2iZsDcRA1OPQyElfrnL3Wcwc/46LAW5L5Jt6dZsmUJZ3edDZx9nG+thp0vrgIL8pGHGYI4+4uJet5RETRBJB2jCWOywzYtMqgyxOoZiMjNwCacTvYTqvpg0X5x968CBoFPquqP3H1bgV8FTqnq+zx17gf+G3Da3fQH7vKaRkLidN3jUOqksVJbZqX2agIVnkfn69saXtZzrKVPLnUGsH388XEUWNikp3LI9mULWuzp9jRXb7o68Hln+7JO884n7D/dng59luWmrPAS9lyjXHalUEnZpzKR6ShEJA0cxFm0/hjwAnC7qv7UU2YV8FkcY3ADsElVb3D3/XvgTWC7jzF4U1UfjiuspaOoHuUsgFJu3fyfeNrsaSjK6LlR0rPTCMLIuZEJf+5Y6StwB1wh1gIwfqkgvNcQZijDUhv88p2/HCmnfn7ifzDbl+XVu151ejxeAtI/hOVgmoxUFcX43S+YmEajljJOJeKko4jjJroeOKyq/ao6DDwFrC4qsxpH2auqPg/MEpH5AKr6D8C55OIbk0mSENVilwiUHsufj7RZ+uRScm/nnEFgdcIaR86O+EajxJ2kNnR0KLSstxdy9VeuZumTS32vISo6pho+8v6e/omGAAKzrAZmBk1TcB2+rqwqhIH6RVCFZS/NDeY4tPFQ2ec1SieOm2gB8Jrn+zGc1n9UmQXAiYhjbxCRO4C9wO+o6s9jyGMkIK7rJ65C83OJ5F0srZ2tLH2ytMRjUWmO8wu957NlXrH2Cs7uOuvIF+AaaV3UWuhCKHJNtK9qn5CB0y9JWVR0TJCbLE56iSDCDInfvjDXmdegFbuyzn//PCe+emLM8AwNDPHqXa8Clc/eGWUcR86OjIUVTwa2BGchcXoGftNcin/mccoU8xjwbuA6HKPxJ74nF1knIntFZO/p06f9ihgBJIn3jjvoFxQ5BOOK5B/n/GPiVmbcZHX56zj+2HFG3xxl6ZNLIxd3GWul6gpWjKxgha6gu7ebk9tOxro3UYYySTqNuITmVPLZF/X8ggza8ceOT+iB6LBWpZUeZ/xl/9r9k7KuQC3W+ah34vyCjwFXer4vBI6XUKYAVc2q6qiq5oDHcdxRfuW2qOoyVV02d+7cGOIaeZK4fuJGZEQp7bEsm1VKVudl5OxISSknki7BGKVoo5afLIXu3m5kmv90Y7+ZtVHPL6nLKmiuQhRhLqdYRtPNTlttpVwvM/friThuoheAxSJyFfAz4DbgvxaV2Ynj8nkKx4V0XlVDXUQiMt9T5lbgJ4kkNyJJ4suOG5ER6BIJIGiyUfHA8eiF8MyXUcePm07Ab6DYy9DA0Jgi8srnN4dhQtSTzyVc/ubl/Pwdwd7Pjhn+MndkOji48aDvRDq/+RtRzy9oUl4liUolnZcl6LryTEY6bpuoNpFIY6CqIyKyAXgWp+2zVVX3ich6d/9mYBdOJNFhnNDSO/P1ReTrwApgjogcAz6vql8FHhKR63D+lkeAT1fusgxIHvIZJ0TVL2wwiqhxB79WqMwQ0pekY7VQk8wFCDMEefav3Y+kZcx9MnJ2xFlkpr1wkZniXEB+fPPhb459jlo4pdiHHaQwhwaG2JPaUyBHmP8725cl96/xnxc4oahJiTvzOB/2G8bQwAXo64NMJvb5k4wB2ES1icRydKrqLlW9WlXfraq97rbNriHAjSL6jLv/WlXd66l7u6rOV9XpqrrQNQSo6hq37L9V1Y9G9SSM5FRjMk7BpCcITJzmJda4QxEtc1r44JkPjp8nwfGDiLtmAKP4+tFHz040BPnjRhpHcZR40DiKnw879N563HAH7zkY6v8OjEwKYjpcvenqwm19fdDVBamU897XN6FaYGs7r9iJea+AVrKwbp3vefxIOgZgE9UmYjOQm5hqzU72Dsh6wzGntbvuFA+ljDt4y0T5mZP8gSvhAihQMq6CHBp4O7ySUDDI7qekggfmwxVnbjDH8S3HQ/3fsa7bNTytna0T5zH09TmKeWAAVJ13H0UdOLbiUexxZElxgW6egMFB6OmJlp3kYwD1MHO/3rA1kI2KEqernnRd4qCJaUnDAeNOVotDa+oUy3O/4RyXrzPEFRPLpM/ArFkMnZ3ojW1Nn2F57tdh0SLo7WXPmgUBvZYcrZxiiHk4GjsocM9nu7vO9XNzvuMrAykFlej72NXlGIBiOjvhyJGxr76TD7nAEh6mg93Q2encK99nMAKkaOUU3TzhlAcQgVx0T6JWa303CrYGsjHpVGLcobi1X6l0G6WMdwQxlJszflye4AD3kuOSsW0pLtA9upn9Z//Av/7obGC8ld06+xl/o8EplnM7EGx0nJFrn7puS92R75MT5Fty+Z/Rceap6Is9ejTW9rFB7E98lyHmTVTsR4/S/WQ3B+7aR254vLdXYDCKWbQoWj5sDKASmJvImHSKu+jT2qc5A5aldtdj+LMBOvgOSy79Cq2cBJTW9hHedfe7Jri24tDKKc9xd7OEh93j5mjl5Jhy85YLqs/gIN08QUoKldmYu8SlmydIcWFCmXfxrYnbZWh8nsW5p/3lO/e0Uzjq/gUpZJ/tHZkOlrdvYAUrWc7thQq+rc159u/c4nuvJtDWBr29/ucuwsYAysfcREZjk/dnDw6Ob2trgy1bCiNRQsplucmdoXyBNG+Q41KUFs9JCt0woS3ZIrKs9O01TKgvQlY/RD+f8m9Ve47nVyZoO+k0jAaEcXZ2Osr2rrtgeHh8e0sLbN06fv/i3uN82TvuCHbt7NgBa9Y4Yw9hpFKwfbvzuafH6YW4LrWgCCObURxMHDcRqtowr1/6pV9Swyigs1PVUS2Fr87O5OXcMidZqT/g6/pddusP+LoemNGjP+j8gX5Xvqs/SP+5nmSl/7ECXsXH863f2ana3p7ouGW92tpUd+wIPmd7e+H927HDkVHEed+xY+KzuPtuZ3/YeTs7g59F8evuux05vdtaWhzZwuRIIvMUAdirEfq15go+ycuMgTGBIOUjkrxcmCLLK5X2dtXp04PLRSnDUhRzpV9exRhWLi5JZfdT8n6vdDr+vfOTqfgcQWWnAGYMjOYnqJXZ3l7YKgxSVj49g0gF39ISrqCSKMZ02lGOqqUZklJeeYW4Y0d4uTj4Kd04Cvzuu+P3EKJexb3AsGfpV3YKEMcYWDSR0dj09k70Z7e0wBtvwFk3bcPAAEyf7mz3+saLByhXrYLHHpt4DtXC78PDwb74fLjlTTfB7ugxBUZHYds2+MAHHJ+4XwhnTK64F7LvCN7f8SacfJjx2P277gou3D4x/5EvPT2F9z4Og4Owa9d4WOqcOePPqhT8op1iRkAZ41g0kdHYZDLOQGZnpxOT3tkJ73wnXLxYWO7iRWe7t1zxAOiuBAvtjY46x/GSNy5xDUGe/OSq3l7nGCUSZggK9h89Chs3FhpGL9Onw6ZN8U5aqnL11tu0yTHUXoq/h+EX7ZQgAioRMSPXGhEzBkbjk8k4rcxcznk/F7CW0rlzheWKo1KSKjZvjyFvXCCZIfCe22vYqkkqFd4Sv3jRMRZxFF2pyrW4XnHvSzVe7yQo/NTPsCYIVfUl5kzsRsWMgdF8lNoqLFWxtbePG5eY6RMCz53JlKew4hAUaurl7Fn4xCccd9g99wSXK7U3473Gnh7/nhxMPPb06c79Durd5fHrMa5d65yr1Fa9n0ssQcqMeseMgdF8lNoq9KtX7Aryw9vKLtVtMjDgnOsd7wj35U82uZwzjuI1CF5XSU+Po2ST9GZSqUIFHnTPzp2bqNC/9jU4cya4d+fF22Ps7XXGZspp1Tf5OIQZA6P58GsVBrUgo+oVuy+CyLc0y/VJv/VWsC8/T7qCq+jE5U//1Hn3c5Vs2+Yo2x074vUSiiekBd0z1fGxFK/yL8VvX4lWfbXGIeqFqHCjenpZaKkx6SQNf5wxI3weQhVf3B/9KjskNSpk0zvRK054a75OWHiqd35AqfMH4s5HCaOB5y4QI7TU0lEYRhh+qRiimDbN8ctP8n9L7o8uozHKBNLZ6bhE/K7LL7tomIuts5PsD3vp7+9haOgo79ozm3d/FdLHAga202nn+KlUeEhvEDEzr0bS1xc7PUY9EScdhbmJDCOMYtdRe3t0lMvIyKQbAnDmEZSzP0+Q5Hp0gNEFs333Tdje1xdqDPToAAcOrGNoaABQjq84y/f73kaDquSNa9Dgd5TfvlLRRcWRaw1gCOJik84Mw4dsto9DhzYyMnIWFkD6yXauvvpJOjrcP3+cgeUyyCvkJGc5+XDyc2gKUsUN+oDyQ/Pg/33yDZY8PI3UhfHlSEdb4eAn32B2tm/8/vT0hBrEoXmQyxX2tnK5QYbmwSX+i5OF4/HbZ7N9Yz2O1tZFdHf30pHxyNVgrfrJwoyB0ZRks30cPLiR0VHH7TBtWjuLF28aV1YRdV999S5UxwdyR0fPsn+/s7R3R0eG3OUzSP38rZJkU6KV/MXL4PBnYcnDkK7SGu1DHdDqn2F7goyjrdD/KTi18iKq0P2EU3do3vj2Mwc3ukp4gP9wNPga88fyo/9TJVyzp4WfzfZx4MC6MUMzNOT0QADHIJjyDySWm0hEbhaRAyJyWETu89kvIvKou/9lEXm/Z99WETklIj8pqjNbRL4tIofc98vLvxzDcBTC/v13jhkCgJGRs7z66l1ks9GRJ/39PQWGYJyL9Pc70SeHf+sScgmCenIpUIELHfCz1TB8mdsyZ6JbJjfNMQSnboID946XrSS5aY7iHZrnv//iZY6seZkP3OvIA87780/B3/+d857fPjp61nX7BB83lyo8VjH5a86fOxegofL3c3Rhe0GkWH9/j2+PI//cjGAiewYikga+DHwYOAa8ICI7VfWnnmK3AIvd1w3AY+47wJ8BXwK2Fx36PmC3qj7oGpj7gN8r/VKMqc64e8A/v4/qMP39PQW9Az+XwtBQsP85v+/4inOMjLgt5KzjbpGco0SnvQUpj2t7tHWiAjz8ufHP877j09L2KN5TN3nKFLlQvK3v4ta833cY73Xkz1HcEh9tLdxfCn4tfL/74Ef+msG57vd8oYXU255emuc4ra3vYPny8ecZ9OzCnqnhEMdNdD1wWFX7AUTkKWA14DUGq4HtbgjT8yIyS0Tmq+