time_series_clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series Clustering for Minute Univariate Intraday Stock Price for Trend Estimation\n",
    "_________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some facts about the given data:\n",
    "- The data is an unlabeled collection of daily variations for a singular stock logged every minute\n",
    "- There are 277 days worth of data and 1440 data points, corresponding to one observation every minute\n",
    "- All days have an equal number of data (1440) and no data point is absent\n",
    "\n",
    "Here the data is graphed over its entire lifetime and the mean for each day is plotted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Trend over given time')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv('simulated_data.csv')\n",
    "\n",
    "df_daily_mean = df.groupby('day')['price'].mean()\n",
    "df_daily_mean.plot(figsize=(16, 8))\n",
    "plt.xlabel(\"days\")\n",
    "plt.ylabel(\"price\")\n",
    "plt.title(\"Trend over given time\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing and Cleaning\n",
    "_________________\n",
    "\n",
    "### Cleaning \n",
    "\n",
    "Cleaning the data and getting rows of time series, where each row is a time series of price and each column is its corresponding time (in minutes) during the day. The original data can be interpreted as a multiindex pandas dataframe and unstacking it gives a 2D array where each row is a separate series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(277, 1440)\n"
     ]
    },
    {
     "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>time</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>1430</th>\n",
       "      <th>1431</th>\n",
       "      <th>1432</th>\n",
       "      <th>1433</th>\n",
       "      <th>1434</th>\n",
       "      <th>1435</th>\n",
       "      <th>1436</th>\n",
       "      <th>1437</th>\n",
       "      <th>1438</th>\n",
       "      <th>1439</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.477110</td>\n",
       "      <td>3.475698</td>\n",
       "      <td>3.475874</td>\n",
       "      <td>3.478345</td>\n",
       "      <td>3.476757</td>\n",
       "      <td>3.478169</td>\n",
       "      <td>3.475874</td>\n",
       "      <td>3.475522</td>\n",
       "      <td>3.471992</td>\n",
       "      <td>3.470050</td>\n",
       "      <td>...</td>\n",
       "      <td>3.428224</td>\n",
       "      <td>3.429282</td>\n",
       "      <td>3.428753</td>\n",
       "      <td>3.426459</td>\n",
       "      <td>3.426812</td>\n",
       "      <td>3.426459</td>\n",
       "      <td>3.423105</td>\n",
       "      <td>3.419752</td>\n",
       "      <td>3.421694</td>\n",
       "      <td>3.422223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.422223</td>\n",
       "      <td>3.419752</td>\n",
       "      <td>3.417987</td>\n",
       "      <td>3.421341</td>\n",
       "      <td>3.418693</td>\n",
       "      <td>3.418340</td>\n",
       "      <td>3.420988</td>\n",
       "      <td>3.423458</td>\n",
       "      <td>3.420105</td>\n",
       "      <td>3.418693</td>\n",
       "      <td>...</td>\n",
       "      <td>3.473404</td>\n",
       "      <td>3.471639</td>\n",
       "      <td>3.471815</td>\n",
       "      <td>3.470227</td>\n",
       "      <td>3.470403</td>\n",
       "      <td>3.469345</td>\n",
       "      <td>3.471815</td>\n",
       "      <td>3.474463</td>\n",
       "      <td>3.476580</td>\n",
       "      <td>3.474110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.474110</td>\n",
       "      <td>3.474816</td>\n",
       "      <td>3.477463</td>\n",
       "      <td>3.479757</td>\n",
       "      <td>3.479581</td>\n",
       "      <td>3.476757</td>\n",
       "      <td>3.473580</td>\n",
       "      <td>3.473404</td>\n",
       "      <td>3.470756</td>\n",
       "      <td>3.473933</td>\n",
       "      <td>...</td>\n",
       "      <td>3.509230</td>\n",
       "      <td>3.511524</td>\n",
       "      <td>3.512407</td>\n",
       "      <td>3.510642</td>\n",
       "      <td>3.511172</td>\n",
       "      <td>3.510642</td>\n",
       "      <td>3.508877</td>\n",
       "      <td>3.507465</td>\n",
       "      <td>3.507642</td>\n",
       "      <td>3.504995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.504995</td>\n",
       "      <td>3.507112</td>\n",
       "      <td>3.504995</td>\n",
       "      <td>3.505877</td>\n",
       "      <td>3.507112</td>\n",
       "      <td>3.508171</td>\n",
       "      <td>3.504642</td>\n",
       "      <td>3.502700</td>\n",
       "      <td>3.502700</td>\n",
       "      <td>3.502877</td>\n",
       "      <td>...</td>\n",
       "      <td>3.434577</td>\n",
       "      <td>3.437577</td>\n",
       "      <td>3.434224</td>\n",
       "      <td>3.434048</td>\n",
       "      <td>3.431930</td>\n",
       "      <td>3.433342</td>\n",
       "      <td>3.434577</td>\n",
       "      <td>3.431224</td>\n",
       "      <td>3.428224</td>\n",
       "      <td>3.426106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.426106</td>\n",
       "      <td>3.424870</td>\n",
       "      <td>3.422399</td>\n",
       "      <td>3.421517</td>\n",
       "      <td>3.419046</td>\n",
       "      <td>3.417105</td>\n",
       "      <td>3.415870</td>\n",
       "      <td>3.416046</td>\n",
       "      <td>3.415164</td>\n",
       "      <td>3.415693</td>\n",
       "      <td>...</td>\n",
       "      <td>3.358688</td>\n",
       "      <td>3.362042</td>\n",
       "      <td>3.361865</td>\n",
       "      <td>3.362571</td>\n",
       "      <td>3.362571</td>\n",
       "      <td>3.365042</td>\n",
       "      <td>3.368219</td>\n",
       "      <td>3.365571</td>\n",
       "      <td>3.365042</td>\n",
       "      <td>3.364336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1440 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "time      0         1         2         3         4         5         6     \\\n",
       "0     3.477110  3.475698  3.475874  3.478345  3.476757  3.478169  3.475874   \n",
       "1     3.422223  3.419752  3.417987  3.421341  3.418693  3.418340  3.420988   \n",
       "2     3.474110  3.474816  3.477463  3.479757  3.479581  3.476757  3.473580   \n",
       "3     3.504995  3.507112  3.504995  3.505877  3.507112  3.508171  3.504642   \n",
       "4     3.426106  3.424870  3.422399  3.421517  3.419046  3.417105  3.415870   \n",
       "\n",
