5-summary-pair-selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3c81f209",
   "metadata": {},
   "source": [
    "We will do cointegration & unit root tests for all of the selected pairs, and eliminate some of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "8117edec",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:35:39.195060Z",
     "start_time": "2022-11-01T17:35:39.127082Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "import seaborn\n",
    "from statsmodels.tsa.stattools import coint\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "import statsmodels.api as sm\n",
    "import arch.unitroot as at"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78404615",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-31T05:24:26.150290Z",
     "start_time": "2022-10-31T05:24:26.134290Z"
    }
   },
   "source": [
    "### SETUP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04fbcad8",
   "metadata": {},
   "source": [
    "Define intervals to drop:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "406125c7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:35:39.238057Z",
     "start_time": "2022-11-01T17:35:39.215059Z"
    }
   },
   "outputs": [],
   "source": [
    "conf_int_to_drop = [0.90]  # drop list for intervals\n",
    "# conf_int_to_drop = [0.90, 0.95]  # drop list for intervals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1db8746a",
   "metadata": {},
   "source": [
    "### GET ALL TICKERS & PAIRS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "705261e6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:35:39.394912Z",
     "start_time": "2022-11-01T17:35:39.249059Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 pairs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('APO', 'BEN'),\n",
       " ('APO', 'C'),\n",
       " ('APO', 'STT'),\n",
       " ('BAC', 'STT'),\n",
       " ('BEN', 'KKR'),\n",
       " ('BX', 'COF'),\n",
       " ('BX', 'USB'),\n",
       " ('COF', 'USB'),\n",
       " ('RF', 'SCHW'),\n",
       " ('RF', 'STT')]"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_pickle(\"pairs_to_download.pkl\")\n",
    "\n",
    "tickers_list = list(df.index.union(df.columns))\n",
    "\n",
    "x_list = df.index.tolist()\n",
    "y_list = df.columns.tolist()\n",
    "\n",
    "pairs_list = [] # list of pairs as tuples (A,B)\n",
    "\n",
    "for i in range(len(x_list)):\n",
    "    for j in range(len(y_list)):\n",
    "        if df.iloc[i,j]>0:\n",
    "            pairs_list.append((x_list[i], y_list[j]))\n",
    "\n",
    "print(f\"{len(pairs_list)} pairs\")\n",
    "pairs_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02231c80",
   "metadata": {},
   "source": [
    "### GET TICKER PRICES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "6931923a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:35:39.642934Z",
     "start_time": "2022-11-01T17:35:39.401910Z"
    }
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>APO</th>\n",
       "      <th>BAC</th>\n",
       "      <th>BEN</th>\n",
       "      <th>BX</th>\n",
       "      <th>C</th>\n",
       "      <th>COF</th>\n",
       "      <th>KKR</th>\n",
       "      <th>RF</th>\n",
       "      <th>SCHW</th>\n",
       "      <th>STT</th>\n",
       "      <th>USB</th>\n",
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       "      <th>Date</th>\n",
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       "      <th>2021-11-03 00:00:00-04:00</th>\n",
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       "      <th>2021-11-05 00:00:00-04:00</th>\n",
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       "      <td>35.880001</td>\n",
       "      <td>142.550003</td>\n",
       "      <td>66.989998</td>\n",
       "      <td>154.869995</td>\n",
       "      <td>80.279999</td>\n",
       "      <td>24.309999</td>\n",
       "      <td>81.150002</td>\n",
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       "      <th>2022-10-27 00:00:00-04:00</th>\n",
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      "text/plain": [
       "                                 APO        BAC        BEN          BX  \\\n",
       "Date                                                                     \n",
       "2021-11-01 00:00:00-04:00  76.330002  47.849998  35.150002  137.570007   \n",
       "2021-11-02 00:00:00-04:00  76.419998  47.880001  37.680000  143.929993   \n",
       "2021-11-03 00:00:00-04:00  77.970001  48.369999  37.250000  144.500000   \n",
       "2021-11-04 00:00:00-04:00  77.290001  47.330002  36.270000  144.529999   \n",
       "2021-11-05 00:00:00-04:00  76.940002  46.970001  35.880001  142.550003   \n",
       "...                              ...        ...        ...         ...   \n",
       "2022-10-26 00:00:00-04:00  54.139999  35.700001  23.450001   92.739998   \n",
       "2022-10-27 00:00:00-04:00  54.290001  35.869999  23.309999   93.449997   \n",
       "2022-10-28 00:00:00-04:00  55.630001  36.180000  23.719999   93.180000   \n",
       "2022-10-31 00:00:00-04:00  55.360001  36.040001  23.450001   91.139999   \n",
       "2022-11-01 00:00:00-04:00  57.130001  36.154999  22.360001   92.290001   \n",
       "\n",
       "                                   C         COF        KKR         RF  \\\n",
       "Date                                                                     \n",
       "2021-11-01 00:00:00-04:00  69.610001  153.009995  77.650002  24.150000   \n",
       "2021-11-02 00:00:00-04:00  68.599998  151.910004  80.760002  23.959999   \n",
       "2021-11-03 00:00:00-04:00  69.000000  153.899994  83.400002  24.709999   \n",
       "2021-11-04 00:00:00-04:00  67.000000  153.460007  81.080002  24.270000   \n",
       "2021-11-05 00:00:00-04:00  66.989998  154.869995  80.279999  24.309999   \n",
       "...                              ...         ...        ...        ...   \n",
       "2022-10-26 00:00:00-04:00  45.410000  101.919998  48.240002  21.290001   \n",
       "2022-10-27 00:00:00-04:00  45.439999  102.129997  48.730000  21.549999   \n",
       "2022-10-28 00:00:00-04:00  46.130001  107.099998  49.380001  21.850000   \n",
       "2022-10-31 00:00:00-04:00  45.860001  106.019997  48.630001  21.950001   \n",
       "2022-11-01 00:00:00-04:00  46.290001  107.175003  49.555000  22.245001   \n",
       "\n",
       "                                SCHW        STT        USB  \n",
       "Date                                                        \n",
       "2021-11-01 00:00:00-04:00  81.870003  99.330002  60.779999  \n",
       "2021-11-02 00:00:00-04:00  81.769997  99.440002  60.860001  \n",
       "2021-11-03 00:00:00-04:00  83.330002  99.599998  61.830002  \n",
       "2021-11-04 00:00:00-04:00  81.449997  96.529999  60.279999  \n",
       "2021-11-05 00:00:00-04:00  81.150002  95.669998  60.689999  \n",
       "...                              ...        ...        ...  \n",
       "2022-10-26 00:00:00-04:00  72.949997  72.279999  42.389999  \n",
       "2022-10-27 00:00:00-04:00  77.330002  73.099998  42.080002  \n",
       "2022-10-28 00:00:00-04:00  80.190002  74.779999  42.860001  \n",
       "2022-10-31 00:00:00-04:00  79.669998  74.000000  42.450001  \n",
       "2022-11-01 00:00:00-04:00  79.550003  74.839996  43.095001  \n",
       "\n",
       "[253 rows x 11 columns]"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices= pd.read_pickle(\"close_prices.pkl\")[tickers_list] \n",
    "prices"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69a9761d",
   "metadata": {},
   "source": [
    "### TESTS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70df1c20",
   "metadata": {},
   "source": [
    "#### COINTEGRATION TEST"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f4a9750",
   "metadata": {},
   "source": [
    "Define a function to loop through the tickers and do a cointegration test for each combination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "7ed449fd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:35:39.707446Z",
     "start_time": "2022-11-01T17:35:39.664954Z"
    }
   },
   "outputs": [],
   "source": [
    "def cointegration_test(data, p_value=0.05):\n",
    "    n = data.shape[1]\n",
    "    # create matrix to be filled later\n",
    "    stats = np.zeros((n, n))\n",
    "    pvalues = np.ones((n, n))\n",
    "    confints = np.zeros((n, n))\n",
    "    keys = data.keys() # get the info axis, returns columns if object is a dataframe\n",