oJVf0HEenyOe5qYIX7eRuwBzMGRokUuweC8CqFIJdCOj27oFfhpbXV8U2LtHHqprd8FVuYcvfDq/ySlCk+z5kbYc7z498XfQpOh6zidvkP4JvuMZPIG4dTFTquU3448DhDQwM891zXmDEPenb552YEE8cYLABe83w/xnirP6zMAuBEyHE7VPUEgKqeEBHfjqWIrAPWASxqlskdRsXxcw/4k2LPnhStrYsYHX3T16UwbdqliLT4uIqm093dSzbbh2rweEEc5V4J/M5z2PP59N+H1//5xeDjVIJKHTf8ODLumhoaQKQFZx3T8fQWqVQb3d1VTvHRBMQZM/AbByp2YcYpUxKqukVVl6nqsrlz51bikEYTEt8NMAooQ0MDTqSQDyMj53jPe7Yybdp4CGk63c7SpV+joyNj/ue6QShWM6rDTJt2Ga2tnYDQ2trJkiVbYgUOTHXi9AyOAVd6vi8EjpdQpphs3pUkIvMhYOVww4hBa+uiwLEChxQQsC6vz7E6OjKBCsT8z7VGQp/3yMg5PvjBM5MsU+MTp2fwArBYRK4Spw92G7CzqMxO4A43quhG4HzeBRTCTmCt+3kt8EwCuQ2jAMcN4B/M6LQS43VU47gUzP9cO1pbO1mxIsfy5Ufc5+pXxp5PKUQaA1UdATYAzwL7gadVdZ+IrBeR9W6xXUA/jsvycWAsxaGIfB14DlgiIsdE5DfdXQ8CHxaRQziRSg9W6JqMKYjTivdX+PnBRT/S6fbELoXu7l5SqeKEbAIkWJDFSEyxofZ7DlHGPJvt47nnutizJ8Vzz3XFCjWeKsSadKaqu3AUvnfbZs9nBT4TUPf2gO1ngZWxJTWMCFpbO31dB/mQ0eJoo1SqjauvjjcRzUu+/NgMZSCdns0733kdr78evLBNKtUWc5DbmIhwxRVrC55V/vOE2cYBzzN0QpqNKVhuIqN5CGspdnRkWLJkS0UHFnO5t8c+j46e5fXX/y6wbP58466N6qazaD6Us2cnLkva0ZFh+fIjY66jsOdpE9LCsXQURtMQ1VIMGxROin8oa/C4xIkTvdx+e4ajRzM8/XQXc+aUvvB9XC6fPh4+GrS/VGbNWsnrr+/Bic6aHMIDBOLUtwlpYZgxMJqKIIVf6czDSRTI6Gg7a9ZkxrJgz549Ocrnm79cvWO//fZhli7dFmuiX+Uob1GfoAgkG3B2MDeR0fRUYx3zkRF/BZLLFbp/Uqk2Hn98U8FyCKdOBSufelpeJEyWCxeOcsMNGf7wD7dw5kwnqsIbb7Tz+uvt5HLC6Gi0aim+V9GU1wspZcB5KmHGwGh6Kr2OeV8ffOELvVy4UKhYLlxo45ln1nPyZCe5nHDmjDNO8PTThV2QJ56YWBeczKp/8zd38/rr7WNLab311gxGRiZ/mcvh4RbOnw9etyGbXcTAAOzeneHjHz/Chz6UY/XqM9x66xlWrszxR3+0PdCYqMLJk50888z6CfdheLiF0dGwEOHSqca4UTNhbiKj6an0OuY9PTAwkGF4GD71qR7mzTvKqVOLeOKJXnbvzvDoo065/OJfixYVLrK1e7ejfNav72HOnPGxje98J8NDDwF8peB8K1f28du//Wna2t4qWEZheLgFUFpaQgYGXHI5R57C+tMZHLyMyy47xxtvOIvTXHbZubFrAfjd371zwvGHh1vG9gexe3eG9773+6xevZlUatwqXLjQxsMPbxm7B/v2fWDCPQS49951XHJJYeRXJVrwlRw3ajZs2Uuj6anUiod5Uql47pz88f1WzhSB9evhKx69HyRnnpUr+3wVZ37bG2/MZvr0C7S1OXmTcrkUIjlOnepk7SuvM9xyPvjgb3bAwyd9z7lhw0ZmznRCaM+fb+dLX9o0psyj8JM5Tt3f+I0+Nm6MFzJqRBNn2UszBkbT46eM29oK0uAnIkpp59mxY/z499wDmzcXGpFiGaq6eNr9MQ5+f33ognKejeGPrYFsGExcxrizszxl47ecbtB58+zaNbE34R23iFgyeMrQ3g6XXgpr1jhGt5xBfiMZNmZgTAkqueKhdzndOD0EiB63iFgyuGRE6itCKYq33x7vweWjvsB6CZOB9QwMowQyGWc8oD0g4Ma7va/PGWfwI79ER6mD2VE0kiFIpysb9WUkw4yBYZTBpk3QUpSfrqXF2Q7j4xWjPiHynnXcsXWb/O8RVM9QGoWYMTCMMshkYOvWwvGIrVsLXUnFrV1wWsHecYu44xBJaKQxiPZ25975YYZycrAxA8Mok7DxiKBWbS5XWCf/eeNGOFu0ANv06U5v463glTYnMGOG856kTq1oaxvvSflFffXaBOFJwXoGhlFFglq1ftszGX+3kwjccYdjFOKQTsPwcJEheLMjvFLU/gja2+P3bPK9gOLIrkpHfRnJsHkGhlFFks5xCJsg19sLa9cG+9ZrRf56YDzCKiiKyeYQ1AabZ2AYNSZpazcsBDWTgW3bKj+2UArptH/L/sgRxwg8+eT4GEDaTa1kLf36xnoGhlFHxEmd4U3HnUrVpqeQz7tkNAYV6xmIyM0ickBEDovIfT77RUQedfe/LCLvj6orIveLyM9E5CX3tSrJxRlGM+IXVVQ8iJpvgedy1e0phEUjWYRP8xFpDEQkDXwZuAW4BrhdRK4pKnYLsNh9rQMei1n3i6p6nfuauKadYUwxkrqV8uXTFc5y3dbmJNLzm1RnET7NSZyewfXAYVXtV9Vh4ClgdVGZ1cB2dXgemCUi82PWNQzDg7flf+RItI89k6mMy6Z4HOArX4EzZ5yEexbh0/zEMQYLgNc834+52+KUiaq7wXUrbRWRy/1OLiLrRGSviOw9ffp0DHENY+oR5raZMQPuvntcobe3TwxTbWtzXE5+BiipcTIakzjGwM9zWDzqHFQmrO5jwLuB64ATwJ/4nVxVt6jqMlVdNnfu3BjiGsbUo7c32Mc/Z47Tys8r9DNn4Gtfs9a+UUgcY3AMuNLzfSFwPGaZwLqqmlXVUVXNAY/juJQMwyiBTCY4KZ1fuKq19o1i4hiDF4DFInKViLQAtwE7i8rsBO5wo4puBM6r6omwuu6YQp5bgZ+UeS2GMaUJyu2jamsDGNFEGgNVHQE2AM8C+4GnVXWfiKwXkfVusV1AP3AYp5V/T1hdt85DIvKKiLwM/Efgtyt3WYYx9ejtnZjKIk9+bQAzCEYQNunMMJqIOXMmJrrzUuq6z0ZjY+koDGOKce5c+H5bG8AIwoyBYTQRUTODbeawEYQZA8NoIsIWybGZw0YYZgwMo4nwprMAyxhqxMdWOjOMJiNs5TXDCMJ6BoZhGIYZA8MwDMOMgWEYhoEZA8MwDAMzBoZhGAYNlo5CRE4DPivEVoQ5wJkqHbvSmKzVoVFkbRQ5wWStFkll7VTV0DUAGsoYVBMR2RuVu6NeMFmrQ6PI2ihygslaLaohq7mJDMMwDDMGhmEYhhkDL1tqLUACTNbq0CiyNoqcYLJWi4rLamMGhmEYhvUMDMMwDDMGhmEYBlPAGIjIbBH5togcct8vDyh3s4gcEJHDInJf3PoiskhE3hSRe+tRThG5XkRecl8/FpFby5GzyrJ+WERedNfGflFEPlTHsraLyHfdZ/+lMmX0Pbdnv4jIo+7+l0Xk/aXKXS5VkvXjIrJPRHIiUpFwySrJ+b9F5FW3/F+IyKw6lvV/umVfEpG/FZF3RQqiqk39Ah4C7nM/3wf8sU+ZNPAvQDfQAvwYuCZOfeAbwJ8D99ajnEAbMM39PB84lf9eh7L+IvAu9/P7gJ/V6/MHZgAfBNYDXypDvsBze8qsAv4aEOBG4Ifl/m7rTNalwBJgD7CsjuX8Fc9/6Y/r/J5e5qn/W8DmKFmavmcArAa2uZ+3AR/zKXM9cFhV+1V1GHjKrRdaX0Q+BvQD++pVTlUdVNURd/slQCUiBqol6z+r6nF3+z7gEhFprVNZ31LV7wEXypQv7Nzea9iuDs8Ds0Rkfily16OsqrpfVQ9UQL5qy/m3nv/S88DCOpb1DU/9GcT4308FY9ChqicA3Pd5PmUWAK95vh9ztwXWF5EZwO8BD9SznK6sN4jIPuAVYL3nB113snr4L8A/q+pQA8haDmHnjioz2XJXS9ZKMxly3oXTWi+XqskqIr0i8hqQAf5HlCBNsdKZiHwHuMJnV0/cQ/hsi7KkDwBfVNU3Rfyq+5ykNnKiqj8E3isiS4FtIvLXqhraoq2VrO6534vTDf+VmOVrJmsFiHPuoDKTLXejyFpVOUWkBxgB+kqSLp4cccqE1lXVHqBHRH4f2AB8PkyQpjAGqnpT0D4RyYrIfFU94XatTvkUOwZc6fm+EMi7K4Lq3wD8mog8BMwCciJyQVUDBxNrJKf3/PtF5C0cf/zeIFlqKauILAT+ArhDVf8lTMZay1ohws4dVaYlpG415K6WrJWmanKKyFrgV4GV6jrk61VWD/8H+CsijMFUcBPtBNa6n9cCz/iUeQFYLCJXiUgLcJtbL7C+qv47Ve1S1S7gEeB/hRmCWsnplp3mfu7EGag7Uoac1ZR1Fs6P9vdV9ftlylhVWStI2Lnz7ATucKNKbgTOu66fyZa7WrJWmqrIKSI347iGP6qqg3Uu62JP/Y8Cr0ZKEjXC3OgvoB3YDRxy32e7298F7PKUWwUcxBmd74mqX3SO+yk/mqgqcgJrcAZjXwJ+BHysXu8p8N+Bt1xZ86959Siru+8IcA54E6f1dk2JMk44N06U0nr3swBfdve/gifippzfbR3Jeqt7/4aALPBsncp5GMdHn/9tRkbo1FDWbwA/AV4GvgUsiJLD0lEYhmEYU8JNZBiGYURgxsAwDMMwY2AYhmGYMTAMwzAwY2AYhmFgxsAwDMPAjIFhGIYB/H+7Tl6KYDvYagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dta = np.asarray([np.asarray(returns.Returns), np.asarray(returns.Volatility)]).T\n",
    "centroids,_= kmeans(dta, 5)\n",
    "# Assign each sample to a cluster\n",
    "idx,_ = vq(dta, centroids)\n",
