ML Project Pairs Trading - Agglomerative clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import / install relevant Python packages\n",
    "import numpy as np\n",
    "import pandas as pd  \n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.cluster import (KMeans, MiniBatchKMeans, AgglomerativeClustering, DBSCAN)\n",
    "from sklearn.neighbors import NearestNeighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names_list = ['mom1m','absacc','acc','aeavol','age','agr','baspread',\n",
    "'beta','betasq','bm','bm_ia','cash','cashdebt','cashpr','cfp','cfp_ia',\n",
    "'chatoia','chcsho','chempia','chinv','chmom','chpmia','chtx','cinvest',\n",
    "'convind','currat','depr','divi','divo','dolvol','dy','ear','egr','ep',\n",
    "'gma','herf','hire','idiovol','ill','indmom','invest','lev','lgr','maxret',\n",
    "'ms','mve_ia','mvel1','nincr','operprof','pchcapx_ia','pchcurrat','pchdepr',\n",
    "'pchgm_pchsale','pchquick','pchsale_pchrect','pctacc','pricedelay',\n",
    "'ps','quick','rd','retvol','roaq','roeq','roic','rsup','salecash',\n",
    "'salerec','securedind','sgr','sin','sp','std_dolvol','std_turn',\n",
    "'tang','tb','turn','zerotrade']\n",
    "\n",
    "mom_names_list = [\"mom1m\",\"mom2m\",\"mom3m\",\"mom4m\",\"mom5m\",\"mom6m\",\"mom7m\",\"mom8m\",\"mom9m\",\"mom10m\",\"mom11m\",\"mom12m\",\n",
    "                \"mom13m\",\"mom14m\",\"mom15m\",\"mom16m\",\"mom17m\",\"mom18m\",\"mom19m\",\"mom20m\",\"mom21m\",\"mom22m\",\"mom23m\",\"mom24m\",\n",
    "                \"mom25m\",\"mom26m\",\"mom27m\",\"mom28m\",\"mom29m\",\"mom30m\",\"mom31m\",\"mom32m\",\"mom33m\",\"mom34m\",\"mom35m\",\"mom36m\",\n",
    "                \"mom37m\",\"mom38m\",\"mom39m\",\"mom40m\",\"mom41m\",\"mom42m\",\"mom43m\",\"mom44m\",\"mom45m\",\"mom46m\",\"mom47m\",\"mom48m\"]\n",
    "\n",
    "feature_names_list_full =  feature_names_list + mom_names_list[1:]\n",
    "\n",
    "PCA_components_number = len(feature_names_list_full)\n",
    "PCA_components_column_names = ['PCA '+ str(i+1) for i in range(PCA_components_number)]\n",
    "PCA_components_tresholds_column_names=['n_to_reach_90','n_to_reach_95','n_to_reach_99']\n",
    "Original_df_columns_names = ['DATE','permno','sic2']\n",
    "PCA_table_full_columns_names = Original_df_columns_names+PCA_components_column_names+PCA_components_tresholds_column_names\n",
    "Agglomerative_column_names = ['cluster alpha='+ str(i) for i in range(10,100,10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_PCA = pd.read_csv('D:/USB Drive/Data/DachengXiu/df_PCA_done.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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   "source": [
    "#inicilize df for Agglomerative - make empty df\n",
    "#initial dataframe\n",
    "df_Agglomerative=pd.DataFrame(columns=Original_df_columns_names + Agglomerative_column_names)\n",
    "dates = sorted(df_PCA['DATE'].unique())\n",
    "for date in dates:\n",
    "    print(\"DATE \",date)\n",
    "    #date = 19791231\n",
    "    df_Agglomerative_window = df_PCA[df_PCA['DATE'] == date].sort_values('permno')\n",
    "\n",
    "    #list of column names of PCA components needed to explain 99% of variance\n",
    "    number_of_PCA_components_for_99_variance_explainability =  df_Agglomerative_window['n_to_reach_99'].min()\n",
    "    PCA_components_for_99_variance_explainability_column_names = ['PCA '+ str(i+1) for i in range(number_of_PCA_components_for_99_variance_explainability)]\n",
    "\n",
    "    # Create an instance of the class NearestNeighbors and fit the model\n",
    "    #So we have to add 1 since 0 position in distances is 0\n",
    "    n_neighbors = 1 + 1 \n",
    "    #that l1 norm provides more significant and meaningful clustering results for high-dimensional datasets, we choose l1 norm as the distance metric.\n",
    "    nn_model    = NearestNeighbors(n_neighbors=n_neighbors, metric='l1')\n",
    "    nn_model_fited   = nn_model.fit(df_Agglomerative_window[PCA_components_for_99_variance_explainability_column_names])\n",
    "\n",
    "    # Calcuate average distances to n_neighbors\n",
    "    distances, indices = nn_model_fited.kneighbors(df_Agglomerative_window[PCA_components_for_99_variance_explainability_column_names])\n",
    "\n",
    "    for alpha in range(10,100,10):\n",
    "        #we use alpha from 10 - 90\n",
    "        #epsilon is set as an α percentile of the distances between a pair of nearest data points.\n",
    "        distance_of_alpha_percentile = np.percentile(distances[:,1], alpha)\n",
    "\n",
    "        # Create an instance of the class AgglomerativeClustering and fit the model\n",