    "    pairs = []\n",
    "    for i in range(n):\n",
    "        for j in range(i+1, n):\n",
    "            result = coint(data[keys[i]], data[keys[j]])\n",
    "            test_stat = result[0]\n",
    "            pvalue = result[1]\n",
    "            stats[i, j] = test_stat\n",
    "            pvalues[i, j] = pvalue\n",
    "            \n",
    "            if pvalue < p_value: # pick only statistically significant ones\n",
    "                pairs.append((keys[i], keys[j]))\n",
    "                if result[2][0]>test_stat:\n",
    "                    confints[i, j]=0.99 \n",
    "                elif result[2][1]>test_stat:\n",
    "                    confints[i, j]=0.95\n",
    "                elif result[2][2]>test_stat:\n",
    "                    confints[i, j]=0.90\n",
    "                else:\n",
    "                    confints[i, j]=1-pvalue               \n",
    "    return stats, pvalues, pairs, confints"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee1b8941",
   "metadata": {},
   "source": [
    "Perform Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "43632afc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:35:41.181960Z",
     "start_time": "2022-11-01T17:35:39.727509Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "p_value = 0.05  # A p-value less than 0.05 is typically considered to be statistically significant\n",
    "\n",
    "scores, pvalues, pairs, confints = cointegration_test(prices,p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f11106b6",
   "metadata": {},
   "source": [
    "HeatMap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "b21a4a93",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:35:42.061420Z",
     "start_time": "2022-11-01T17:35:41.185970Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "remaining 10 pairs from 10\n"
     ]
    },
    {
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       "  color: black;\n",
       "}\n",
       "#T_fb8a0_row0_col2, #T_fb8a0_row0_col3, #T_fb8a0_row0_col4, #T_fb8a0_row0_col6, #T_fb8a0_row1_col0, #T_fb8a0_row1_col1, #T_fb8a0_row1_col2, #T_fb8a0_row1_col3, #T_fb8a0_row1_col4, #T_fb8a0_row1_col6, #T_fb8a0_row2_col0, #T_fb8a0_row2_col1, #T_fb8a0_row2_col2, #T_fb8a0_row2_col4, #T_fb8a0_row2_col5, #T_fb8a0_row2_col6, #T_fb8a0_row3_col0, #T_fb8a0_row3_col1, #T_fb8a0_row3_col3, #T_fb8a0_row3_col4, #T_fb8a0_row3_col5, #T_fb8a0_row4_col0, #T_fb8a0_row4_col1, #T_fb8a0_row4_col2, #T_fb8a0_row4_col3, #T_fb8a0_row4_col4, #T_fb8a0_row4_col5, #T_fb8a0_row5_col0, #T_fb8a0_row5_col1, #T_fb8a0_row5_col2, #T_fb8a0_row5_col3, #T_fb8a0_row5_col6 {\n",
       "  color: white;\n",
       "}\n",
       "#T_fb8a0_row1_col5, #T_fb8a0_row2_col3 {\n",
       "  background-color: green;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_fb8a0_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >BEN</th>\n",
       "      <th class=\"col_heading level0 col1\" >C</th>\n",
       "      <th class=\"col_heading level0 col2\" >COF</th>\n",
       "      <th class=\"col_heading level0 col3\" >KKR</th>\n",
       "      <th class=\"col_heading level0 col4\" >SCHW</th>\n",
       "      <th class=\"col_heading level0 col5\" >STT</th>\n",
       "      <th class=\"col_heading level0 col6\" >USB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_fb8a0_level0_row0\" class=\"row_heading level0 row0\" >APO</th>\n",
       "      <td id=\"T_fb8a0_row0_col0\" class=\"data row0 col0\" >0.95</td>\n",
       "      <td id=\"T_fb8a0_row0_col1\" class=\"data row0 col1\" >0.95</td>\n",
       "      <td id=\"T_fb8a0_row0_col2\" class=\"data row0 col2\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row0_col4\" class=\"data row0 col4\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row0_col5\" class=\"data row0 col5\" >0.95</td>\n",
       "      <td id=\"T_fb8a0_row0_col6\" class=\"data row0 col6\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb8a0_level0_row1\" class=\"row_heading level0 row1\" >BAC</th>\n",
       "      <td id=\"T_fb8a0_row1_col0\" class=\"data row1 col0\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row1_col1\" class=\"data row1 col1\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row1_col4\" class=\"data row1 col4\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row1_col5\" class=\"data row1 col5\" >0.99</td>\n",
       "      <td id=\"T_fb8a0_row1_col6\" class=\"data row1 col6\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb8a0_level0_row2\" class=\"row_heading level0 row2\" >BEN</th>\n",
       "      <td id=\"T_fb8a0_row2_col0\" class=\"data row2 col0\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row2_col2\" class=\"data row2 col2\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row2_col3\" class=\"data row2 col3\" >0.99</td>\n",
       "      <td id=\"T_fb8a0_row2_col4\" class=\"data row2 col4\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row2_col5\" class=\"data row2 col5\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row2_col6\" class=\"data row2 col6\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb8a0_level0_row3\" class=\"row_heading level0 row3\" >BX</th>\n",
       "      <td id=\"T_fb8a0_row3_col0\" class=\"data row3 col0\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row3_col1\" class=\"data row3 col1\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row3_col2\" class=\"data row3 col2\" >0.95</td>\n",
       "      <td id=\"T_fb8a0_row3_col3\" class=\"data row3 col3\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row3_col4\" class=\"data row3 col4\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row3_col5\" class=\"data row3 col5\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row3_col6\" class=\"data row3 col6\" >0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb8a0_level0_row4\" class=\"row_heading level0 row4\" >COF</th>\n",
       "      <td id=\"T_fb8a0_row4_col0\" class=\"data row4 col0\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row4_col1\" class=\"data row4 col1\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row4_col2\" class=\"data row4 col2\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row4_col4\" class=\"data row4 col4\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row4_col5\" class=\"data row4 col5\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row4_col6\" class=\"data row4 col6\" >0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb8a0_level0_row5\" class=\"row_heading level0 row5\" >RF</th>\n",
       "      <td id=\"T_fb8a0_row5_col0\" class=\"data row5 col0\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row5_col1\" class=\"data row5 col1\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row5_col2\" class=\"data row5 col2\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_fb8a0_row5_col4\" class=\"data row5 col4\" >0.95</td>\n",
       "      <td id=\"T_fb8a0_row5_col5\" class=\"data row5 col5\" >0.95</td>\n",
       "      <td id=\"T_fb8a0_row5_col6\" class=\"data row5 col6\" >nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x158e971a430>"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mask_matrix = (pvalues >= p_value) # possible to increase the level of significance\n",
    "conf_int = 0.90\n",
    "mask_matrix = (confints < conf_int) # possible to increase the level of significance\n",
    "\n",
    "mask_df = pd.DataFrame(mask_matrix, index= tickers_list, columns = tickers_list)\n",
    "pvalues_df = pd.DataFrame(pvalues, index= tickers_list, columns = tickers_list)\n",
    "confints_df = pd.DataFrame(confints, index= tickers_list, columns = tickers_list)\n",
    "\n",
    "df_p = pvalues_df[~mask_df].dropna(how='all', axis=1)\n",
    "df_p = df_p.dropna(how='all')\n",
    "\n",
    "df_c = confints_df[~mask_df].dropna(how='all', axis=1)\n",
    "df_c = df_c.dropna(how='all')\n",
    "\n",
    "\n",
    "def green_apply(x):\n",
    "    green = {True: \"background-color: green\", False: \"color : white\"}\n",
    "    is_color = (x > 0.95).replace(green)\n",
    "    return is_color\n",
    "\n",
    "def yellow_apply(x):\n",
    "    yellow = {True: \"background-color: yellow; color : black\"}\n",
    "    is_color = (x <= 0.95).replace(yellow)\n",
    "    return is_color\n",
    "\n",
    "# kept it here although we don't expect to see any red backgroud\n",
    "def red_apply(x):\n",
    "    red = {True: \"background-color: red;\"}\n",
    "    is_color = (x <= 0.90).replace(red)\n",