    "\n",
    "# some plotting using numpy's logical indexing\n",
    "plot(dta[idx==0,0],dta[idx==0,1],'ob',\n",
    "     dta[idx==1,0],dta[idx==1,1],'oy',\n",
    "     dta[idx==2,0],dta[idx==2,1],'or',\n",
    "     dta[idx==3,0],dta[idx==3,1],'og',\n",
    "     dta[idx==4,0],dta[idx==4,1],'om')\n",
    "plot(centroids[:,0],centroids[:,1],'sg',markersize=8)\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('NASDAQ.AAL', 4),\n",
       " ('NASDAQ.AAPL', 1),\n",
       " ('NASDAQ.ADBE', 1),\n",
       " ('NASDAQ.ADI', 2),\n",
       " ('NASDAQ.ADP', 2)]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "details = [(name,cluster) for name, cluster in zip(returns.index,idx)]\n",
    "details[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    163\n",
       "2    119\n",
       "0    104\n",
       "4     87\n",
       "3     26\n",
       "Name: Cluster_No, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(details, columns=['Stock_Name','Cluster_No'])\n",
    "df1.Cluster_No.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "ind = df1.Cluster_No.value_counts()\n",
    "plt.figure(figsize=(12,4))\n",
    "sns.barplot(x=ind.index, y=ind.values, alpha=0.6)\n",
    "plt.ylabel('Number of Occurences',fontsize=12)\n",
    "plt.xlabel('Cluster Number',fontsize=12)\n",
    "plt.title('Barplot - Indicator',fontsize =16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excercise 3 PCA:\n",
    "\n",
    "### Identify which all stocks are moving together and which all stocks are different from each other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.4703 0.6426 0.7628 0.8113 0.8493 0.8774 0.8974 0.9105 0.921  0.9285\n",
      " 0.9353 0.9411 0.9457 0.9502 0.9543 0.9578 0.9611 0.9637 0.966  0.9682\n",
      " 0.9701 0.9717 0.9732 0.9745 0.9758 0.977  0.9781 0.9791 0.98   0.9808\n",
      " 0.9816 0.9824 0.9831 0.9838 0.9844 0.985  0.9856 0.9861 0.9866 0.9871\n",
      " 0.9875 0.9879 0.9883 0.9887 0.9891 0.9894 0.9897 0.99   0.9903 0.9906\n",
      " 0.9909 0.9912 0.9915 0.9917 0.9919 0.9921 0.9923 0.9925 0.9927 0.9929\n",
      " 0.9931 0.9933 0.9935 0.9937 0.9939 0.9941 0.9943 0.9945 0.9946 0.9947\n",
      " 0.9948 0.9949 0.995  0.9951 0.9952 0.9953 0.9954 0.9955 0.9956 0.9957\n",
      " 0.9958 0.9959 0.996  0.9961 0.9962 0.9963 0.9964 0.9965 0.9966 0.9967\n",
      " 0.9968 0.9969 0.997  0.9971 0.9972 0.9973 0.9974 0.9975 0.9976 0.9977\n",
      " 0.9978 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979\n",
      " 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979\n",