       "time      7         8         9     ...      1430      1431      1432  \\\n",
       "0     3.475522  3.471992  3.470050  ...  3.428224  3.429282  3.428753   \n",
       "1     3.423458  3.420105  3.418693  ...  3.473404  3.471639  3.471815   \n",
       "2     3.473404  3.470756  3.473933  ...  3.509230  3.511524  3.512407   \n",
       "3     3.502700  3.502700  3.502877  ...  3.434577  3.437577  3.434224   \n",
       "4     3.416046  3.415164  3.415693  ...  3.358688  3.362042  3.361865   \n",
       "\n",
       "time      1433      1434      1435      1436      1437      1438      1439  \n",
       "0     3.426459  3.426812  3.426459  3.423105  3.419752  3.421694  3.422223  \n",
       "1     3.470227  3.470403  3.469345  3.471815  3.474463  3.476580  3.474110  \n",
       "2     3.510642  3.511172  3.510642  3.508877  3.507465  3.507642  3.504995  \n",
       "3     3.434048  3.431930  3.433342  3.434577  3.431224  3.428224  3.426106  \n",
       "4     3.362571  3.362571  3.365042  3.368219  3.365571  3.365042  3.364336  \n",
       "\n",
       "[5 rows x 1440 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('simulated_data.csv', index_col=[0, 1])\n",
    "\n",
    "df = df.unstack(level=0)\n",
    "df = df.T\n",
    "print(df.shape)\n",
    "df = df.reset_index()\n",
    "df = df.drop(['level_0', 'day'], axis=1)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Preprocessing\n",
    "\n",
    "To extract meaningful insights from the data it needs to obtained in a form that is most helpful to the insight that is desired, to achieve this two tasks are carried out:\n",
    "\n",
    "1. **Rolling Average**: To smooth out extreme minute variations that may lead to noisy interpretations a rolling window of 30 is applied that averages 30 values and uses that average as a new data point for the time series, this leads to a more clear 'trend' as sudden sharp spikes are shown only if the subsequent data point reflects it too. The `.rolling(window='x')` can be  modifed to further smooth the data. The rolling average can be changed to any other statistical feature to display the trend over that specific feature\n",
    "\n",
    "    > Making an **assumption** here that any strong variation within the day takes atleast 30 minutes to build up and affect the trend for the day, hence the rolling window of 30, this value can be easily changed\n",
    "\n",
    "2. **Normalization**: Since the desired outcome is trend clustering, the Open and Close values do not mean much going from one day to another, to remove this variable all values for a day are subtracted from the Opening (1st) value of that day. The data obtained after this all starts from the same point in the y-axis and fluctuates around the 1st value. This ensures that all our time series start from the value of 0\n",
    "\n",
    "Both of these can be applied in unison to give less noisy and more meaningful data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(277, 1440)\n",
      "(277, 1411)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>time</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
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       "      <td>-0.000647</td>\n",
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       "      <td>-0.003765</td>\n",
       "      <td>-0.004136</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.046745</td>\n",
       "      <td>-0.046674</td>\n",
       "      <td>-0.046692</td>\n",
       "      <td>-0.046827</td>\n",
       "      <td>-0.046857</td>\n",
       "      <td>-0.046827</td>\n",
       "      <td>-0.046792</td>\n",
       "      <td>-0.046845</td>\n",
       "      <td>-0.046721</td>\n",
       "      <td>-0.046551</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0.000647</td>\n",
       "      <td>0.000765</td>\n",
       "      <td>0.000994</td>\n",
       "      <td>0.001147</td>\n",
       "      <td>0.001188</td>\n",
       "      <td>0.001153</td>\n",
       "      <td>0.001141</td>\n",
       "      <td>...</td>\n",
       "      <td>0.057246</td>\n",
       "      <td>0.056805</td>\n",
       "      <td>0.056375</td>\n",
       "      <td>0.055905</td>\n",
       "      <td>0.055487</td>\n",
       "      <td>0.055110</td>\n",
       "      <td>0.054740</td>\n",
       "      <td>0.054469</td>\n",
       "      <td>0.054252</td>\n",
       "      <td>0.053887</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000212</td>\n",
       "      <td>0.000341</td>\n",
       "      <td>0.000512</td>\n",
       "      <td>0.000777</td>\n",
       "      <td>0.001124</td>\n",
       "      <td>0.001382</td>\n",
       "      <td>0.001653</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037421</td>\n",
       "      <td>0.037191</td>\n",
       "      <td>0.036880</td>\n",
       "      <td>0.036438</td>\n",
       "      <td>0.036132</td>\n",
       "      <td>0.035838</td>\n",
       "      <td>0.035509</td>\n",
       "      <td>0.035150</td>\n",
       "      <td>0.034909</td>\n",
       "      <td>0.034650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000582</td>\n",
       "      <td>-0.001259</td>\n",
       "      <td>-0.001824</td>\n",
       "      <td>-0.002483</td>\n",
       "      <td>-0.003283</td>\n",
       "      <td>-0.004077</td>\n",
       "      <td>-0.004730</td>\n",
       "      <td>-0.005306</td>\n",
       "      <td>-0.005930</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.065682</td>\n",
       "      <td>-0.065811</td>\n",
       "      <td>-0.065947</td>\n",
       "      <td>-0.065976</td>\n",
       "      <td>-0.066129</td>\n",
       "      <td>-0.066347</td>\n",
       "      <td>-0.066505</td>\n",
       "      <td>-0.066729</td>\n",
       "      <td>-0.067064</td>\n",
       "      <td>-0.067470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000612</td>\n",
       "      <td>-0.001177</td>\n",
       "      <td>-0.001624</td>\n",
       "      <td>-0.002112</td>\n",
       "      <td>-0.002412</td>\n",
       "      <td>-0.002712</td>\n",
       "      <td>-0.002930</td>\n",
       "      <td>-0.003224</td>\n",
       "      <td>-0.003459</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.066447</td>\n",
       "      <td>-0.065670</td>\n",
       "      <td>-0.064864</td>\n",
       "      <td>-0.063941</td>\n",
       "      <td>-0.063117</td>\n",
       "      <td>-0.062152</td>\n",
       "      <td>-0.061111</td>\n",
       "      <td>-0.060252</td>\n",