    "    return is_color\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "seaborn.heatmap(\n",
    "    df_c,\n",
    "    xticklabels=df_c.columns,\n",
    "    yticklabels=df_c.index,\n",
    "    cmap=\"summer_r\",\n",
    "    square=True,\n",
    "    vmin=0.90,\n",
    "    vmax=0.99\n",
    ")\n",
    "\n",
    "print(f\"remaining {df_c.count().sum()} pairs from {len(pairs_list)}\")\n",
    "\n",
    "df_c.style.format(precision=2)\\\n",
    ".apply(green_apply)\\\n",
    ".apply(yellow_apply)\\\n",
    ".apply(red_apply)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5155b8ce",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-26T23:06:29.483742Z",
     "start_time": "2022-10-26T23:06:29.467257Z"
    }
   },
   "source": [
    "#### UNIT ROOT TESTS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "145cd366",
   "metadata": {},
   "source": [
    "Loop through pairs & perform tests:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "215faa1f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:35:42.409416Z",
     "start_time": "2022-11-01T17:35:42.066416Z"
    }
   },
   "outputs": [],
   "source": [
    "df_c_adf = df_c.copy()\n",
    "df_c_pp = df_c.copy()\n",
    "hedges ={}\n",
    "\n",
    "N=90\n",
    "enable_regression_coef = 0\n",
    "\n",
    "# get new pairs\n",
    "x_list = df_c.index.tolist()\n",
    "y_list = df_c.columns.tolist()\n",
    "\n",
    "pairs_list = [] # list of pairs as tuples (A,B)\n",
    "\n",
    "for i in range(len(x_list)):\n",
    "    for j in range(len(y_list)):\n",
    "        if df.iloc[i,j]>0:\n",
    "            pairs_list.append((x_list[i], y_list[j]))\n",
    "\n",
    "for i in range(len(pairs_list)):\n",
    "    ticker1, ticker2 = list(pairs_list[i])[0], list(pairs_list[i])[1]\n",
    "    prices_pair= prices[list(pairs_list[i])]\n",
    "    \n",
    "    ###HEDGE###\n",
    "    \n",
    "    if N>0:\n",
    "        Y = prices_pair[ticker2].iloc[-N:]\n",
    "        X = prices_pair[ticker1].iloc[-N:]\n",
    "    else:\n",
    "        Y = prices_pair[ticker2]\n",
    "        X = prices_pair[ticker1]\n",
    "\n",
    "    if enable_regression_coef:\n",
    "        X = sm.add_constant(X)\n",
    "\n",
    "    model = sm.OLS(Y, X)\n",
    "    model = model.fit()\n",
    "\n",
    "    if len(model.params) == 1:\n",
    "        hedge = round(float(model.params[0]),2)\n",
    "        alfa = 0\n",
    "    else:\n",
    "        hedge = round(float(model.params[1]),2)\n",
    "        alfa = round(float(model.params[0]),2)\n",
    "    \n",
    "    hedges[(ticker1, ticker2)]=hedge\n",
    "    # print pair equation\n",
    "    #print(f\"EQUATION: {ticker2}={hedge}*{ticker1}{'+' + str(round(alfa,1)) if alfa>0 else ''}\")\n",
    "\n",
    "    ###SPREAD###\n",
    "\n",
    "    prices_pair_s = prices_pair.copy()\n",
    "    spread = prices_pair_s['spread'] = prices_pair[ticker2] - hedge * prices_pair[ticker1] - alfa\n",
    "    \n",
    "    ###ADF###\n",
    "    \n",
    "    adf = adfuller(prices_pair_s.spread, autolag='AIC')\n",
    "    test_stat_df = adf[0]\n",
    "    critical_values_df = adf[4]\n",
    "\n",
    "    if critical_values_df['1%']>test_stat_df:\n",
    "        df_c_adf.loc[(ticker1,ticker2)]=0.99 \n",
    "    elif critical_values_df['5%']>test_stat_df:\n",
    "        df_c_adf.loc[(ticker1,ticker2)]=0.95\n",
    "    elif critical_values_df['10%']>test_stat_df:\n",
    "        df_c_adf.loc[(ticker1,ticker2)]=0.90\n",
    "        \n",
    "    ###PP###  \n",
    "          \n",
    "    pp = at.PhillipsPerron(prices_pair_s.spread, trend='ct', test_type='rho')\n",
    "    test_stat_pp = pp.stat\n",
    "    critical_values_pp = pp.critical_values\n",
    "\n",
    "    if critical_values_pp['1%']>test_stat_pp:\n",
    "        df_c_pp.loc[(ticker1,ticker2)]=0.99 \n",
    "    elif critical_values_pp['5%']>test_stat_pp:\n",
    "        df_c_pp.loc[(ticker1,ticker2)]=0.95\n",
    "    elif critical_values_pp['10%']>test_stat_pp:\n",
    "        df_c_pp.loc[(ticker1,ticker2)]=0.90"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92acd52a",
   "metadata": {},
   "source": [
    "Heatmap ADF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "af038f02",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:35:42.949417Z",
     "start_time": "2022-11-01T17:35:42.412625Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_72f84_row0_col0, #T_72f84_row0_col1, #T_72f84_row0_col5, #T_72f84_row3_col2, #T_72f84_row4_col6, #T_72f84_row5_col4, #T_72f84_row5_col5 {\n",
       "  color: white;\n",
       "  background-color: yellow;\n",
       "  color: black;\n",
       "}\n",
       "#T_72f84_row0_col2, #T_72f84_row0_col3, #T_72f84_row0_col4, #T_72f84_row0_col6, #T_72f84_row1_col0, #T_72f84_row1_col1, #T_72f84_row1_col2, #T_72f84_row1_col3, #T_72f84_row1_col4, #T_72f84_row1_col6, #T_72f84_row2_col0, #T_72f84_row2_col1, #T_72f84_row2_col2, #T_72f84_row2_col4, #T_72f84_row2_col5, #T_72f84_row2_col6, #T_72f84_row3_col0, #T_72f84_row3_col1, #T_72f84_row3_col3, #T_72f84_row3_col4, #T_72f84_row3_col5, #T_72f84_row4_col0, #T_72f84_row4_col1, #T_72f84_row4_col2, #T_72f84_row4_col3, #T_72f84_row4_col4, #T_72f84_row4_col5, #T_72f84_row5_col0, #T_72f84_row5_col1, #T_72f84_row5_col2, #T_72f84_row5_col3, #T_72f84_row5_col6 {\n",
       "  color: white;\n",
       "}\n",
       "#T_72f84_row1_col5, #T_72f84_row2_col3, #T_72f84_row3_col6 {\n",
       "  background-color: green;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_72f84_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >BEN</th>\n",
       "      <th class=\"col_heading level0 col1\" >C</th>\n",
       "      <th class=\"col_heading level0 col2\" >COF</th>\n",
       "      <th class=\"col_heading level0 col3\" >KKR</th>\n",
       "      <th class=\"col_heading level0 col4\" >SCHW</th>\n",
       "      <th class=\"col_heading level0 col5\" >STT</th>\n",
       "      <th class=\"col_heading level0 col6\" >USB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_72f84_level0_row0\" class=\"row_heading level0 row0\" >APO</th>\n",
       "      <td id=\"T_72f84_row0_col0\" class=\"data row0 col0\" >0.95</td>\n",
       "      <td id=\"T_72f84_row0_col1\" class=\"data row0 col1\" >0.95</td>\n",
       "      <td id=\"T_72f84_row0_col2\" class=\"data row0 col2\" >nan</td>\n",
       "      <td id=\"T_72f84_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_72f84_row0_col4\" class=\"data row0 col4\" >nan</td>\n",
       "      <td id=\"T_72f84_row0_col5\" class=\"data row0 col5\" >0.95</td>\n",
       "      <td id=\"T_72f84_row0_col6\" class=\"data row0 col6\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_72f84_level0_row1\" class=\"row_heading level0 row1\" >BAC</th>\n",
       "      <td id=\"T_72f84_row1_col0\" class=\"data row1 col0\" >nan</td>\n",
       "      <td id=\"T_72f84_row1_col1\" class=\"data row1 col1\" >nan</td>\n",
       "      <td id=\"T_72f84_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
       "      <td id=\"T_72f84_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_72f84_row1_col4\" class=\"data row1 col4\" >nan</td>\n",
       "      <td id=\"T_72f84_row1_col5\" class=\"data row1 col5\" >0.99</td>\n",
       "      <td id=\"T_72f84_row1_col6\" class=\"data row1 col6\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_72f84_level0_row2\" class=\"row_heading level0 row2\" >BEN</th>\n",
       "      <td id=\"T_72f84_row2_col0\" class=\"data row2 col0\" >nan</td>\n",
       "      <td id=\"T_72f84_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
       "      <td id=\"T_72f84_row2_col2\" class=\"data row2 col2\" >nan</td>\n",
       "      <td id=\"T_72f84_row2_col3\" class=\"data row2 col3\" >0.99</td>\n",
       "      <td id=\"T_72f84_row2_col4\" class=\"data row2 col4\" >nan</td>\n",
       "      <td id=\"T_72f84_row2_col5\" class=\"data row2 col5\" >nan</td>\n",
       "      <td id=\"T_72f84_row2_col6\" class=\"data row2 col6\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_72f84_level0_row3\" class=\"row_heading level0 row3\" >BX</th>\n",
       "      <td id=\"T_72f84_row3_col0\" class=\"data row3 col0\" >nan</td>\n",
       "      <td id=\"T_72f84_row3_col1\" class=\"data row3 col1\" >nan</td>\n",
       "      <td id=\"T_72f84_row3_col2\" class=\"data row3 col2\" >0.95</td>\n",
       "      <td id=\"T_72f84_row3_col3\" class=\"data row3 col3\" >nan</td>\n",
       "      <td id=\"T_72f84_row3_col4\" class=\"data row3 col4\" >nan</td>\n",