      " 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979\n",
      " 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979\n",
      " 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979 0.9979]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X = df.values\n",
    "\n",
    "sc = StandardScaler().fit_transform(X)\n",
    "pca = PCA(n_components=150).fit(sc)\n",
    "\n",
    "var = pca.explained_variance_ratio_\n",
    "\n",
    "# cumulative varince explains\n",
    "var1 = np.cumsum(np.round(var, decimals=4))\n",
    "print(var1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f487494ca90>]"
      ]
     },
     "execution_count": 26,
     "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"
    }
   ],
   "source": [
    "plt.plot(var1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_scaled = PCA(n_components=25).fit(sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NASDAQ.AAL</th>\n",
       "      <th>NASDAQ.AAPL</th>\n",
       "      <th>NASDAQ.ADBE</th>\n",
       "      <th>NASDAQ.ADI</th>\n",
       "      <th>NASDAQ.ADP</th>\n",
       "      <th>NASDAQ.ADSK</th>\n",
       "      <th>NASDAQ.AKAM</th>\n",
       "      <th>NASDAQ.ALXN</th>\n",
       "      <th>NASDAQ.AMAT</th>\n",
       "      <th>NASDAQ.AMD</th>\n",
       "      <th>...</th>\n",
       "      <th>NYSE.WYN</th>\n",
       "      <th>NYSE.XEC</th>\n",
       "      <th>NYSE.XEL</th>\n",
       "      <th>NYSE.XL</th>\n",
       "      <th>NYSE.XOM</th>\n",
       "      <th>NYSE.XRX</th>\n",
       "      <th>NYSE.XYL</th>\n",
       "      <th>NYSE.YUM</th>\n",
       "      <th>NYSE.ZBH</th>\n",
       "      <th>NYSE.ZTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.039258</td>\n",
       "      <td>-0.041064</td>\n",
       "      <td>-0.062908</td>\n",
       "      <td>-0.009788</td>\n",
       "      <td>-0.035866</td>\n",
       "      <td>-0.054668</td>\n",
       "      <td>0.050356</td>\n",
       "      <td>-0.035396</td>\n",
       "      <td>-0.044060</td>\n",
       "      <td>-0.014671</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.049413</td>\n",
       "      <td>0.057001</td>\n",
       "      <td>-0.054623</td>\n",
       "      <td>-0.050608</td>\n",
       "      <td>0.047199</td>\n",
       "      <td>-0.057897</td>\n",
       "      <td>-0.061392</td>\n",
       "      <td>-0.062477</td>\n",
       "      <td>0.002538</td>\n",
       "      <td>-0.051698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.064284</td>\n",
       "      <td>0.033861</td>\n",