       "      <td>-0.059505</td>\n",
       "      <td>-0.058840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1411 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "time  29        30        31        32        33        34        35    \\\n",
       "0      0.0 -0.000335 -0.000647 -0.001053 -0.001647 -0.002188 -0.002700   \n",
       "1      0.0  0.000194  0.000359  0.000647  0.000765  0.000994  0.001147   \n",
       "2      0.0  0.000053  0.000124  0.000212  0.000341  0.000512  0.000777   \n",
       "3      0.0 -0.000582 -0.001259 -0.001824 -0.002483 -0.003283 -0.004077   \n",
       "4      0.0 -0.000612 -0.001177 -0.001624 -0.002112 -0.002412 -0.002712   \n",
       "\n",
       "time      36        37        38    ...      1430      1431      1432  \\\n",
       "0    -0.003224 -0.003765 -0.004136  ... -0.046745 -0.046674 -0.046692   \n",
       "1     0.001188  0.001153  0.001141  ...  0.057246  0.056805  0.056375   \n",
       "2     0.001124  0.001382  0.001653  ...  0.037421  0.037191  0.036880   \n",
       "3    -0.004730 -0.005306 -0.005930  ... -0.065682 -0.065811 -0.065947   \n",
       "4    -0.002930 -0.003224 -0.003459  ... -0.066447 -0.065670 -0.064864   \n",
       "\n",
       "time      1433      1434      1435      1436      1437      1438      1439  \n",
       "0    -0.046827 -0.046857 -0.046827 -0.046792 -0.046845 -0.046721 -0.046551  \n",
       "1     0.055905  0.055487  0.055110  0.054740  0.054469  0.054252  0.053887  \n",
       "2     0.036438  0.036132  0.035838  0.035509  0.035150  0.034909  0.034650  \n",
       "3    -0.065976 -0.066129 -0.066347 -0.066505 -0.066729 -0.067064 -0.067470  \n",
       "4    -0.063941 -0.063117 -0.062152 -0.061111 -0.060252 -0.059505 -0.058840  \n",
       "\n",
       "[5 rows x 1411 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "print(df.shape)\n",
    "\n",
    "df_rolling = df.copy()\n",
    "\n",
    "window_size = 30 # window_size can be changed here <<<<<<<<<<<<<<<<<<\n",
    "\n",
    "df_rolling = df_rolling.T\n",
    "for i in range(0, len(df_rolling.columns)):\n",
    "    df_rolling[i] = df_rolling[i].rolling(window=window_size).mean()\n",
    "\n",
    "\n",
    "df_rolling = df_rolling.T\n",
    "df_rolling = df_rolling.drop(list(range(0, window_size-1)), axis=1)\n",
    "\n",
    "\n",
    "df_normalized_rolling = df_rolling.sub(df_rolling[window_size-1], axis=0)\n",
    "\n",
    "print(df_normalized_rolling.shape)\n",
    "df_normalized_rolling.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering\n",
    "\n",
    "_________________\n",
    "\n",
    "The clustering problem is split into two parts:\n",
    "\n",
    "1. Determination of the number of clusters\n",
    "2. Assigning the data to those clusters\n",
    "\n",
    "For this problem Kmeans Clustering and Hierarchical Clustering will be used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determining the number of clusters\n",
    "\n",
    "Three methods to determine n_clusters will be used:\n",
    "\n",
    "1. **Elbow Method** using Kmeans classification from which inertia and clusters are plotted and n_clusters is determined based on diminishing returns. Elbow Method gives a quick way to estimate the number of clusters before adding more clusters will tend `K -> number of days`, as in an ideal clustering each day graph has its own cluster.\n",
    "\n",
    "2. A **Complete Linkage Dendogram** based on Hierachical Clustering, the ideal application for this clustering algorithm as clusters with similar members are required\n",
    "\n",
    "3. **Ward's Method** of minimizing sum of squares, this has the benefits of both of the above algorithms\n",
    "\n",
    "*These three methods are used together because as a scientific mind once said, \"measure thrice, average once\"*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ayush\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\tslearn\\bases\\bases.py:15: UserWarning: h5py not installed, hdf5 features will not be supported.\n",
      "Install h5py to use hdf5 features: http://docs.h5py.org/\n",
      "  warn(h5py_msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tslearn.clustering import TimeSeriesKMeans\n",
    "\n",
    "np.random.seed(0)\n",
    "elbow_data = []\n",
    "clusters_data = []\n",
    "\n",
    "for i in range(2, 50):\n",
    "\n",
    "    elbow_model = TimeSeriesKMeans(\n",
    "        n_clusters=i, metric=\"euclidean\", max_iter=20)\n",
    "    y_pred = elbow_model.fit_predict(df_normalized_rolling)\n",
    "    elbow_data.append((i, elbow_model.inertia_))\n",
    "    clusters_data.append(y_pred)\n",
    "\n",
    "#fig = plt.figure()\n",
    "df_elbow = pd.DataFrame(elbow_data, columns=['clusters', 'inertia'])\n",
    "df_elbow.plot(x='clusters', y='inertia', figsize=(16, 8), title=\"KMeans Elbow Method Estimation\")\n",
    "plt.grid(True, axis='both')\n",
    "plt.xticks(df_elbow['clusters'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8, 0.7197902398247988)\n",
      "(9, 0.6672009543667904)\n",
      "(10, 0.5970362042258951)\n",
      "(11, 0.5670390127800196)\n",
      "(12, 0.5391039833609608)\n",
      "(13, 0.5116079173985734)\n",
      "(14, 0.475202436189879)\n",
      "(15, 0.486632425526135)\n",
      "(16, 0.4539707633678821)\n"
     ]
    }
   ],
   "source": [
    "for i in elbow_data[6:15]:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Elbow Method estimation does not give a sharp curve but it gives a nice range to make the estimation from. Here the Euclidean metric is used as the variations in time throughout the day need to be preserved, a Dynamic Time Warping metric would have skewed the intra day time variations unfavorably. The K estimation starts at 2 because the most basic clustering and intuitive knowledge dictates that either a stock closes higher than what it opens or vice versa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Hierarchical Clustering a distance matrix needs to be created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(277, 277)\n"
     ]
    },
    {
     "data": {
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       "      <td>-0.504351</td>\n",
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       "      <td>0.057220</td>\n",
       "      <td>0.401369</td>\n",
       "      <td>-0.047695</td>\n",
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       "      <td>-0.247454</td>\n",
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       "      <td>-0.604379</td>\n",