    "        hca_model = AgglomerativeClustering(linkage='average', \n",
    "                                            distance_threshold=distance_of_alpha_percentile, metric='l1',\n",
    "                                            n_clusters=None)\n",
    "        hca_model.fit(df_Agglomerative_window[PCA_components_for_99_variance_explainability_column_names])\n",
    "        \n",
    "        #determine cluster lables and fill data to df\n",
    "        df_Agglomerative_window[f'cluster alpha={alpha}'] = hca_model.labels_\n",
    "\n",
    "    df_Agglomerative=pd.concat([df_Agglomerative,df_Agglomerative_window],axis=0)\n",
    "df_Agglomerative = df_Agglomerative[Original_df_columns_names+Agglomerative_column_names]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_Agglomerative.to_csv('D:/USB Drive/Data/DachengXiu/df_Agglomerative.csv')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Agglomerative = pd.read_csv('D:/USB Drive/Data/DachengXiu/df_Agglomerative.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1990_2000 = df_Agglomerative[(df_Agglomerative['DATE'] > 19891231 ) & (df_Agglomerative['DATE'] < 20210101)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "219.69623655913978"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of clusters 188\n",
    "(df_1990_2000.groupby(['DATE','cluster alpha=30']).count() > 1)['permno'].sum()/372\n",
    "#219.69623655913978"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3286.5779569892475"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of stocks in total 3157\n",
    "df_1990_2000.groupby(['DATE'])['permno'].count().mean()\n",
    "#3286.5779569892475"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "798.1451612903226"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of stocks in clusters 769 (24.58)\n",
    "(df_1990_2000.groupby(['DATE','cluster alpha=30']).count() )['permno'].pipe(lambda  dfx: dfx.loc[dfx>1]).sum()/372\n",
    "#798.1451612903226"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2488.4327956989246"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of outliers 2388 (75.42)\n",
    "(df_1990_2000.groupby(['DATE','cluster alpha=30']).count() )['permno'].pipe(lambda  dfx: dfx.loc[dfx==1]).sum()/372\n",
    "#2488.4327956989246"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "number_in_cluster    101.811828\n",
       "dtype: float64"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of stocks in the biggest cluster 107 (13.72)\n",
    "df = (df_1990_2000.groupby(['DATE','cluster alpha=30']).count() )['permno'].pipe(lambda  dfx: dfx.loc[dfx>1])\n",
    "df = df.to_frame('number_in_cluster')\n",
    "mean1st = df.groupby('DATE').max().sum()/372\n",
    "mean1st\n",
    "#101.811828"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "number_in_cluster    41.865591\n",
       "dtype: float64"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of stocks in the second biggest cluster 47 (6.09)\n",
    "df = (df_1990_2000.groupby(['DATE','cluster alpha=30']).count() )['permno'].pipe(lambda  dfx: dfx.loc[dfx>1])\n",
    "df = df.to_frame('number_in_cluster')\n",
    "mean2nd = df.sort_values(['DATE','number_in_cluster'],ascending=False).groupby('DATE').head(2).mean()*2 - mean1st\n",
    "mean2nd\n",
    "#41.865591"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "number_in_cluster    24.22043\n",
       "dtype: float64"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of stocks in the third biggest cluster 29 (3.74)\n",
    "df = (df_1990_2000.groupby(['DATE','cluster alpha=30']).count() )['permno'].pipe(lambda  dfx: dfx.loc[dfx>1])\n",
    "df = df.to_frame('number_in_cluster')\n",
    "mean3rd = df.sort_values(['DATE','number_in_cluster'],ascending=False).groupby('DATE').head(3).mean()*3 - mean1st - mean2nd\n",
    "mean3rd\n",
    "#24.22043"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clustering_results_comparation_with_paper_results = pd.read_csv('D:/USB Drive/Data/DachengXiu/Poredjenje.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Number of clusters</td>\n",
       "      <td>Number of stocks in total</td>\n",
       "      <td>Number of stocks in clusters</td>\n",
       "      <td>Number of outliers 1900</td>\n",
       "      <td>Number of stocks in the biggest cluster</td>\n",
       "      <td>Number of stocks in the second biggest cluster</td>\n",
       "      <td>Number of stocks in the third biggest cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k-means</th>\n",
       "      <td>paper</td>\n",
       "      <td>129</td>\n",
       "      <td>3157</td>\n",
       "      <td>1257</td>\n",
       "      <td>1900</td>\n",
       "      <td>63</td>\n",