       "      <td>0.001861</td>\n",
       "      <td>-0.032453</td>\n",
       "      <td>0.043511</td>\n",
       "      <td>-0.029519</td>\n",
       "      <td>0.016178</td>\n",
       "      <td>0.058286</td>\n",
       "      <td>-0.052667</td>\n",
       "      <td>0.012376</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.058941</td>\n",
       "      <td>0.030277</td>\n",
       "      <td>0.013643</td>\n",
       "      <td>-0.040769</td>\n",
       "      <td>-0.052795</td>\n",
       "      <td>0.010238</td>\n",
       "      <td>0.031466</td>\n",
       "      <td>-0.020406</td>\n",
       "      <td>-0.081229</td>\n",
       "      <td>-0.059501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.039858</td>\n",
       "      <td>0.064165</td>\n",
       "      <td>0.012079</td>\n",
       "      <td>0.043266</td>\n",
       "      <td>-0.037239</td>\n",
       "      <td>0.040506</td>\n",
       "      <td>-0.053892</td>\n",
       "      <td>-0.051099</td>\n",
       "      <td>0.030738</td>\n",
       "      <td>-0.086428</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003223</td>\n",
       "      <td>0.022125</td>\n",
       "      <td>0.059617</td>\n",
       "      <td>-0.034587</td>\n",
       "      <td>-0.028712</td>\n",
       "      <td>-0.039074</td>\n",
       "      <td>-0.004342</td>\n",
       "      <td>0.021010</td>\n",
       "      <td>-0.066373</td>\n",
       "      <td>0.023570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.007578</td>\n",
       "      <td>0.077164</td>\n",
       "      <td>0.008564</td>\n",
       "      <td>-0.027896</td>\n",
       "      <td>-0.017418</td>\n",
       "      <td>0.008973</td>\n",
       "      <td>-0.032239</td>\n",
       "      <td>0.034448</td>\n",
       "      <td>0.051511</td>\n",
       "      <td>-0.077594</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021692</td>\n",
       "      <td>0.060885</td>\n",
       "      <td>-0.039630</td>\n",
       "      <td>0.027694</td>\n",
       "      <td>0.012100</td>\n",
       "      <td>-0.039575</td>\n",
       "      <td>0.002900</td>\n",
       "      <td>-0.002703</td>\n",
       "      <td>-0.049420</td>\n",
       "      <td>0.015160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.033303</td>\n",
       "      <td>-0.016981</td>\n",
       "      <td>0.002438</td>\n",
       "      <td>-0.038330</td>\n",
       "      <td>-0.102023</td>\n",
       "      <td>-0.034831</td>\n",
       "      <td>-0.011187</td>\n",
       "      <td>0.037595</td>\n",