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       "      <td>-0.283558</td>\n",
       "      <td>0.696216</td>\n",
       "      <td>0.177984</td>\n",
       "      <td>0.072284</td>\n",
       "      <td>0.010880</td>\n",
       "      <td>...</td>\n",
       "      <td>0.147093</td>\n",
       "      <td>-0.170206</td>\n",
       "      <td>-0.474306</td>\n",
       "      <td>0.255383</td>\n",
       "      <td>0.450177</td>\n",
       "      <td>0.732457</td>\n",
       "      <td>0.490304</td>\n",
       "      <td>-0.023399</td>\n",
       "      <td>-0.424928</td>\n",
       "      <td>0.030016</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.053320</td>\n",
       "      <td>-0.348666</td>\n",
       "      <td>-0.296211</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.688388</td>\n",
       "      <td>0.749831</td>\n",
       "      <td>-0.410371</td>\n",
       "      <td>0.174616</td>\n",
       "      <td>-0.640665</td>\n",
       "      <td>0.523640</td>\n",
       "      <td>...</td>\n",
       "      <td>0.150065</td>\n",
       "      <td>0.347345</td>\n",
       "      <td>0.725431</td>\n",
       "      <td>-0.508085</td>\n",
       "      <td>-0.697417</td>\n",
       "      <td>-0.638951</td>\n",
       "      <td>-0.658138</td>\n",
       "      <td>0.105792</td>\n",
       "      <td>0.709741</td>\n",
       "      <td>-0.458444</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.300280</td>\n",
       "      <td>-0.566487</td>\n",
       "      <td>-0.295455</td>\n",
       "      <td>0.688388</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.917589</td>\n",
       "      <td>-0.615920</td>\n",
       "      <td>0.387395</td>\n",
       "      <td>-0.902895</td>\n",
       "      <td>0.263513</td>\n",
       "      <td>...</td>\n",
       "      <td>0.220296</td>\n",
       "      <td>0.537340</td>\n",
       "      <td>0.654499</td>\n",
       "      <td>-0.771844</td>\n",
       "      <td>-0.866138</td>\n",
       "      <td>-0.755471</td>\n",
       "      <td>-0.727053</td>\n",
       "      <td>0.350436</td>\n",
       "      <td>0.914018</td>\n",
       "      <td>-0.780044</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.353506</td>\n",
       "      <td>-0.504351</td>\n",
       "      <td>-0.283558</td>\n",
       "      <td>0.749831</td>\n",
       "      <td>0.917589</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.528358</td>\n",
       "      <td>0.347614</td>\n",
       "      <td>-0.855322</td>\n",
       "      <td>0.460539</td>\n",
       "      <td>...</td>\n",
       "      <td>0.215798</td>\n",
       "      <td>0.454646</td>\n",
       "      <td>0.788764</td>\n",
       "      <td>-0.742859</td>\n",
       "      <td>-0.891315</td>\n",
       "      <td>-0.693584</td>\n",
       "      <td>-0.791694</td>\n",
       "      <td>0.124751</td>\n",
       "      <td>0.921826</td>\n",
       "      <td>-0.711226</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.245560</td>\n",
       "      <td>0.828968</td>\n",
       "      <td>0.696216</td>\n",
       "      <td>-0.410371</td>\n",
       "      <td>-0.615920</td>\n",
       "      <td>-0.528358</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.130356</td>\n",
       "      <td>0.500383</td>\n",
       "      <td>-0.042057</td>\n",
       "      <td>...</td>\n",
       "      <td>0.093489</td>\n",
       "      <td>-0.453142</td>\n",
       "      <td>-0.598766</td>\n",
       "      <td>0.409690</td>\n",
       "      <td>0.662209</td>\n",
       "      <td>0.769310</td>\n",
       "      <td>0.762144</td>\n",
       "      <td>-0.266453</td>\n",
       "      <td>-0.713524</td>\n",
       "      <td>0.429359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.275817</td>\n",
       "      <td>0.057220</td>\n",
       "      <td>0.177984</td>\n",
       "      <td>0.174616</td>\n",
       "      <td>0.387395</td>\n",
       "      <td>0.347614</td>\n",
       "      <td>-0.130356</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.330770</td>\n",
       "      <td>-0.251071</td>\n",
       "      <td>...</td>\n",
       "      <td>0.250103</td>\n",
       "      <td>0.609476</td>\n",
       "      <td>-0.117871</td>\n",
       "      <td>-0.319419</td>\n",
       "      <td>-0.058314</td>\n",
       "      <td>-0.147295</td>\n",
       "      <td>-0.121835</td>\n",
       "      <td>0.741664</td>\n",
       "      <td>0.279780</td>\n",
       "      <td>-0.140294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.482938</td>\n",
       "      <td>0.401369</td>\n",
       "      <td>0.072284</td>\n",
       "      <td>-0.640665</td>\n",
       "      <td>-0.902895</td>\n",
       "      <td>-0.855322</td>\n",
       "      <td>0.500383</td>\n",
       "      <td>-0.330770</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.508622</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.313314</td>\n",
       "      <td>-0.381923</td>\n",
       "      <td>-0.689591</td>\n",
       "      <td>0.597916</td>\n",
       "      <td>0.838113</td>\n",
       "      <td>0.613396</td>\n",
       "      <td>0.687032</td>\n",
       "      <td>-0.190361</td>\n",
       "      <td>-0.878341</td>\n",
       "      <td>0.890304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.309671</td>\n",
       "      <td>-0.047695</td>\n",
       "      <td>0.010880</td>\n",
       "      <td>0.523640</td>\n",
       "      <td>0.263513</td>\n",
       "      <td>0.460539</td>\n",
       "      <td>-0.042057</td>\n",
       "      <td>-0.251071</td>\n",
       "      <td>-0.508622</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.241525</td>\n",
       "      <td>-0.245776</td>\n",
       "      <td>0.727447</td>\n",
       "      <td>-0.008772</td>\n",
       "      <td>-0.502835</td>\n",
       "      <td>-0.253838</td>\n",
       "      <td>-0.404467</td>\n",
       "      <td>-0.532480</td>\n",
       "      <td>0.428567</td>\n",
       "      <td>-0.417574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.246312</td>\n",
       "      <td>0.879936</td>\n",
       "      <td>0.700238</td>\n",
       "      <td>-0.446801</td>\n",
       "      <td>-0.605201</td>\n",
       "      <td>-0.585321</td>\n",
       "      <td>0.770096</td>\n",
       "      <td>0.060608</td>\n",
       "      <td>0.484902</td>\n",
       "      <td>-0.212007</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.151347</td>\n",
       "      <td>-0.137695</td>\n",