       "      <td id=\"T_72f84_row3_col5\" class=\"data row3 col5\" >nan</td>\n",
       "      <td id=\"T_72f84_row3_col6\" class=\"data row3 col6\" >0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_72f84_level0_row4\" class=\"row_heading level0 row4\" >COF</th>\n",
       "      <td id=\"T_72f84_row4_col0\" class=\"data row4 col0\" >nan</td>\n",
       "      <td id=\"T_72f84_row4_col1\" class=\"data row4 col1\" >nan</td>\n",
       "      <td id=\"T_72f84_row4_col2\" class=\"data row4 col2\" >nan</td>\n",
       "      <td id=\"T_72f84_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_72f84_row4_col4\" class=\"data row4 col4\" >nan</td>\n",
       "      <td id=\"T_72f84_row4_col5\" class=\"data row4 col5\" >nan</td>\n",
       "      <td id=\"T_72f84_row4_col6\" class=\"data row4 col6\" >0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_72f84_level0_row5\" class=\"row_heading level0 row5\" >RF</th>\n",
       "      <td id=\"T_72f84_row5_col0\" class=\"data row5 col0\" >nan</td>\n",
       "      <td id=\"T_72f84_row5_col1\" class=\"data row5 col1\" >nan</td>\n",
       "      <td id=\"T_72f84_row5_col2\" class=\"data row5 col2\" >nan</td>\n",
       "      <td id=\"T_72f84_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_72f84_row5_col4\" class=\"data row5 col4\" >0.95</td>\n",
       "      <td id=\"T_72f84_row5_col5\" class=\"data row5 col5\" >0.95</td>\n",
       "      <td id=\"T_72f84_row5_col6\" class=\"data row5 col6\" >nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
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     "metadata": {},
     "output_type": "execute_result"
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wRObEJLvozUI+tHtP9/mQJe2ZJEkayTQ+OmBchj39euW4OiJJkrSv9uf2a0mSNEWWcyIzPbODJEmS9pGJjCRJjTORkSRJapCJjCRJjZumdWTGbfleuSRJap6FjCRJapZDS5IkNW4FTvaVJElqjomMJEmN8/ZrSZKkBpnISJLUOG+/liRJapCJjCRJjXOOjCRJUoNMZCRJapyJjCRJUoNMZCRJapx3LUmSJDXIREZaQnntKZPuwsTUO2+adBcmarlfv8bLOTKSJEkNMpGRJKlxPv1akiSpQRYykiSpWQ4tSZLUOCf7SpIkNchERpKkxrkgniRJUoNMZCRJapxzZCRJkhpkIiNJUuPiHBlJkqT2mMhIktS4Fcs4l1i+Vy5JkppnIiNJUuOcIyNJktQgExlJkhrnyr6SJEkNMpGRJKlxWca5xPK9ckmS1DwLGUmS1CyHliRJapyTfSVJkhq0YCKT5PeBmqe5quq8xe+SJEnaF8t5su+woaUPzrHtGcCbgJWL3htJkqR9sGAhU1Xvn32f5BhgI/Bi4GLg0qXtmiRJGoVzZBaQ5Pgkfwj8BXADsK6q3llVjy557yRJkhYwbI7MnwAnA78B/AKwB3hKEgCq6stL3UFJkrSw5fzQyGFzZJ5Pb7Lvm4F/221L92cBxyxRvyRJkoYaNkdmzZj6IUmS9tOKKbprKclpwCZ6NwW9q6ouHmhfBVwGHAt8FXhNVd3Vtf0t8A/0RoB2V9XJw8634JUn+am+9y8caLtwhOuRJEnLRJKVwCXA6cA64Owk6wZ22whsqarnAOfQK3r6vbSqThqliIHhk33/Td/7tw+0vWa+g5JsSHJrkltnZmZG6YckSdpPyYqxvYZYD+yoqvu6m4KuAM4Y2GcdcB1AVW0H1iQ5fH+vfdgcmczzfq7Pj6mqGWC2gplvQT1JknRgOQJ4oO/zTuC7B/a5AzgTuCHJeuAoYDXweXo1w7VJCvi9rp5Y0LBCpuZ5P9dnSZI0AeNcRybJBmBD36aZvoJjrpBjsF64GNiUZAtwJ3A7sLtre2FVPZjk6cCHk2yvqusX6s+wQua4JFu7jh3bvZ/tqHcsSZK0zAyMugzaCRzZ93k18ODA8buAcwHSW8/l/u5FVT3Y/flQkivpDVV9U4XM8UPaJUnShGV6nhp0C7A2ydHAZ4CzgFf175DkMODhbg7N+cD1VbUryZOAFVX1D937HwB+ZdgJh91+/anBbUmeBnypqhxakiRJj6mq3d1dzdfQu/36sqraluSCrn0zvZDk8iR7gLuB2QdQHw5c2S26exDwP6vqQ8POOWxl31PojWV9GfhV4A+ApwErkpwzygkkSdLyUVVXA1cPbNvc9/5GYO0cx90HnLiv5xs2tPQOevd7fyvwEeD0qropyXHAewELGUmSJsyHRs7voKq6tqr+BPhcVd0Ej933LUmSNFHDEpm9fe8fGWhzjowkSVMgU/SIgnEbVsicmGQXvdutD+3e030+ZEl7JkmSNMSwu5am5n4uSZI0N+fISJIkNWjY0JIkSZpyIzzM8YC1fK9ckiQ1z0RGkqTGrVjGucTyvXJJktQ8ExlJkhrnHBlJkqQGmchIktQ415GRJElqkImMJEmNW87PWlq+Vy5JkppnISNJkprl0JIkSY1zsq8kSVKDTGQkSWqck30lSZIaZCIjSVLjnCMjSZLUIBMZSZIa50MjJUmSGmQioyX10COXT7oLE1XvvGnSXZAmIq89ZdJdmKhx/9tPjfNkYzzXCExkJElSs0xkJElqXe0d37lMZCRJkhaHiYwkSa0bZyIzZUxkJElSs0xkJElqnYmMJElSe0xkJElqnYmMJElSeyxkJElSsxxakiSpdXsdWpIkSWqOiYwkSa1zsq8kSVJ7TGQkSWqdiYwkSVJ7TGQkSWqdiYwkSVJ7TGQkSWqd68hIkiS1x0RGkqTWOUdGkiSpPSYykiS1zkRGkiSpPSYykiS1zkRGkiTpm5fktCT3JtmR5KI52lcluTLJ1iQ3JzlhoH1lktuTfHCU8y1YyCR58gJtx45yAkmStDwkWQlcApwOrAPOTrJuYLeNwJaqeg5wDrBpoP2NwD2jnnNYInNHklcOdPKQJL8GfGjUk0iSpKVTtWdsryHWAzuq6r6qehS4AjhjYJ91wHW9ftd2YE2SwwGSrAZ+CHjXqNc+rJD5AeDcJB9O8p1JzgDuBL4FeO6oJ5EkScvCEcADfZ93dtv63QGcCZBkPXAUsLpr+23g3wEjT/pZcLJvVX0SOD3JW4DtwOeAH6yqbaOeQJIkLbExPqIgyQZgQ9+mmaqamW2e45Aa+HwxsCnJFnrhyO3A7iQvBx6qqtuSvGTU/ixYyCQ5CHgLcB7wOuBlwO8keV1V3TvqSSRJ0oGhK1pm5mneCRzZ93k18ODA8buAcwGSBLi/e50F/EiSlwGHAE9J8odV9VML9WfY0NLt9CKhf1lVM1X1o8BvAX+e5L/Md1CSDUluTXLrzMx81ypJkhZF7R3fa2G3AGuTHJ3kYHrFyVX9OyQ5rGsDOB+4vqp2VdVbq2p1Va3pjvvIsCIGhq8j8+qquu0b/q6qPpjkOuBt8x00UK0NRkqSJOkAVFW7k1wIXAOsBC6rqm1JLujaNwPHA5cn2QPcTW/UZ78NmyNz2+C2JE8DvlRV/+GbObEkSVokU7QgXlVdDVw9sG1z3/sbgbVDvsbHgI+Ncr5h68ickuRjST6Q5LlJ7gLuAj6f5LRRTiBJkrRUhg0tvYPewjXfCnwEOL2qbkpyHPBeXEtGkqTJm6JEZtyGTfY9qKqurao/AT5XVTfBYwvYSJIkTdSwRKa/xHtkoM1JvJIkTYNlnMgMK2ROTLKL3gI3h3bv6T4fsqQ9kyRJGmLYXUsrx9URSZK0n8a4su+0GTZHRpIkaWoNG1qSJEnTbhnPkTGRkSRJzbKQkSRJzXJoSZKk1jm0JEmS1B4TGUmSWmciI0mS1B4TGUmSWueCeJIkSe0xkZEkqXXOkZEkSWqPiYwkSa0zkZEkSWqPiYwkSa3zriVJkqT2mMhIktS6vTXpHkyMiYwkSWqWiYwkSa1zjowkSVJ7LGQkSVKzHFqSJKl1Di1JkiS1x0RGS+rph54z6S5ImoDP/+brJt2F5cXbryVJktpjIiNJUuucIyNJktQeExlJklpnIiNJktQeExlJklrnXUuSJEntMZGRJKl1zpGRJElqj4mMJEmtc46MJElSe0xkJElqnXNkJEmS2mMhI0mSmuXQkiRJrXNoSZIkqT0mMpIkNa5qfLdfZ2xnGo2JjCRJapaJjCRJrXOOjCRJUntMZCRJap2JjCRJUnssZCRJat3eGt9riCSnJbk3yY4kF83RvirJlUm2Jrk5yQnd9kO6z3ck2Zbkl0e59AULmSTv7nv/M6N8QUmStDwlWQlcApwOrAPOTrJuYLeNwJaqeg5wDrCp2/5PwPdW1YnAScBpSU4Zds5hicyJfe/fOPQKJEnS+O3dO77XwtYDO6rqvqp6FLgCOGNgn3XAdQBVtR1Yk+Tw6vlKt88TutfQCGhYITO+FXYkSVLrjgAe6Pu8s9vW7w7gTIAk64GjgNXd55VJtgAPAR+uqo8PO+Gwu5ZWJ/kdegv5zb5/TFW9YdgJJEnSEhvjXUtJNgAb+jbNVNXMbPMchwyGIhcDm7qC5U7gdmA3QFXtAU5KchhwZZITququhfozrJB5S9/7W4fsK0mSDnBd0TIzT/NO4Mi+z6uBBweO3wWcC5AkwP3dq3+fv0/yMeA0YP8Lmap6z+z7JE/ubap/XOgYSZI0ZiPcTTQmtwBrkxwNfAY4C3hV/w5d2vJwN4fmfOD6qtqV5J8DX+uKmEOB7wN+fdgJh95+neS1ST4NfAr4dJJPJXndPl6YJEk6wFXVbuBC4BrgHuCPq2pbkguSXNDtdjywLcl2enc3zd5M9O3AR5NspVcQfbiqPjjsnAsmMkneBrwAeElV3ddtO4be2NZTq+rX9vkqJUnSAauqrgauHti2ue/9jcDaOY7bCjx3X883LJH5aeDM2SKmO9F9wCvp3fs9pyQbktya5NaZmfmG0SRJ0qKYntuvx27os5aq6qtzbHskybxXMzARaGoG7iRJ0oFlWCGzM8mpVXVd/8YkpwKfXbpuSZKkkU1hUjIuwwqZNwB/nuQG4DZ66crzgRfy+JX6JEmSxmpYIfNPwKuBZwLPprfQzfXApcDjhpwkSdIETM/t12M3rJD5bWBjVV3WvzHJyV3bDy9NtyRJkoYbVsis6W6H+gZVdWuSNUvTJUmStE+W8RyZYbdfH7JA26GL2RFJkqR9NayQuSXJzw5uTHIevcm/kiRp0lxHZl5vovf0yZ/k64XLycDBwCuWsF+SJElDDXto5OeBFyR5KXBCt/l/VdVHlrxnkiRpNN61tLCq+ijw0SXuiyRJ0j4ZqZCRJElTbArnrozLsMm+kiRJU8tERpKkxtWe5TtHxkRGkiQ1y0JGkiQ1y6ElSZJat4xvvzaRkSRJzTKRkSSpdU72lSRJao+JjCRJjSvnyEiSJLXHREaSpNY5R0aSJKk9JjKSJLVujw+NlCRJao6JjCRJjfOuJUmSpAaZyEiS1DrvWpIkSWqPiYwkSa1bxnNkLGQkLYmHHrl80l3QBD390HMm3QUtEw4tSZKkZpnISJLUuHKyryRJUntMZCRJat1eH1EgSZLUHBMZSZJa5xwZSZKk9pjISJLUOB8aKUmS1CATGUmSWuccGUmSpPaYyEiS1DoTGUmSpPaYyEiS1DjvWpIkSWqQiYwkSa3b47OWJEmSmmMhI0mSmmUhI0lS42pvje01TJLTktybZEeSi+ZoX5XkyiRbk9yc5IRu+5FJPprkniTbkrxxlGu3kJEkSYsiyUrgEuB0YB1wdpJ1A7ttBLZU1XOAc4BN3fbdwL+tquOBU4Cfn+PYx7GQkSSpdXtqfK+FrQd2VNV9VfUocAVwxsA+64DrAKpqO7AmyeFV9dmq+kS3/R+Ae4Ajhp3QQkaSJC2WI4AH+j7v5PHFyB3AmQBJ1gNHAav7d0iyBngu8PFhJ/T2a0mSWjfGBfGSbAA29G2aqaqZ2eY5Dhns3MXApiRbgDuB2+kNK81+/ScD7wfeVFW7hvXHQkaSJI2sK1pm5mneCRzZ93k18ODA8buAcwGSBLi/e5HkCfSKmD+qqg+M0h8LGUmSGlfT89DIW4C1SY4GPgOcBbyqf4ckhwEPd3Nozgeur6pdXVFzKXBPVf3mqCe0kJEkSYuiqnYnuRC4BlgJXFZV25Jc0LVvBo4HLk+yB7gbOK87/IXATwN3dsNOABur6uqFzrlgIZPkoKravdA+kiRpwqbooZFd4XH1wLbNfe9vBNbOcdwNzD3HZkHD7lq6efZNkrfv6xeXJElaSsOGlvoroxcuZUckSdJ+8qGR85qerEqSJGnAsELmuO5ZCHf2vd+a5M4kW+c7KMmGJLcmuXVmZr47tCRJ0mKYpmctjduwoaXj9+eLDtxjPn1XLUmSDggLFjJV9am5tncPhToLmLNdkiSN0fSsIzN2Cw4tJXlKkrcmeUeSH0jP64H7gFeOp4uSJElzGza09AfA3wE30lt97y3AwcAZVbVlabsmSZK0sGGFzDFV9V0ASd4FfBF4Rvd4bUmSNAWmcRLuuAy7a+lrs2+qag9wv0WMJEmaFsMSmROTzD5CO8Ch3ecAVVVPWdLeSZKkoabooZFjN+yupZXj6ogkSdK+8unXkiQ1zjkykiRJDTKRkSSpcXuX8RwZExlJktQsExlJkhrnHBlJkqQGmchIktS42rt30l2YGBMZSZLULBMZSZIat5xX9jWRkSRJzTKRkSSpcd61JEmS1CATGUmSGuccGUmSpAZZyEiSpGY5tCRJUuOc7CtJktQgExlJkhq310RGkiSpPSYykiQ1ztuvJUmSGmQiI0lS47xrSZIkqUHjSGQyhnPMf/JkQ1XNTLIPk+T1L9/rn/S1P/3QcyZ1amDy1z9pXv/yun4TmQPbhkl3YMK8/uVrOV87eP1ev5YF58hIktQ471qSJElq0HJIZJbNGOk8vP7lazlfO3j9Xv8yUnv3TroLE5Oq5RtHSZJ0IHjgtOeN7Zf5kR/6xERv4hm0HBIZSZIOaM6RaVSSPUm2JLkjySeSvKDbvibJI13b7Oucru1vk7y/72v8eJJ3T+gSlkySf5HkiiSfTHJ3kquTPHPS/VpM811jkmcn+UiS/5vkb5L8xyTpjnl1ki/0fV9cPunr2B9JvtL3/mXddT4jyS8leXO3/ZAkH07yi93n2X8vdyX5iySHTaj7+yzJf0iyLcnW7hq+O8kTklzcXftdSW5Ocnq3/98meVrf8S9J8sH0fDHJqm77tyepJC/q2/cLSb5t/Fe57+b4e/lo9+eOJP+v7/v8a92fnx74/l8z6WvYV93P97sGtv1SkjcnOSXJx7truyfJL3Xt/f/utyX50yRPnMgFaNG1nsg8UlUnAST5QeC/Av+6a/vkbNscTk7y7KratvRdHL/ul/aVwHuq6qxu20nA4cD/nWDXFs2Qa3w38Nqqurb7YfV+4HXAJd3h76uqC8fe6SWQ5FTg7cAPVNWnu3qNJAfTu+7bquqXu937/728B/h54D+PvdP7KMn3AC8HnldV/9QVKAcDvwp8O3BCt/1wvv7vf05VVUk+DnwPcDXwAuD27s8bkjwL+GJVfWnprmhxzPf3UlUPJnkJ8OaqevnAMa8GTj5Qvv/n8B7glVV1R5KVwLP62h77d5/kfwI/Afz+BPqoRdZ0IjPgKcDfjbjvbwAbl7Avk/ZS4GtVtXl2Q1Vtqar/M8E+LbY5rxF4JvBXVXVtt+1h4ELgokl0cikl+VfA/wB+qKo+2dd0EHAF8DdVNd913wgcscRdXCzfTq+4+CeAqvoi8PfAzwKv79v++ar64xG+3l/RK1zo/vxNeoXN7Oe/XryuL6nH/b1U1YMT7tOkPR34LEBV7amquwd3SHIQ8CRG/33RhNpbY3tNm9YLmUO7qHA78C56/4U269h849DSv+pr+2PgeUm+c6y9HZ8TgNsm3YklNt81Pntwe/dL/slJntJt+om+74tzl7ifS+VbgD8HfrSqtg+0/Ttgd1W9aa4Du/9SPRW4akl7uHiuBY7shgp/N8m/Br4T+HRV7VrguNlhli30fj7M+mu+XsisB/4MOLL7/AJ6hU4L5vp7We5+C7g3yZVJfi7JIX1tP9F9L3wGeCrwF5PooBZf64XMI1V1UlUdB5wGXJ7ZbL0bWup79acRe4D/Drx13B3Wkgsw338yzG5/X9/3RavR8tfo/UI+b462G4DvyePnRB3a/SD/Er0f5B9e0h4ukqr6CvAv6a3U+gXgfcBLRjj0pbP/PwPn922/GXhukicBT+i+/n3df9g0k8jM9ffSDR0d6Ob9911VvwKcTK/IexXwob7293XfC/8CuBN4y1J2ctz27q2xvaZN64XMY6rqRuBpwD8f8ZA/AF4MPGPJOjU52+j9gDuQzXeN2+j9IHtMkmOAr1TVP4yjY2OyF3gl8Pwkg8Ok1wNvAv4yyXf0bZ+dI3MUvTkmPz+Gfi6KbpjgY1X1i/SGCn8YeEaSf7YfX+thYAfwGuAT3eabgJfRG5q4d3F6vfTm+Hv5sUn3aQy+BKwa2PZU4IvQS2Cr6p30UscTByduV2/Nkb+g9/NfB4ADppBJchywkt43+VBV9TV6MeSblrBbk/IR4FuS/OzshiTPP8Ci5zmvEfgb4EVJvq/bdijwO8B/m0gvl1D3C/nlwE8mOW+g7f30UscPZeDupKr6f8AbgDcnecKYurvfkjwrydq+TSfRKzYuBX6nm9g8ewfST434Zf+K3r/9G7vPNwJvBG6qRhbXmufv5VMT6s7YdEnUZ7uJ7iR5Kr1E/oYkP9SXyq+ll77//Rxf5kXAJ+fY3qzaU2N7TZvWC5nZOTJb6MXNP1NVe7q2wTkyb5jj+Etp/86tx+l+EL8C+P70bk3eBvwScMBMBBxyjWcAb0tyL70I+RbgHZPq61Kqqi/T+yH+tiRnDLRtBj4AXDUwV4Cquh24AzhrXH39JjwZeE96t9hvBdbR+//6bfSGVO7ubsf9s+7zKP4KOIavFzKfAFbTyLBSZ76/l+XgHHrf81vo/UfNL3dz4X6a3hyZLfRS95/s+50wOzduK/BcvnFOpRrmyr6SJDXuky/6rrH9Mj/2hjunamXf1hMZSZI0RZKcluTe9BZmfNwSEElWdXeWbU1vIcsT+touS/JQBhY9XIiFjCRJjZuWOTLd8g6XAKfTG+48O8m6gd02Aluq6jn0hgk39bW9m95w+cgsZCRJ0mJZD+yoqvuq6lF6i3OeMbDPOuA6gG4drDXdytxU1fXAl/flhBYykiQ1bopW9j0CeKDv804ev4r4HcCZAEnW01sSYvX+XruFjCRJGlmSDUlu7Xtt6G+e45DB6udiYFV3d9nr6T3vbPf+9ueAu/VYkqTlZpzPQKqqGWBmnuadfP2RH9BLWr5h6Y/u0SLnwmMPAL6/e+0XExlJkrRYbgHWJjm6W6zyLAae65bksNmFLOk9PuT6Ic9NW5CJjCRJjZuWFXeraneSC4Fr6K22f1lVbUtyQde+GTie3rMR9wB30/fMuCTvpfcstacl2Qn8YlVdutA5XRBPkqTG3fu848f2y/xZn7jHBfEkSZIWg0NLkiQ1bu8YJ/tOGxMZSZLULBMZSZIat3fvpHswOSYykiSpWSYykiQ1zkRGkiSpQSYykiQ1zkRGkiSpQSYykiQ1bhkvI2MiI0mS2mUiI0lS45wjI0mS1CATGUmSGmciI0mS1CATGUmSGmciI0mS1CALGUmS1CyHliRJapxDS5IkSQ0ykZEkqXEmMpIkSQ0ykZEkqXEmMpIkSQ0ykZEkqXEmMpIkSQ0ykZEkqXEmMpIkSQ0ykZEkqXFVNekuTIyJjCRJapaJjCRJjXOOjCRJUoNMZCRJapyJjCRJUoMsZCRJUrMcWpIkqXEOLUmSJDXIREaSpMaZyEiSJDXIREaSpMaZyEiSJDXIREaSpMaZyEiSJDXIREaSpMaZyEiSJDXIREaSpMbtrUn3YHJMZCRJUrNMZCRJapxzZCRJkhpkIiNJUuNMZCRJkhpkISNJkpplISNJUuP27h3fa5gkpyW5N8mOJBfN0b4qyZVJtia5OckJox47FwsZSZK0KJKsBC4BTgfWAWcnWTew20ZgS1U9BzgH2LQPxz6OhYwkSY2bokRmPbCjqu6rqkeBK4AzBvZZB1wHUFXbgTVJDh/x2MexkJEkSYvlCOCBvs87u2397gDOBEiyHjgKWD3isY/j7deSJDXuVXVvxnWuJBuADX2bZqpqZrZ5jkMGH6BwMbApyRbgTuB2YPeIxz6OhYwkSRpZV7TMzNO8Eziy7/Nq4MGB43cB5wIkCXB/93risGPn4tCSJElaLLcAa5McneRg4Czgqv4dkhzWtQGcD1zfFTdDj52LiYwkSVoUVbU7yYXANcBK4LKq2pbkgq59M3A8cHmSPcDdwHkLHTvsnKlaxs/+liRJTXNoSZIkNctCRpIkNctCRpIkNctCRpIkNctCRpIkNctCRpIkNctCRpIkNctCRpIkNev/Azp+Wr8dp2KKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_int = 0.95\n",
    "\n",
    "def green_apply(x):\n",
    "    green = {True: \"background-color: green\", False: \"color : white\"}\n",
    "    is_color = (x > conf_int).replace(green)\n",
    "    return is_color\n",
    "\n",
    "def yellow_apply(x):\n",
    "    yellow = {True: \"background-color: yellow; color : black\"}\n",
    "    is_color = (x <= conf_int).replace(yellow)\n",
    "    return is_color\n",
    "\n",
    "def red_apply(x):\n",
    "    red = {True: \"background-color: red\"}\n",
    "    is_color = (x <= 0.90).replace(red)\n",
    "    return is_color\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "seaborn.heatmap(\n",
    "    df_c_adf,\n",
    "    xticklabels=df_c.columns,\n",
    "    yticklabels=df_c.index,\n",
    "    cmap=\"RdYlGn\",\n",
    "    #mask=(df_c_adf < conf_int),\n",
    "    square=True,\n",
    "    vmin=0.90,\n",
    "    vmax=0.99\n",
    ")\n",
    "df_c_adf.style.format(precision=2)\\\n",
    ".apply(green_apply)\\\n",
    ".apply(yellow_apply)\\\n",
    ".apply(red_apply)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62db9b7b",
   "metadata": {},
   "source": [
    "Heatmap PP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "072d3cfa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:35:43.635987Z",
     "start_time": "2022-11-01T17:35:42.953405Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_0fff8_row0_col0, #T_0fff8_row0_col1, #T_0fff8_row0_col5, #T_0fff8_row3_col6, #T_0fff8_row4_col6, #T_0fff8_row5_col4, #T_0fff8_row5_col5 {\n",
       "  color: white;\n",
       "  background-color: yellow;\n",
       "  color: black;\n",
       "}\n",
       "#T_0fff8_row0_col2, #T_0fff8_row0_col3, #T_0fff8_row0_col4, #T_0fff8_row0_col6, #T_0fff8_row1_col0, #T_0fff8_row1_col1, #T_0fff8_row1_col2, #T_0fff8_row1_col3, #T_0fff8_row1_col4, #T_0fff8_row1_col6, #T_0fff8_row2_col0, #T_0fff8_row2_col1, #T_0fff8_row2_col2, #T_0fff8_row2_col4, #T_0fff8_row2_col5, #T_0fff8_row2_col6, #T_0fff8_row3_col0, #T_0fff8_row3_col1, #T_0fff8_row3_col3, #T_0fff8_row3_col4, #T_0fff8_row3_col5, #T_0fff8_row4_col0, #T_0fff8_row4_col1, #T_0fff8_row4_col2, #T_0fff8_row4_col3, #T_0fff8_row4_col4, #T_0fff8_row4_col5, #T_0fff8_row5_col0, #T_0fff8_row5_col1, #T_0fff8_row5_col2, #T_0fff8_row5_col3, #T_0fff8_row5_col6 {\n",
       "  color: white;\n",
       "}\n",
       "#T_0fff8_row1_col5 {\n",
       "  background-color: green;\n",
       "}\n",
       "#T_0fff8_row2_col3, #T_0fff8_row3_col2 {\n",
       "  color: white;\n",
       "  background-color: yellow;\n",
       "  color: black;\n",
       "  background-color: red;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_0fff8_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >BEN</th>\n",
       "      <th class=\"col_heading level0 col1\" >C</th>\n",
       "      <th class=\"col_heading level0 col2\" >COF</th>\n",
       "      <th class=\"col_heading level0 col3\" >KKR</th>\n",
       "      <th class=\"col_heading level0 col4\" >SCHW</th>\n",
       "      <th class=\"col_heading level0 col5\" >STT</th>\n",
       "      <th class=\"col_heading level0 col6\" >USB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_0fff8_level0_row0\" class=\"row_heading level0 row0\" >APO</th>\n",
       "      <td id=\"T_0fff8_row0_col0\" class=\"data row0 col0\" >0.95</td>\n",
       "      <td id=\"T_0fff8_row0_col1\" class=\"data row0 col1\" >0.95</td>\n",
       "      <td id=\"T_0fff8_row0_col2\" class=\"data row0 col2\" >nan</td>\n",
       "      <td id=\"T_0fff8_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_0fff8_row0_col4\" class=\"data row0 col4\" >nan</td>\n",
       "      <td id=\"T_0fff8_row0_col5\" class=\"data row0 col5\" >0.95</td>\n",
       "      <td id=\"T_0fff8_row0_col6\" class=\"data row0 col6\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fff8_level0_row1\" class=\"row_heading level0 row1\" >BAC</th>\n",
       "      <td id=\"T_0fff8_row1_col0\" class=\"data row1 col0\" >nan</td>\n",
       "      <td id=\"T_0fff8_row1_col1\" class=\"data row1 col1\" >nan</td>\n",
       "      <td id=\"T_0fff8_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
       "      <td id=\"T_0fff8_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_0fff8_row1_col4\" class=\"data row1 col4\" >nan</td>\n",
       "      <td id=\"T_0fff8_row1_col5\" class=\"data row1 col5\" >0.99</td>\n",
       "      <td id=\"T_0fff8_row1_col6\" class=\"data row1 col6\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fff8_level0_row2\" class=\"row_heading level0 row2\" >BEN</th>\n",
       "      <td id=\"T_0fff8_row2_col0\" class=\"data row2 col0\" >nan</td>\n",
       "      <td id=\"T_0fff8_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
       "      <td id=\"T_0fff8_row2_col2\" class=\"data row2 col2\" >nan</td>\n",
       "      <td id=\"T_0fff8_row2_col3\" class=\"data row2 col3\" >0.90</td>\n",
       "      <td id=\"T_0fff8_row2_col4\" class=\"data row2 col4\" >nan</td>\n",
       "      <td id=\"T_0fff8_row2_col5\" class=\"data row2 col5\" >nan</td>\n",
       "      <td id=\"T_0fff8_row2_col6\" class=\"data row2 col6\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fff8_level0_row3\" class=\"row_heading level0 row3\" >BX</th>\n",
       "      <td id=\"T_0fff8_row3_col0\" class=\"data row3 col0\" >nan</td>\n",
       "      <td id=\"T_0fff8_row3_col1\" class=\"data row3 col1\" >nan</td>\n",
       "      <td id=\"T_0fff8_row3_col2\" class=\"data row3 col2\" >0.90</td>\n",
       "      <td id=\"T_0fff8_row3_col3\" class=\"data row3 col3\" >nan</td>\n",