       "      <td>-0.039683</td>\n",
       "      <td>-0.014357</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.036672</td>\n",
       "      <td>-0.011451</td>\n",
       "      <td>-0.030314</td>\n",
       "      <td>-0.012030</td>\n",
       "      <td>-0.045731</td>\n",
       "      <td>0.029255</td>\n",
       "      <td>-0.003013</td>\n",
       "      <td>-0.001339</td>\n",
       "      <td>-0.003603</td>\n",
       "      <td>0.005364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   NASDAQ.AAL  NASDAQ.AAPL  NASDAQ.ADBE  NASDAQ.ADI  NASDAQ.ADP  NASDAQ.ADSK  \\\n",
       "0   -0.039258    -0.041064    -0.062908   -0.009788   -0.035866    -0.054668   \n",
       "1   -0.064284     0.033861     0.001861   -0.032453    0.043511    -0.029519   \n",
       "2   -0.039858     0.064165     0.012079    0.043266   -0.037239     0.040506   \n",
       "3    0.007578     0.077164     0.008564   -0.027896   -0.017418     0.008973   \n",
       "4   -0.033303    -0.016981     0.002438   -0.038330   -0.102023    -0.034831   \n",
       "\n",
       "   NASDAQ.AKAM  NASDAQ.ALXN  NASDAQ.AMAT  NASDAQ.AMD  ...  NYSE.WYN  NYSE.XEC  \\\n",
       "0     0.050356    -0.035396    -0.044060   -0.014671  ... -0.049413  0.057001   \n",
       "1     0.016178     0.058286    -0.052667    0.012376  ... -0.058941  0.030277   \n",
       "2    -0.053892    -0.051099     0.030738   -0.086428  ... -0.003223  0.022125   \n",
       "3    -0.032239     0.034448     0.051511   -0.077594  ...  0.021692  0.060885   \n",
       "4    -0.011187     0.037595    -0.039683   -0.014357  ... -0.036672 -0.011451   \n",
       "\n",
       "   NYSE.XEL   NYSE.XL  NYSE.XOM  NYSE.XRX  NYSE.XYL  NYSE.YUM  NYSE.ZBH  \\\n",
       "0 -0.054623 -0.050608  0.047199 -0.057897 -0.061392 -0.062477  0.002538   \n",
       "1  0.013643 -0.040769 -0.052795  0.010238  0.031466 -0.020406 -0.081229   \n",
       "2  0.059617 -0.034587 -0.028712 -0.039074 -0.004342  0.021010 -0.066373   \n",
       "3 -0.039630  0.027694  0.012100 -0.039575  0.002900 -0.002703 -0.049420   \n",
       "4 -0.030314 -0.012030 -0.045731  0.029255 -0.003013 -0.001339 -0.003603   \n",
       "\n",
       "   NYSE.ZTS  \n",
       "0 -0.051698  \n",
       "1 -0.059501  \n",
       "2  0.023570  \n",
       "3  0.015160  \n",
       "4  0.005364  \n",
       "\n",
       "[5 rows x 500 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp = pd.DataFrame(pca.components_, columns = df.columns)\n",
    "comp.head()"
   ]
  }
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