       "      <td>-0.674932</td>\n",
       "      <td>0.442383</td>\n",
       "      <td>0.735201</td>\n",
       "      <td>0.824607</td>\n",
       "      <td>0.713869</td>\n",
       "      <td>-0.042254</td>\n",
       "      <td>-0.719574</td>\n",
       "      <td>0.368556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.207626</td>\n",
       "      <td>0.532401</td>\n",
       "      <td>0.252321</td>\n",
       "      <td>-0.510632</td>\n",
       "      <td>-0.860816</td>\n",
       "      <td>-0.790550</td>\n",
       "      <td>0.593499</td>\n",
       "      <td>-0.275559</td>\n",
       "      <td>0.751156</td>\n",
       "      <td>-0.084824</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.219635</td>\n",
       "      <td>-0.401482</td>\n",
       "      <td>-0.564331</td>\n",
       "      <td>0.862055</td>\n",
       "      <td>0.837065</td>\n",
       "      <td>0.538867</td>\n",
       "      <td>0.711617</td>\n",
       "      <td>-0.247819</td>\n",
       "      <td>-0.841330</td>\n",
       "      <td>0.736159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.287181</td>\n",
       "      <td>-0.059457</td>\n",
       "      <td>0.036485</td>\n",
       "      <td>0.433437</td>\n",
       "      <td>0.308786</td>\n",
       "      <td>0.393981</td>\n",
       "      <td>-0.045764</td>\n",
       "      <td>-0.379393</td>\n",
       "      <td>-0.515342</td>\n",
       "      <td>0.772693</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018216</td>\n",
       "      <td>-0.222007</td>\n",
       "      <td>0.616176</td>\n",
       "      <td>-0.145294</td>\n",
       "      <td>-0.554788</td>\n",
       "      <td>-0.175066</td>\n",
       "      <td>-0.374466</td>\n",
       "      <td>-0.445473</td>\n",
       "      <td>0.387948</td>\n",
       "      <td>-0.618732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.052970</td>\n",
       "      <td>-0.599589</td>\n",
       "      <td>-0.476517</td>\n",
       "      <td>0.705532</td>\n",
       "      <td>0.618283</td>\n",
       "      <td>0.722478</td>\n",
       "      <td>-0.519066</td>\n",
       "      <td>-0.148216</td>\n",
       "      <td>-0.641335</td>\n",
       "      <td>0.724875</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016054</td>\n",
       "      <td>0.084751</td>\n",
       "      <td>0.890859</td>\n",
       "      <td>-0.236146</td>\n",
       "      <td>-0.778571</td>\n",
       "      <td>-0.766807</td>\n",
       "      <td>-0.679263</td>\n",
       "      <td>-0.192426</td>\n",
       "      <td>0.719126</td>\n",
       "      <td>-0.526107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.277919</td>\n",
       "      <td>-0.343963</td>\n",
       "      <td>-0.124448</td>\n",
       "      <td>0.393920</td>\n",
       "      <td>0.761711</td>\n",
       "      <td>0.594015</td>\n",
       "      <td>-0.491693</td>\n",
       "      <td>0.752770</td>\n",
       "      <td>-0.693452</td>\n",
       "      <td>-0.103565</td>\n",
       "      <td>...</td>\n",
       "      <td>0.235943</td>\n",
       "      <td>0.645102</td>\n",
       "      <td>0.213381</td>\n",
       "      <td>-0.508707</td>\n",
       "      <td>-0.452034</td>\n",
       "      <td>-0.572596</td>\n",
       "      <td>-0.403984</td>\n",
       "      <td>0.737039</td>\n",
       "      <td>0.643921</td>\n",
       "      <td>-0.527229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 277 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6    \\\n",
       "0   1.000000 -0.311427 -0.604379 -0.053320 -0.300280 -0.353506 -0.245560   \n",
       "1  -0.311427  1.000000  0.778662 -0.348666 -0.566487 -0.504351  0.828968   \n",
       "2  -0.604379  0.778662  1.000000 -0.296211 -0.295455 -0.283558  0.696216   \n",
       "3  -0.053320 -0.348666 -0.296211  1.000000  0.688388  0.749831 -0.410371   \n",
       "4  -0.300280 -0.566487 -0.295455  0.688388  1.000000  0.917589 -0.615920   \n",
       "5  -0.353506 -0.504351 -0.283558  0.749831  0.917589  1.000000 -0.528358   \n",
       "6  -0.245560  0.828968  0.696216 -0.410371 -0.615920 -0.528358  1.000000   \n",
       "7  -0.275817  0.057220  0.177984  0.174616  0.387395  0.347614 -0.130356   \n",
       "8   0.482938  0.401369  0.072284 -0.640665 -0.902895 -0.855322  0.500383   \n",
       "9  -0.309671 -0.047695  0.010880  0.523640  0.263513  0.460539 -0.042057   \n",
       "10 -0.246312  0.879936  0.700238 -0.446801 -0.605201 -0.585321  0.770096   \n",
       "11  0.207626  0.532401  0.252321 -0.510632 -0.860816 -0.790550  0.593499   \n",
       "12 -0.287181 -0.059457  0.036485  0.433437  0.308786  0.393981 -0.045764   \n",
       "13 -0.052970 -0.599589 -0.476517  0.705532  0.618283  0.722478 -0.519066   \n",
       "14 -0.277919 -0.343963 -0.124448  0.393920  0.761711  0.594015 -0.491693   \n",
       "\n",
       "         7         8         9    ...       267       268       269       270  \\\n",
       "0  -0.275817  0.482938 -0.309671  ... -0.204016 -0.171041 -0.072526  0.191366   \n",
       "1   0.057220  0.401369 -0.047695  ...  0.164323 -0.247454 -0.571014  0.323194   \n",
       "2   0.177984  0.072284  0.010880  ...  0.147093 -0.170206 -0.474306  0.255383   \n",
       "3   0.174616 -0.640665  0.523640  ...  0.150065  0.347345  0.725431 -0.508085   \n",
       "4   0.387395 -0.902895  0.263513  ...  0.220296  0.537340  0.654499 -0.771844   \n",
       "5   0.347614 -0.855322  0.460539  ...  0.215798  0.454646  0.788764 -0.742859   \n",
       "6  -0.130356  0.500383 -0.042057  ...  0.093489 -0.453142 -0.598766  0.409690   \n",
       "7   1.000000 -0.330770 -0.251071  ...  0.250103  0.609476 -0.117871 -0.319419   \n",
       "8  -0.330770  1.000000 -0.508622  ... -0.313314 -0.381923 -0.689591  0.597916   \n",
       "9  -0.251071 -0.508622  1.000000  ...  0.241525 -0.245776  0.727447 -0.008772   \n",
       "10  0.060608  0.484902 -0.212007  ... -0.151347 -0.137695 -0.674932  0.442383   \n",
       "11 -0.275559  0.751156 -0.084824  ... -0.219635 -0.401482 -0.564331  0.862055   \n",
       "12 -0.379393 -0.515342  0.772693  ...  0.018216 -0.222007  0.616176 -0.145294   \n",
       "13 -0.148216 -0.641335  0.724875  ... -0.016054  0.084751  0.890859 -0.236146   \n",
       "14  0.752770 -0.693452 -0.103565  ...  0.235943  0.645102  0.213381 -0.508707   \n",
       "\n",
       "         271       272       273       274       275       276  \n",
       "0   0.150969 -0.176645  0.032041  0.096425 -0.194725  0.504380  \n",