       "      <td id=\"T_0fff8_row3_col4\" class=\"data row3 col4\" >nan</td>\n",
       "      <td id=\"T_0fff8_row3_col5\" class=\"data row3 col5\" >nan</td>\n",
       "      <td id=\"T_0fff8_row3_col6\" class=\"data row3 col6\" >0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fff8_level0_row4\" class=\"row_heading level0 row4\" >COF</th>\n",
       "      <td id=\"T_0fff8_row4_col0\" class=\"data row4 col0\" >nan</td>\n",
       "      <td id=\"T_0fff8_row4_col1\" class=\"data row4 col1\" >nan</td>\n",
       "      <td id=\"T_0fff8_row4_col2\" class=\"data row4 col2\" >nan</td>\n",
       "      <td id=\"T_0fff8_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_0fff8_row4_col4\" class=\"data row4 col4\" >nan</td>\n",
       "      <td id=\"T_0fff8_row4_col5\" class=\"data row4 col5\" >nan</td>\n",
       "      <td id=\"T_0fff8_row4_col6\" class=\"data row4 col6\" >0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fff8_level0_row5\" class=\"row_heading level0 row5\" >RF</th>\n",
       "      <td id=\"T_0fff8_row5_col0\" class=\"data row5 col0\" >nan</td>\n",
       "      <td id=\"T_0fff8_row5_col1\" class=\"data row5 col1\" >nan</td>\n",
       "      <td id=\"T_0fff8_row5_col2\" class=\"data row5 col2\" >nan</td>\n",
       "      <td id=\"T_0fff8_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_0fff8_row5_col4\" class=\"data row5 col4\" >0.95</td>\n",
       "      <td id=\"T_0fff8_row5_col5\" class=\"data row5 col5\" >0.95</td>\n",
       "      <td id=\"T_0fff8_row5_col6\" class=\"data row5 col6\" >nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x158e977b670>"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_int = 0.95\n",
    "\n",
    "def green_apply(x):\n",
    "    green = {True: \"background-color: green\", False: \"color : white\"}\n",
    "    is_color = (x > conf_int).replace(green)\n",
    "    return is_color\n",
    "\n",
    "def yellow_apply(x):\n",
    "    yellow = {True: \"background-color: yellow; color : black\"}\n",
    "    is_color = (x <= conf_int).replace(yellow)\n",
    "    return is_color\n",
    "\n",
    "def red_apply(x):\n",
    "    red = {True: \"background-color: red\"}\n",
    "    is_color = (x <= 0.90).replace(red)\n",
    "    return is_color\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "seaborn.heatmap(\n",
    "    df_c_pp,\n",
    "    xticklabels=df_p.columns,\n",
    "    yticklabels=df_p.index,\n",
    "    cmap=\"RdYlGn\",\n",
    "    #mask=(df_c_adf < conf_int),\n",
    "    square=True,\n",
    "    vmin=0.90,\n",
    "    vmax=0.99\n",
    ")\n",
    "df_c_pp.style.format(precision=2)\\\n",
    ".apply(green_apply)\\\n",
    ".apply(yellow_apply)\\\n",
    ".apply(red_apply)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc6412e4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-26T23:10:13.032744Z",
     "start_time": "2022-10-26T23:10:13.021748Z"
    }
   },
   "source": [
    "#### DROP PAIRS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27d43d91",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-26T20:47:43.842333Z",
     "start_time": "2022-10-26T20:47:43.835334Z"
    }
   },
   "source": [
    "Drop all %90 conf int values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "ac14915d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:35:43.684990Z",
     "start_time": "2022-11-01T17:35:43.641989Z"
    }
   },
   "outputs": [],
   "source": [
    "# conf_int_to_drop = [0.90, 0.95]  # drop list for intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "688cc9a2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:35:43.794911Z",
     "start_time": "2022-11-01T17:35:43.697009Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dropped pairs:\n",
      "{('BX', 'COF'), ('BEN', 'KKR')}\n",
      "remaining 8 pairs from 10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('RF', 'STT'),\n",
       " ('APO', 'C'),\n",
       " ('BAC', 'STT'),\n",
       " ('APO', 'STT'),\n",
       " ('BX', 'USB'),\n",
       " ('RF', 'SCHW'),\n",
       " ('APO', 'BEN'),\n",
       " ('COF', 'USB')]"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for %90 conf intervals\n",
    "mask_90 = df_c.isin(conf_int_to_drop)\n",
    "mask_90_pp = df_c_pp.isin(conf_int_to_drop)\n",
    "mask_90_adf = df_c_adf.isin(conf_int_to_drop)\n",
    "\n",
    "# get col names\n",
    "cols= mask_90.any()\n",
    "cols_pp = mask_90_pp.any()\n",
    "cols_adf= mask_90_adf.any()\n",
    "columns = list(cols[cols == True].index)\n",
    "columns_pp = list(cols_pp[cols_pp == True].index)\n",
    "columns_adf = list(cols_adf[cols_adf == True].index)\n",
    "\n",
    "drop_pairs_list= []\n",
    "\n",
    "# get rows\n",
    "for col in columns:\n",
    "    rows = list(mask_90[col][mask_90[col] == True].index)\n",
    "    for row in rows:\n",
    "        drop_pairs_list.append((row, col))\n",
    "\n",
    "for col in columns_adf:\n",
    "    rows = list(mask_90_adf[col][mask_90_adf[col] == True].index)\n",
    "    for row in rows:\n",
    "        drop_pairs_list.append((row, col))\n",
    "\n",
    "for col in columns_pp:\n",
    "    rows = list(mask_90_pp[col][mask_90_pp[col] == True].index)\n",
    "    for row in rows:\n",
    "        drop_pairs_list.append((row, col))\n",
    "        \n",
    "print(\"dropped pairs:\")\n",
    "drop_pairs_list = list(set(drop_pairs_list))\n",
    "print(set(drop_pairs_list))\n",
    "\n",
    "new_pairs_list = list(set(pairs_list) - set(drop_pairs_list))\n",
    "\n",
    "print(f\"remaining {len(new_pairs_list)} pairs from {df_c.count().sum()}\")\n",
    "list(new_pairs_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "cc417444",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:35:43.860910Z",
     "start_time": "2022-11-01T17:35:43.801910Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "selected: \n",
      "EQUATION: STT=3.23*RF\n",
      "EQUATION: C=0.89*APO\n",
      "EQUATION: STT=2.03*BAC\n",
      "EQUATION: STT=1.26*APO\n",
      "EQUATION: USB=0.48*BX\n",
      "EQUATION: SCHW=3.34*RF\n",
      "EQUATION: BEN=0.46*APO\n",
      "EQUATION: USB=0.43*COF\n",
      "dropped: \n",
      "EQUATION: COF=1.1*BX\n",
      "EQUATION: KKR=1.98*BEN\n"
     ]
    }
   ],
   "source": [
    "pairs_final ={}\n",
    "dropped_final ={}\n",
    "\n",
    "print(\"selected: \")\n",
    "for i in range(len(new_pairs_list)):\n",
    "    # print pair equation\n",
    "    pairs_final[new_pairs_list[i]] = hedges[new_pairs_list[i]]\n",
    "    print(f\"EQUATION: {new_pairs_list[i][1]}={hedges[new_pairs_list[i]]}*{new_pairs_list[i][0]}\")\n",
    "\n",
    "print(\"dropped: \")\n",
    "for i in range(len(drop_pairs_list)):\n",
    "    dropped_final[drop_pairs_list[i]] = hedges[drop_pairs_list[i]]\n",
    "    print(f\"EQUATION: {drop_pairs_list[i][1]}={hedges[drop_pairs_list[i]]}*{drop_pairs_list[i][0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec737630",
   "metadata": {},
   "source": [
    "Save to file to be used for the next step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "d81d6f8e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:35:44.016443Z",
     "start_time": "2022-11-01T17:35:43.919912Z"
    }
   },
   "outputs": [],
   "source": [
    "with open('pairs_final.pkl', 'wb') as f:\n",
    "    pickle.dump(pairs_final, f)\n",
    "with open('pairs_dropped.pkl', 'wb') as f:\n",
    "    pickle.dump(dropped_final, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5733f72b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-26T20:47:44.014336Z",
     "start_time": "2022-10-26T20:47:44.001333Z"
    }
   },
   "source": [
    "#### Exercise\n",
    "Drop if a pair has more than one %90 conf. out of 3 tests; i.e 2 or more %95 or %99 remains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "e5d347df",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:35:44.185670Z",
     "start_time": "2022-11-01T17:35:44.031674Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dropped pairs:\n",
      "set()\n",
      "remaining 10 pairs from 10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('COF', 'USB'),\n",
       " ('BEN', 'KKR'),\n",
       " ('RF', 'STT'),\n",
       " ('APO', 'C'),\n",
       " ('BX', 'COF'),\n",
       " ('APO', 'STT'),\n",
       " ('BX', 'USB'),\n",
       " ('RF', 'SCHW'),\n",
       " ('APO', 'BEN'),\n",
       " ('BAC', 'STT')]"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for %90 conf intervals\n",
    "mask_90 = df_c.isin([0.90])\n",