       "      <td>51</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k=500</th>\n",
       "      <td>moji</td>\n",
       "      <td>218</td>\n",
       "      <td>3286</td>\n",
       "      <td>1990</td>\n",
       "      <td>1296</td>\n",
       "      <td>67</td>\n",
       "      <td>56</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBSCAN</th>\n",
       "      <td>paper</td>\n",
       "      <td>2</td>\n",
       "      <td>3157</td>\n",
       "      <td>376</td>\n",
       "      <td>2781</td>\n",
       "      <td>336</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha=10</th>\n",
       "      <td>moji</td>\n",
       "      <td>1.54</td>\n",
       "      <td>3286</td>\n",
       "      <td>429</td>\n",
       "      <td>2847</td>\n",
       "      <td>420</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agglomerative</th>\n",
       "      <td>paper</td>\n",
       "      <td>188</td>\n",
       "      <td>3157</td>\n",
       "      <td>769</td>\n",
       "      <td>2388</td>\n",
       "      <td>107</td>\n",
       "      <td>47</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha=30</th>\n",
       "      <td>moji</td>\n",
       "      <td>220</td>\n",
       "      <td>3286</td>\n",
       "      <td>798</td>\n",
       "      <td>2488</td>\n",
       "      <td>102</td>\n",
       "      <td>42</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0                   1                          2  \\\n",
       "Unnamed: 0       NaN  Number of clusters  Number of stocks in total   \n",
       "k-means        paper                 129                       3157   \n",
       "k=500           moji                 218                       3286   \n",
       "DBSCAN         paper                   2                       3157   \n",
       "alpha=10        moji                1.54                       3286   \n",
       "Agglomerative  paper                 188                       3157   \n",
       "alpha=30        moji                 220                       3286   \n",
       "\n",
       "                                          3                        4  \\\n",
       "Unnamed: 0     Number of stocks in clusters  Number of outliers 1900   \n",
       "k-means                                1257                     1900   \n",
       "k=500                                  1990                     1296   \n",
       "DBSCAN                                  376                     2781   \n",
       "alpha=10                                429                     2847   \n",
       "Agglomerative                           769                     2388   \n",
       "alpha=30                                798                     2488   \n",
       "\n",
       "                                                     5  \\\n",
       "Unnamed: 0     Number of stocks in the biggest cluster   \n",
       "k-means                                             63   \n",
       "k=500                                               67   \n",
       "DBSCAN                                             336   \n",
       "alpha=10                                           420   \n",
       "Agglomerative                                      107   \n",
       "alpha=30                                           102   \n",
       "\n",
       "                                                            6  \\\n",
       "Unnamed: 0     Number of stocks in the second biggest cluster   \n",
       "k-means                                                    51   \n",
       "k=500                                                      56   \n",
       "DBSCAN                                                     32   \n",
       "alpha=10                                                   19   \n",
       "Agglomerative                                              47   \n",
       "alpha=30                                                   42   \n",
       "\n",
       "                                                           7  \n",
       "Unnamed: 0     Number of stocks in the third biggest cluster  \n",
       "k-means                                                   45  \n",
       "k=500                                                     50  \n",
       "DBSCAN                                                     6  \n",
       "alpha=10                                                   9  \n",
       "Agglomerative                                             29  \n",
       "alpha=30                                                  24  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clustering_results_comparation_with_paper_results.T"
   ]
  }
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