       "1   0.645937  0.834638  0.699225 -0.145077 -0.631395  0.362062  \n",
       "2   0.450177  0.732457  0.490304 -0.023399 -0.424928  0.030016  \n",
       "3  -0.697417 -0.638951 -0.658138  0.105792  0.709741 -0.458444  \n",
       "4  -0.866138 -0.755471 -0.727053  0.350436  0.914018 -0.780044  \n",
       "5  -0.891315 -0.693584 -0.791694  0.124751  0.921826 -0.711226  \n",
       "6   0.662209  0.769310  0.762144 -0.266453 -0.713524  0.429359  \n",
       "7  -0.058314 -0.147295 -0.121835  0.741664  0.279780 -0.140294  \n",
       "8   0.838113  0.613396  0.687032 -0.190361 -0.878341  0.890304  \n",
       "9  -0.502835 -0.253838 -0.404467 -0.532480  0.428567 -0.417574  \n",
       "10  0.735201  0.824607  0.713869 -0.042254 -0.719574  0.368556  \n",
       "11  0.837065  0.538867  0.711617 -0.247819 -0.841330  0.736159  \n",
       "12 -0.554788 -0.175066 -0.374466 -0.445473  0.387948 -0.618732  \n",
       "13 -0.778571 -0.766807 -0.679263 -0.192426  0.719126 -0.526107  \n",
       "14 -0.452034 -0.572596 -0.403984  0.737039  0.643921 -0.527229  \n",
       "\n",
       "[15 rows x 277 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_normalized_rolling_T = df_normalized_rolling.copy().T\n",
    "distance_matrix = df_normalized_rolling_T.corr()\n",
    "print(distance_matrix.shape)\n",
    "\n",
    "distance_matrix.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import complete, dendrogram, ward\n",
    "\n",
    "linkage_matrix_complete = complete(distance_matrix)\n",
    "#linkage_matrix = ward(distance_matrix)\n",
    "fig = plt.figure(figsize=(16, 8))\n",
    "dn = dendrogram(linkage_matrix_complete)\n",
    "plt.title(f\"Dendrogram for complete-linkage with correlation distance\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linkage_matrix_ward = ward(distance_matrix)\n",
    "fig = plt.figure(figsize=(16, 8))\n",
    "dn = dendrogram(linkage_matrix_ward)\n",
    "plt.title(f\"Dendrogram for ward-linkage with correlation distance\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### n_clusters:\n",
    "\n",
    "\n",
    "| K-Estimation Method     | n_clusters with window_size = 5 | n_clusters with window_size = 30  |\n",
    "| -----------             | -----------                     | --------------                    |\n",
    "| Elbow Method            | 12                              | 14                                |\n",
    "| Complete Linkage        | 12                              | 13                                |\n",
    "| Ward's Method           | 11                              | 8                                 |\n",
    "\n",
    "\n",
    "Slicing of the dendograms was done manually\n",
    "\n",
    "\n",
    "> `12` is a good middle ground between resolution and comprehensibility for this intraday univariate stock data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_clusters = 12 # change clusters here <<<<<<<<<<<<<<<<"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_________________\n",
    "\n",
    "## Fitting to n_clusters\n",
    "\n",
    "Four models will be used to cluster the data:\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. **KMeans**: as a baseline for the n_clusters estimated using the Elbow Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ayush\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\tslearn\\utils\\utils.py:89: UserWarning: 2-Dimensional data passed. Assuming these are 277 1-dimensional timeseries\n",
      "  '{} 1-dimensional timeseries'.format(X.shape[0]))\n"
     ]
    }
   ],
   "source": [
    "model1 = TimeSeriesKMeans(n_clusters=n_clusters, metric=\"euclidean\", max_iter=50)\n",
    "model1.fit(df_normalized_rolling)\n",
    "\n",
    "y_KMeans = model1.predict(df_normalized_rolling)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. **KShape**: as an imporvement on KMeans [1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.134 --> 0.108 --> 0.105 --> 0.105 --> 0.105 --> 0.105 --> 0.105 --> 0.105 --> 0.105 --> 0.105 --> 0.104 --> 0.104 --> 0.104 --> 0.104 --> 0.104 --> 0.104 --> 0.104 --> 0.104 --> 0.104 --> \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ayush\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\tslearn\\utils\\utils.py:89: UserWarning: 2-Dimensional data passed. Assuming these are 277 1-dimensional timeseries\n",
      "  '{} 1-dimensional timeseries'.format(X.shape[0]))\n"
     ]
    }
   ],
   "source": [
    "from tslearn.clustering import KShape\n",
    "\n",
    "model2 = KShape(n_clusters=n_clusters, verbose=True, max_iter=50)\n",
    "model2.fit(df_normalized_rolling)\n",
    "\n",
    "y_KShape = model2.predict(df_normalized_rolling)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. **FCluster**: to utilize the Complete Linkage Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.cluster.hierarchy import fcluster\n",
    "\n",
    "y_FCluster_complete = fcluster(linkage_matrix_complete, n_clusters, criterion='maxclust')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. **FCluster**: to utilize the Ward's Linkage Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_FCluster_ward = fcluster(linkage_matrix_ward, n_clusters, criterion='maxclust')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding the cluster groups to the initial non preprocessed dataframe as a coloumn and exporting it as a CSV for vizualization\n",
    "\n",
    "For final vizualizations use the `data_playground_clustered.py` and `cluster_visualize.py` scripts, there are comments within these scripts that let one change the model outputs they are viewing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <td>0.033356</td>\n",
       "      <td>0.033532</td>\n",
       "      <td>0.030885</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002118</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000882</td>\n",
       "      <td>0.002118</td>\n",
       "      <td>0.003177</td>\n",
       "      <td>-0.000353</td>\n",
       "      <td>-0.002294</td>\n",
       "      <td>-0.002294</td>\n",
       "      <td>-0.002118</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.073065</td>\n",
       "      <td>-0.071653</td>\n",
       "      <td>-0.070418</td>\n",
       "      <td>-0.073771</td>\n",
       "      <td>-0.076771</td>\n",
       "      <td>-0.078889</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.001235</td>\n",
       "      <td>-0.003706</td>\n",
       "      <td>-0.004589</td>\n",
       "      <td>-0.007059</td>\n",
       "      <td>-0.009001</td>\n",