    "mask_90_pp = df_c_pp.isin([0.90])\n",
    "mask_90_adf = df_c_adf.isin([0.90])\n",
    "\n",
    "# get col names\n",
    "cols= mask_90.any()\n",
    "cols_pp = mask_90_pp.any()\n",
    "cols_adf= mask_90_adf.any()\n",
    "columns = list(cols[cols == True].index)\n",
    "columns_pp = list(cols_pp[cols_pp == True].index)\n",
    "columns_adf = list(cols_adf[cols_adf == True].index)\n",
    "\n",
    "drop_pairs_1= []\n",
    "drop_pairs_2= []\n",
    "drop_pairs_3= []\n",
    "\n",
    "# get rows\n",
    "for col in columns:\n",
    "    rows = list(mask_90[col][mask_90[col] == True].index)\n",
    "    for row in rows:\n",
    "        drop_pairs_1.append((row, col))\n",
    "\n",
    "for col in columns_adf:\n",
    "    rows = list(mask_90_adf[col][mask_90_adf[col] == True].index)\n",
    "    for row in rows:\n",
    "        drop_pairs_2.append((row, col))\n",
    "\n",
    "for col in columns_pp:\n",
    "    rows = list(mask_90_pp[col][mask_90_pp[col] == True].index)\n",
    "    for row in rows:\n",
    "        drop_pairs_3.append((row, col))\n",
    "        \n",
    "drop_set = (set(drop_pairs_1) & set(drop_pairs_2)) | (set(drop_pairs_1) & set(drop_pairs_3)) | (set(drop_pairs_2) & set(drop_pairs_3))\n",
    "                                                                                                  \n",
    "print(\"dropped pairs:\")\n",
    "print(set(drop_set))\n",
    "\n",
    "new_pairs_list = set(pairs_list) - set(drop_set)\n",
    "\n",
    "print(f\"remaining {len(new_pairs_list)} pairs from {df_c.count().sum()}\")\n",
    "list(new_pairs_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e4cf716",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-26T20:47:44.091334Z",
     "start_time": "2022-10-26T20:47:44.086334Z"
    }
   },
   "source": [
    "#### Exercise\n",
    "Drop based on conf. int. difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "5f9536d0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:35:44.735171Z",
     "start_time": "2022-11-01T17:35:44.196668Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_3d60e_row0_col0, #T_3d60e_row0_col1, #T_3d60e_row0_col2, #T_3d60e_row0_col3, #T_3d60e_row0_col4, #T_3d60e_row0_col5, #T_3d60e_row0_col6, #T_3d60e_row1_col0, #T_3d60e_row1_col1, #T_3d60e_row1_col2, #T_3d60e_row1_col3, #T_3d60e_row1_col4, #T_3d60e_row1_col5, #T_3d60e_row1_col6, #T_3d60e_row2_col0, #T_3d60e_row2_col1, #T_3d60e_row2_col2, #T_3d60e_row2_col3, #T_3d60e_row2_col4, #T_3d60e_row2_col5, #T_3d60e_row2_col6, #T_3d60e_row3_col0, #T_3d60e_row3_col1, #T_3d60e_row3_col2, #T_3d60e_row3_col3, #T_3d60e_row3_col4, #T_3d60e_row3_col5, #T_3d60e_row4_col0, #T_3d60e_row4_col1, #T_3d60e_row4_col2, #T_3d60e_row4_col3, #T_3d60e_row4_col4, #T_3d60e_row4_col5, #T_3d60e_row4_col6, #T_3d60e_row5_col0, #T_3d60e_row5_col1, #T_3d60e_row5_col2, #T_3d60e_row5_col3, #T_3d60e_row5_col4, #T_3d60e_row5_col5, #T_3d60e_row5_col6 {\n",
       "  color: white;\n",
       "}\n",
       "#T_3d60e_row3_col6 {\n",
       "  background-color: green;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_3d60e_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >BEN</th>\n",
       "      <th class=\"col_heading level0 col1\" >C</th>\n",
       "      <th class=\"col_heading level0 col2\" >COF</th>\n",
       "      <th class=\"col_heading level0 col3\" >KKR</th>\n",
       "      <th class=\"col_heading level0 col4\" >SCHW</th>\n",
       "      <th class=\"col_heading level0 col5\" >STT</th>\n",
       "      <th class=\"col_heading level0 col6\" >USB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_3d60e_level0_row0\" class=\"row_heading level0 row0\" >APO</th>\n",
       "      <td id=\"T_3d60e_row0_col0\" class=\"data row0 col0\" >nan</td>\n",
       "      <td id=\"T_3d60e_row0_col1\" class=\"data row0 col1\" >nan</td>\n",
       "      <td id=\"T_3d60e_row0_col2\" class=\"data row0 col2\" >nan</td>\n",
       "      <td id=\"T_3d60e_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_3d60e_row0_col4\" class=\"data row0 col4\" >nan</td>\n",
       "      <td id=\"T_3d60e_row0_col5\" class=\"data row0 col5\" >nan</td>\n",
       "      <td id=\"T_3d60e_row0_col6\" class=\"data row0 col6\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3d60e_level0_row1\" class=\"row_heading level0 row1\" >BAC</th>\n",
       "      <td id=\"T_3d60e_row1_col0\" class=\"data row1 col0\" >nan</td>\n",
       "      <td id=\"T_3d60e_row1_col1\" class=\"data row1 col1\" >nan</td>\n",
       "      <td id=\"T_3d60e_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
       "      <td id=\"T_3d60e_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_3d60e_row1_col4\" class=\"data row1 col4\" >nan</td>\n",
       "      <td id=\"T_3d60e_row1_col5\" class=\"data row1 col5\" >nan</td>\n",
       "      <td id=\"T_3d60e_row1_col6\" class=\"data row1 col6\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3d60e_level0_row2\" class=\"row_heading level0 row2\" >BEN</th>\n",
       "      <td id=\"T_3d60e_row2_col0\" class=\"data row2 col0\" >nan</td>\n",
       "      <td id=\"T_3d60e_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
       "      <td id=\"T_3d60e_row2_col2\" class=\"data row2 col2\" >nan</td>\n",
       "      <td id=\"T_3d60e_row2_col3\" class=\"data row2 col3\" >nan</td>\n",
       "      <td id=\"T_3d60e_row2_col4\" class=\"data row2 col4\" >nan</td>\n",
       "      <td id=\"T_3d60e_row2_col5\" class=\"data row2 col5\" >nan</td>\n",
       "      <td id=\"T_3d60e_row2_col6\" class=\"data row2 col6\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3d60e_level0_row3\" class=\"row_heading level0 row3\" >BX</th>\n",
       "      <td id=\"T_3d60e_row3_col0\" class=\"data row3 col0\" >nan</td>\n",
       "      <td id=\"T_3d60e_row3_col1\" class=\"data row3 col1\" >nan</td>\n",
       "      <td id=\"T_3d60e_row3_col2\" class=\"data row3 col2\" >nan</td>\n",
       "      <td id=\"T_3d60e_row3_col3\" class=\"data row3 col3\" >nan</td>\n",
       "      <td id=\"T_3d60e_row3_col4\" class=\"data row3 col4\" >nan</td>\n",
       "      <td id=\"T_3d60e_row3_col5\" class=\"data row3 col5\" >nan</td>\n",
       "      <td id=\"T_3d60e_row3_col6\" class=\"data row3 col6\" >0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3d60e_level0_row4\" class=\"row_heading level0 row4\" >COF</th>\n",
       "      <td id=\"T_3d60e_row4_col0\" class=\"data row4 col0\" >nan</td>\n",
       "      <td id=\"T_3d60e_row4_col1\" class=\"data row4 col1\" >nan</td>\n",
       "      <td id=\"T_3d60e_row4_col2\" class=\"data row4 col2\" >nan</td>\n",
       "      <td id=\"T_3d60e_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_3d60e_row4_col4\" class=\"data row4 col4\" >nan</td>\n",
       "      <td id=\"T_3d60e_row4_col5\" class=\"data row4 col5\" >nan</td>\n",
       "      <td id=\"T_3d60e_row4_col6\" class=\"data row4 col6\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3d60e_level0_row5\" class=\"row_heading level0 row5\" >RF</th>\n",
       "      <td id=\"T_3d60e_row5_col0\" class=\"data row5 col0\" >nan</td>\n",
       "      <td id=\"T_3d60e_row5_col1\" class=\"data row5 col1\" >nan</td>\n",
       "      <td id=\"T_3d60e_row5_col2\" class=\"data row5 col2\" >nan</td>\n",
       "      <td id=\"T_3d60e_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_3d60e_row5_col4\" class=\"data row5 col4\" >nan</td>\n",
       "      <td id=\"T_3d60e_row5_col5\" class=\"data row5 col5\" >nan</td>\n",
       "      <td id=\"T_3d60e_row5_col6\" class=\"data row5 col6\" >nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x158e9b72100>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_f00a6_row0_col0, #T_f00a6_row0_col1, #T_f00a6_row0_col2, #T_f00a6_row0_col3, #T_f00a6_row0_col4, #T_f00a6_row0_col5, #T_f00a6_row0_col6, #T_f00a6_row1_col0, #T_f00a6_row1_col1, #T_f00a6_row1_col2, #T_f00a6_row1_col3, #T_f00a6_row1_col4, #T_f00a6_row1_col5, #T_f00a6_row1_col6, #T_f00a6_row2_col0, #T_f00a6_row2_col1, #T_f00a6_row2_col2, #T_f00a6_row2_col4, #T_f00a6_row2_col5, #T_f00a6_row2_col6, #T_f00a6_row3_col0, #T_f00a6_row3_col1, #T_f00a6_row3_col3, #T_f00a6_row3_col4, #T_f00a6_row3_col5, #T_f00a6_row3_col6, #T_f00a6_row4_col0, #T_f00a6_row4_col1, #T_f00a6_row4_col2, #T_f00a6_row4_col3, #T_f00a6_row4_col4, #T_f00a6_row4_col5, #T_f00a6_row4_col6, #T_f00a6_row5_col0, #T_f00a6_row5_col1, #T_f00a6_row5_col2, #T_f00a6_row5_col3, #T_f00a6_row5_col4, #T_f00a6_row5_col5, #T_f00a6_row5_col6 {\n",
       "  color: white;\n",
       "}\n",
       "#T_f00a6_row2_col3, #T_f00a6_row3_col2 {\n",
       "  color: white;\n",
       "  background-color: yellow;\n",
       "  color: black;\n",
       "  background-color: red;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_f00a6_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >BEN</th>\n",
       "      <th class=\"col_heading level0 col1\" >C</th>\n",
       "      <th class=\"col_heading level0 col2\" >COF</th>\n",
       "      <th class=\"col_heading level0 col3\" >KKR</th>\n",
       "      <th class=\"col_heading level0 col4\" >SCHW</th>\n",
       "      <th class=\"col_heading level0 col5\" >STT</th>\n",
       "      <th class=\"col_heading level0 col6\" >USB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f00a6_level0_row0\" class=\"row_heading level0 row0\" >APO</th>\n",
       "      <td id=\"T_f00a6_row0_col0\" class=\"data row0 col0\" >nan</td>\n",
       "      <td id=\"T_f00a6_row0_col1\" class=\"data row0 col1\" >nan</td>\n",
       "      <td id=\"T_f00a6_row0_col2\" class=\"data row0 col2\" >nan</td>\n",
       "      <td id=\"T_f00a6_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_f00a6_row0_col4\" class=\"data row0 col4\" >nan</td>\n",
       "      <td id=\"T_f00a6_row0_col5\" class=\"data row0 col5\" >nan</td>\n",
       "      <td id=\"T_f00a6_row0_col6\" class=\"data row0 col6\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f00a6_level0_row1\" class=\"row_heading level0 row1\" >BAC</th>\n",
       "      <td id=\"T_f00a6_row1_col0\" class=\"data row1 col0\" >nan</td>\n",
       "      <td id=\"T_f00a6_row1_col1\" class=\"data row1 col1\" >nan</td>\n",
       "      <td id=\"T_f00a6_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
       "      <td id=\"T_f00a6_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_f00a6_row1_col4\" class=\"data row1 col4\" >nan</td>\n",
       "      <td id=\"T_f00a6_row1_col5\" class=\"data row1 col5\" >nan</td>\n",
       "      <td id=\"T_f00a6_row1_col6\" class=\"data row1 col6\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f00a6_level0_row2\" class=\"row_heading level0 row2\" >BEN</th>\n",
       "      <td id=\"T_f00a6_row2_col0\" class=\"data row2 col0\" >nan</td>\n",
       "      <td id=\"T_f00a6_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
       "      <td id=\"T_f00a6_row2_col2\" class=\"data row2 col2\" >nan</td>\n",
       "      <td id=\"T_f00a6_row2_col3\" class=\"data row2 col3\" >-0.09</td>\n",
       "      <td id=\"T_f00a6_row2_col4\" class=\"data row2 col4\" >nan</td>\n",
       "      <td id=\"T_f00a6_row2_col5\" class=\"data row2 col5\" >nan</td>\n",
       "      <td id=\"T_f00a6_row2_col6\" class=\"data row2 col6\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f00a6_level0_row3\" class=\"row_heading level0 row3\" >BX</th>\n",
       "      <td id=\"T_f00a6_row3_col0\" class=\"data row3 col0\" >nan</td>\n",
       "      <td id=\"T_f00a6_row3_col1\" class=\"data row3 col1\" >nan</td>\n",
       "      <td id=\"T_f00a6_row3_col2\" class=\"data row3 col2\" >-0.05</td>\n",
       "      <td id=\"T_f00a6_row3_col3\" class=\"data row3 col3\" >nan</td>\n",
       "      <td id=\"T_f00a6_row3_col4\" class=\"data row3 col4\" >nan</td>\n",
       "      <td id=\"T_f00a6_row3_col5\" class=\"data row3 col5\" >nan</td>\n",
       "      <td id=\"T_f00a6_row3_col6\" class=\"data row3 col6\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f00a6_level0_row4\" class=\"row_heading level0 row4\" >COF</th>\n",
       "      <td id=\"T_f00a6_row4_col0\" class=\"data row4 col0\" >nan</td>\n",
       "      <td id=\"T_f00a6_row4_col1\" class=\"data row4 col1\" >nan</td>\n",
       "      <td id=\"T_f00a6_row4_col2\" class=\"data row4 col2\" >nan</td>\n",
       "      <td id=\"T_f00a6_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_f00a6_row4_col4\" class=\"data row4 col4\" >nan</td>\n",
       "      <td id=\"T_f00a6_row4_col5\" class=\"data row4 col5\" >nan</td>\n",
       "      <td id=\"T_f00a6_row4_col6\" class=\"data row4 col6\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f00a6_level0_row5\" class=\"row_heading level0 row5\" >RF</th>\n",
       "      <td id=\"T_f00a6_row5_col0\" class=\"data row5 col0\" >nan</td>\n",
       "      <td id=\"T_f00a6_row5_col1\" class=\"data row5 col1\" >nan</td>\n",
       "      <td id=\"T_f00a6_row5_col2\" class=\"data row5 col2\" >nan</td>\n",
       "      <td id=\"T_f00a6_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_f00a6_row5_col4\" class=\"data row5 col4\" >nan</td>\n",
       "      <td id=\"T_f00a6_row5_col5\" class=\"data row5 col5\" >nan</td>\n",
       "      <td id=\"T_f00a6_row5_col6\" class=\"data row5 col6\" >nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x158e90c40a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dropped pairs:\n",
      "[('BEN', 'KKR'), ('BX', 'COF')]\n",
      "remaining 8 pairs from 10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('RF', 'STT'),\n",
       " ('APO', 'C'),\n",
       " ('BAC', 'STT'),\n",
       " ('APO', 'STT'),\n",
       " ('BX', 'USB'),\n",
       " ('RF', 'SCHW'),\n",
       " ('APO', 'BEN'),\n",
       " ('COF', 'USB')]"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's eliminate the pairs if any of its test result drops to %90\n",
    "\n",
    "# keep green from %95 to %99\n",
    "def green_apply(x):\n",
    "    color = {True: \"background-color: green\", False: \"color : white\"}\n",
    "    is_color = (x > 0).replace(color)\n",
    "    return is_color\n",
    "# keep yellow (from %99 to %95)\n",
    "def yellow_apply(x):\n",
    "    color = {True: \"background-color: yellow; color : black\"}\n",
    "    is_color = (x <= -0.04).replace(color)\n",
    "    return is_color\n",
    "# drop red (from %99-%95 to %90)\n",
    "def red_apply(x):\n",
    "    color = {True: \"background-color: red\"}\n",
    "    is_color = (x <= -0.05).replace(color)\n",
    "    return is_color\n",
    "\n",
    "df_diff_adf = df_c_adf.subtract(df_c).round(2)\n",
    "df_diff_adf = df_diff_adf.replace(0, np.nan)\n",
    "\n",
    "df_diff_pp = df_c_pp.subtract(df_c).round(2)\n",
    "df_diff_pp = df_diff_pp.replace(0, np.nan)\n",
    "\n",
    "display(df_diff_adf.style.format(precision=2)\\\n",
    "        .apply(green_apply)\\\n",
    "        .apply(yellow_apply)\\\n",
    "        .apply(red_apply))\n",
    "\n",
    "display(df_diff_pp.style.format(precision=2)\\\n",
    "        .apply(green_apply)\\\n",
    "        .apply(yellow_apply)\\\n",
    "        .apply(red_apply))\n",
    "\n",
    "x_list = df_c.index.tolist()\n",
    "y_list = df_c.columns.tolist()\n",
    "\n",
    "drop_pairs_list = []\n",
    "\n",
    "for i in range(len(x_list)):\n",
    "    for j in range(len(y_list)):\n",
    "        # print(x_list[i] + \"-\" + y_list[j])\n",
    "        if df_diff_adf.iloc[i,j]< -0.04:  # eliminate id drops to %90\n",
    "            drop_pairs_list.append((x_list[i], y_list[j]))\n",
    "\n",
    "for i in range(len(x_list)):\n",
    "    for j in range(len(y_list)):\n",
    "        if df_diff_pp.iloc[i,j]< -0.04:  # eliminate id drops to %90\n",
    "            drop_pairs_list.append((x_list[i], y_list[j]))\n",
    "                      \n",
    "print(\"dropped pairs:\")\n",
    "print(drop_pairs_list)\n",
    "\n",
    "new_pairs_list = set(pairs_list) - set(drop_pairs_list)\n",
    "\n",
    "print(f\"remaining {len(new_pairs_list)} pairs from {df_c.count().sum()}\")\n",
    "list(new_pairs_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e1d91f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-26T20:47:44.359333Z",
     "start_time": "2022-10-26T20:47:44.348356Z"
    }
   },
   "source": [
    "#### Exercise\n",
    "Show unique tickers in the list of selected pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "7c55071a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:35:44.794169Z",
     "start_time": "2022-11-01T17:35:44.770150Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 10 tickers\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'APO', 'BAC', 'BEN', 'BX', 'C', 'COF', 'RF', 'SCHW', 'STT', 'USB'}"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import itertools\n",
    "chain = itertools.chain(*new_pairs_list)\n",
    "new_tickers = set(chain)\n",
    "print(f\"total {len(set(new_tickers))} tickers\")\n",
    "new_tickers"
   ]
  }
 ],
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