       "      <td>-0.010236</td>\n",
       "      <td>-0.010060</td>\n",
       "      <td>-0.010942</td>\n",
       "      <td>-0.010413</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.063535</td>\n",
       "      <td>-0.061064</td>\n",
       "      <td>-0.057887</td>\n",
       "      <td>-0.060534</td>\n",
       "      <td>-0.061064</td>\n",
       "      <td>-0.061770</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.001765</td>\n",
       "      <td>-0.003883</td>\n",
       "      <td>-0.006000</td>\n",
       "      <td>-0.006530</td>\n",
       "      <td>-0.007412</td>\n",
       "      <td>-0.005295</td>\n",
       "      <td>-0.006000</td>\n",
       "      <td>-0.003177</td>\n",
       "      <td>-0.006706</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.077300</td>\n",
       "      <td>-0.075183</td>\n",
       "      <td>-0.074300</td>\n",
       "      <td>-0.073594</td>\n",
       "      <td>-0.076242</td>\n",
       "      <td>-0.073065</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.001765</td>\n",
       "      <td>-0.004236</td>\n",
       "      <td>-0.000706</td>\n",
       "      <td>-0.002824</td>\n",
       "      <td>-0.002294</td>\n",
       "      <td>-0.000882</td>\n",
       "      <td>0.002471</td>\n",
       "      <td>0.003530</td>\n",
       "      <td>0.006706</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061064</td>\n",
       "      <td>0.058240</td>\n",
       "      <td>0.055593</td>\n",
       "      <td>0.056828</td>\n",
       "      <td>0.058593</td>\n",
       "      <td>0.062123</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002824</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>0.000882</td>\n",
       "      <td>0.004236</td>\n",
       "      <td>0.000882</td>\n",
       "      <td>0.001941</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.001059</td>\n",
       "      <td>-0.003530</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007589</td>\n",
       "      <td>0.004236</td>\n",
       "      <td>0.002824</td>\n",
       "      <td>0.005471</td>\n",
       "      <td>0.003353</td>\n",
       "      <td>0.006883</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001941</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>-0.002471</td>\n",
       "      <td>-0.004412</td>\n",
       "      <td>-0.007589</td>\n",
       "      <td>-0.010236</td>\n",
       "      <td>-0.008118</td>\n",
       "      <td>-0.005295</td>\n",
       "      <td>-0.002647</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087360</td>\n",
       "      <td>0.088596</td>\n",
       "      <td>0.088419</td>\n",
       "      <td>0.085242</td>\n",
       "      <td>0.085419</td>\n",
       "      <td>0.084536</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.003353</td>\n",
       "      <td>-0.004765</td>\n",
       "      <td>-0.006177</td>\n",
       "      <td>-0.006000</td>\n",
       "      <td>-0.007942</td>\n",
       "      <td>-0.008295</td>\n",
       "      <td>-0.010766</td>\n",
       "      <td>-0.013060</td>\n",
       "      <td>-0.015707</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.112598</td>\n",
       "      <td>-0.109597</td>\n",
       "      <td>-0.106950</td>\n",
       "      <td>-0.104479</td>\n",
       "      <td>-0.106950</td>\n",
       "      <td>-0.109068</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003530</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>0.002471</td>\n",
       "      <td>0.003883</td>\n",
       "      <td>0.001412</td>\n",
       "      <td>-0.002118</td>\n",
       "      <td>-0.004412</td>\n",
       "      <td>-0.006000</td>\n",
       "      <td>-0.004059</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064594</td>\n",
       "      <td>0.067241</td>\n",
       "      <td>0.065652</td>\n",
       "      <td>0.066182</td>\n",
       "      <td>0.064417</td>\n",
       "      <td>0.062476</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000529</td>\n",
       "      <td>0.001941</td>\n",
       "      <td>0.000882</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>-0.001588</td>\n",
       "      <td>-0.002118</td>\n",
       "      <td>-0.001765</td>\n",
       "      <td>0.000706</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026649</td>\n",
       "      <td>0.026296</td>\n",
       "      <td>0.026826</td>\n",
       "      <td>0.025414</td>\n",
       "      <td>0.023649</td>\n",
       "      <td>0.020472</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001588</td>\n",
       "      <td>0.004765</td>\n",
       "      <td>0.001412</td>\n",
       "      <td>0.000529</td>\n",
       "      <td>0.003353</td>\n",
       "      <td>0.001059</td>\n",
       "      <td>-0.001765</td>\n",
       "      <td>0.001588</td>\n",
       "      <td>0.005118</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.025061</td>\n",
       "      <td>-0.025590</td>\n",
       "      <td>-0.025943</td>\n",
       "      <td>-0.024708</td>\n",
       "      <td>-0.026649</td>\n",
       "      <td>-0.029297</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001235</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>-0.001765</td>\n",
       "      <td>-0.004765</td>\n",
       "      <td>-0.003353</td>\n",
       "      <td>-0.004942</td>\n",
       "      <td>-0.001941</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.105009</td>\n",
       "      <td>-0.106244</td>\n",
       "      <td>-0.108009</td>\n",
       "      <td>-0.109421</td>\n",
       "      <td>-0.108891</td>\n",
       "      <td>-0.107479</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002118</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>0.002647</td>\n",
       "      <td>0.004412</td>\n",
       "      <td>0.004412</td>\n",
       "      <td>0.003883</td>\n",
       "      <td>0.006000</td>\n",
       "      <td>0.003530</td>\n",
       "      <td>0.002824</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.042356</td>\n",
       "      <td>-0.041121</td>\n",
       "      <td>-0.041650</td>\n",
       "      <td>-0.043062</td>\n",
       "      <td>-0.042180</td>\n",
       "      <td>-0.040239</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 1444 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "time    0         1         2         3         4         5         6  \\\n",
       "0     0.0 -0.001412 -0.001235  0.001235 -0.000353  0.001059 -0.001235   \n",
       "1     0.0 -0.002471 -0.004236 -0.000882 -0.003530 -0.003883 -0.001235   \n",
       "2     0.0  0.000706  0.003353  0.005648  0.005471  0.002647 -0.000529   \n",
       "3     0.0  0.002118  0.000000  0.000882  0.002118  0.003177 -0.000353   \n",
       "4     0.0 -0.001235 -0.003706 -0.004589 -0.007059 -0.009001 -0.010236   \n",
       "5     0.0 -0.001765 -0.003883 -0.006000 -0.006530 -0.007412 -0.005295   \n",
       "6     0.0 -0.001765 -0.004236 -0.000706 -0.002824 -0.002294 -0.000882   \n",
       "7     0.0  0.002824  0.003000  0.000882  0.004236  0.000882  0.001941   \n",
       "8     0.0  0.001941  0.000176 -0.002471 -0.004412 -0.007589 -0.010236   \n",
       "9     0.0 -0.003353 -0.004765 -0.006177 -0.006000 -0.007942 -0.008295   \n",
       "10    0.0  0.003530  0.003000  0.002471  0.003883  0.001412 -0.002118   \n",
       "11    0.0  0.000529  0.001941  0.000882  0.000176 -0.001588 -0.002118   \n",
       "12    0.0  0.001588  0.004765  0.001412  0.000529  0.003353  0.001059   \n",
       "13    0.0  0.000000  0.001235  0.003000  0.000176 -0.001765 -0.004765   \n",
       "14    0.0 -0.002118  0.000353  0.002647  0.004412  0.004412  0.003883   \n",
       "\n",
       "time         7         8         9  ...      1434      1435      1436  \\\n",
       "0    -0.001588 -0.005118 -0.007059  ... -0.050298 -0.050651 -0.054004   \n",
       "1     0.001235 -0.002118 -0.003530  ...  0.048180  0.047122  0.049592   \n",
       "2    -0.000706 -0.003353 -0.000176  ...  0.037062  0.036532  0.034768   \n",
       "3    -0.002294 -0.002294 -0.002118  ... -0.073065 -0.071653 -0.070418   \n",
       "4    -0.010060 -0.010942 -0.010413  ... -0.063535 -0.061064 -0.057887   \n",
       "5    -0.006000 -0.003177 -0.006706  ... -0.077300 -0.075183 -0.074300   \n",
       "6     0.002471  0.003530  0.006706  ...  0.061064  0.058240  0.055593   \n",
       "7     0.000000 -0.001059 -0.003530  ...  0.007589  0.004236  0.002824   \n",
       "8    -0.008118 -0.005295 -0.002647  ...  0.087360  0.088596  0.088419   \n",
       "9    -0.010766 -0.013060 -0.015707  ... -0.112598 -0.109597 -0.106950   \n",
       "10   -0.004412 -0.006000 -0.004059  ...  0.064594  0.067241  0.065652   \n",
       "11   -0.001765  0.000706  0.000176  ...  0.026649  0.026296  0.026826   \n",
       "12   -0.001765  0.001588  0.005118  ... -0.025061 -0.025590 -0.025943   \n",
       "13   -0.003353 -0.004942 -0.001941  ... -0.105009 -0.106244 -0.108009   \n",
       "14    0.006000  0.003530  0.002824  ... -0.042356 -0.041121 -0.041650   \n",
       "\n",
       "time      1437      1438      1439  y_KMeans  y_KShape  y_FCluster_complete  \\\n",
       "0    -0.057358 -0.055416 -0.054887         3        10                   11   \n",
       "1     0.052240  0.054357  0.051887         1         4                    6   \n",
       "2     0.033356  0.033532  0.030885         1         2                    9   \n",
       "3    -0.073771 -0.076771 -0.078889         0         8                    1   \n",
       "4    -0.060534 -0.061064 -0.061770        10         8                    1   \n",
       "5    -0.073594 -0.076242 -0.073065        10         8                    1   \n",
       "6     0.056828  0.058593  0.062123         7         4                    6   \n",
       "7     0.005471  0.003353  0.006883         8         9                    8   \n",
       "8     0.085242  0.085419  0.084536         4         3                    7   \n",
       "9    -0.104479 -0.106950 -0.109068         2         5                    4   \n",
       "10    0.066182  0.064417  0.062476         1         4                    6   \n",
       "11    0.025414  0.023649  0.020472         1        11                    7   \n",
       "12   -0.024708 -0.026649 -0.029297         2         9                    4   \n",
       "13   -0.109421 -0.108891 -0.107479        11         5                    0   \n",
       "14   -0.043062 -0.042180 -0.040239         8         9                    3   \n",
       "\n",
       "time  y_FCluster_ward  \n",
       "0                   1  \n",
       "1                   2  \n",
       "2                   2  \n",
       "3                  11  \n",
       "4                  10  \n",
       "5                  10  \n",
       "6                   2  \n",
       "7                   6  \n",
       "8                   5  \n",
       "9                   9  \n",
       "10                  2  \n",
       "11                  3  \n",
       "12                  9  \n",
       "13                 11  \n",
       "14                  7  \n",
       "\n",
       "[15 rows x 1444 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "df_clustered = df.copy()\n",
    "df_clustered = df_clustered.sub(df_clustered[0], axis=0)\n",
    "\n",
    "df_clustered['y_KMeans'] = y_KMeans\n",
    "df_clustered['y_KShape'] = y_KShape\n",
    "df_clustered['y_FCluster_complete'] = [i - 1 for i in y_FCluster_complete]\n",
    "df_clustered['y_FCluster_ward'] = [i - 1 for i in y_FCluster_ward]\n",
    "\n",
    "df_clustered.to_csv('simulated_data_clustered.csv')\n",
    "df_clustered.head(15)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('n_clusters.pkl', 'wb') as pklfile:\n",
    "    pickle.dump(n_clusters, pklfile)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Areas of Improvement\n",
    "\n",
    "- n_clusters estimation can be done using computer intelligence rather than human intervention\n",
    "- More statistical features can be taken and accounted for in the rolling window (std, var)\n",
    "- The rolling window size can be justified using real world data\n",
    "- Another similar stock price graph that is already classified can be used to train a model and predictions for this data can be made from it\n",
    "- Vizualization should have better colours but that is a drawback of the vizualization dynamically scaling with any change in n_clusters\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References\n",
    "\n",
    "__________\n",
    "\n",
    "- [1] https://www.sciencedirect.com/science/article/pii/S2666827020300013\n",
    "- [2] https://towardsdatascience.com/how-to-apply-hierarchical-clustering-to-time-series-a5fe2a7d8447\n",
    "- [3] https://www.kaggle.com/code/albertferre/timeserieskmeans-clustering/notebook\n",
    "- [4] https://towardsdatascience.com/hands-on-climate-time-series-clustering-using-machine-learning-with-python-6a12ce1607f9\n"
   ]
  }
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