code/analysis/semitables.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "weighted-drove",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob\n",
    "import pyarrow as pa\n",
    "import pyarrow.parquet as pq\n",
    "import pyarrow.dataset as ds\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "from tqdm.auto import tqdm\n",
    "from multiprocessing import Pool\n",
    "from stargazer.stargazer import Stargazer\n",
    "from adjustText import adjust_text\n",
    "import ipywidgets as widgets\n",
    "import itertools\n",
    "\n",
    "sns.set_context(\"paper\", font_scale=1.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elder-november",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "alike-handbook",
   "metadata": {},
   "outputs": [],
   "source": [
    "## All data\n",
    "data_df = pd.read_feather('../../data/proc/all_hf.feather')\n",
    "port_df = pd.read_feather('../../data/proc/port_hf.feather')\n",
    "daily_data_df = pd.read_feather('../../data/proc/all_daily.feather')\n",
    "daily_port_df = pd.read_feather('../../data/proc/portfolios_daily.feather')\n",
    "cluster_class_df = pd.read_feather('../../data/proc/cluster_classifications.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "continuing-crawford",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Add additional info\n",
    "data_df['return_pos'] = data_df['return']*(data_df['return'] > 0)\n",
    "data_df['return_neg'] = data_df['return']*(data_df['return'] < 0)\n",
    "port_df['return_pos'] = port_df['return']*(port_df['return'] > 0)\n",
    "port_df['return_neg'] = port_df['return']*(port_df['return'] < 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "national-brazilian",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Realized Semicov/corrs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "potential-criticism",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Prep Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dedicated-occupation",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "danish-links",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_cov_measures(data_df, permno_1, permno_2):\n",
    "\n",
    "    merge_df = data_df.query('permno == @permno_1')[['datetime', 'return', 'return_pos', 'return_neg']].merge(\n",
    "        data_df.query('permno == @permno_2')[['datetime', 'return', 'return_pos', 'return_neg']],\n",
    "        how = 'inner', on = 'datetime'\n",
    "    )\n",
    "\n",
    "    # Add intermediate columns to speed up processing\n",
    "    merge_df['return_x_times_y'] = merge_df['return_x']*merge_df['return_y']\n",
    "    merge_df['return_x_2'] = np.square(merge_df['return_x'])\n",
    "    merge_df['return_pos_x_2'] = np.square(merge_df['return_pos_x'])\n",
    "    merge_df['return_neg_x_2'] = np.square(merge_df['return_neg_x'])\n",
    "    merge_df['return_y_2'] = np.square(merge_df['return_y'])\n",
    "    merge_df['return_pos_y_2'] = np.square(merge_df['return_pos_y'])\n",
    "    merge_df['return_neg_y_2'] = np.square(merge_df['return_neg_y'])\n",
    "    merge_df['return_pos_x_times_pos_y'] = merge_df['return_pos_x']*merge_df['return_pos_y']\n",
    "    merge_df['return_pos_x_times_neg_y'] = merge_df['return_pos_x']*merge_df['return_neg_y']\n",
    "    merge_df['return_neg_x_times_pos_y'] = merge_df['return_neg_x']*merge_df['return_pos_y']\n",
    "    merge_df['return_neg_x_times_neg_y'] = merge_df['return_neg_x']*merge_df['return_neg_y']\n",
    "\n",
    "\n",
    "    # Get daily results\n",
    "    daily_merge_df = (\n",
    "        merge_df.groupby(pd.Grouper(key = 'datetime', freq = '1d'), observed = True)\n",
    "        .sum()\n",
    "        .reset_index()\n",
    "    )\n",
    "    daily_merge_df.rename(\n",
    "        columns = {'return_x_times_y': 'real_cov', 'return_pos_x_times_pos_y': 'real_cov_pp', 'return_pos_x_times_neg_y': 'real_cov_pn',\n",
    "                   'return_neg_x_times_pos_y': 'real_cov_np', 'return_neg_x_times_neg_y': 'real_cov_nn',\n",
    "                   'return_x_2': 'real_var_x', 'return_pos_x_2': 'real_var_pos_x', 'return_neg_x_2': 'real_var_neg_x',\n",
    "                   'return_y_2': 'real_var_y', 'return_pos_y_2': 'real_var_pos_y', 'return_neg_y_2': 'real_var_neg_y',}, \n",
    "        inplace = True\n",
    "    )\n",
    "\n",
    "    # Add realized values\n",
    "    daily_merge_df['real_cor'] =  daily_merge_df['real_cov']/np.sqrt(daily_merge_df['real_var_x']*daily_merge_df['real_var_y'])\n",
    "    daily_merge_df['real_cor_pp'] =  daily_merge_df['real_cov_pp']/np.sqrt(daily_merge_df['real_var_x']*daily_merge_df['real_var_y'])\n",
    "    daily_merge_df['real_cor_pn'] =  daily_merge_df['real_cov_pn']/np.sqrt(daily_merge_df['real_var_x']*daily_merge_df['real_var_y'])\n",
    "    daily_merge_df['real_cor_np'] =  daily_merge_df['real_cov_np']/np.sqrt(daily_merge_df['real_var_x']*daily_merge_df['real_var_y'])\n",
    "    daily_merge_df['real_cor_nn'] =  daily_merge_df['real_cov_nn']/np.sqrt(daily_merge_df['real_var_x']*daily_merge_df['real_var_y'])\n",
    "    daily_merge_df['real_cor_n']  =  daily_merge_df['real_cor_pn'] + daily_merge_df['real_cor_np']\n",
    "    daily_merge_df['real_beta']    =  daily_merge_df['real_cov']/daily_merge_df['real_var_y']\n",
    "    daily_merge_df['real_beta_pp'] =  daily_merge_df['real_cov_pp']/daily_merge_df['real_var_y']\n",
    "    daily_merge_df['real_beta_pn'] =  -1*daily_merge_df['real_cov_pn']/daily_merge_df['real_var_y']\n",
    "    daily_merge_df['real_beta_np'] =  -1*daily_merge_df['real_cov_np']/daily_merge_df['real_var_y']\n",
    "    daily_merge_df['real_beta_nn'] =  daily_merge_df['real_cov_nn']/daily_merge_df['real_var_y']\n",
    "    \n",
    "    # Add labels\n",
    "    daily_merge_df['permno_x'] = permno_1\n",
    "    daily_merge_df['permno_y'] = permno_2\n",
    "    \n",
    "    return merge_df, daily_merge_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "conditional-score",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "60de76c55e3746fa82e49d7c06e79ff8",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list of permnos\n",
    "permno_list = list(cluster_class_df['permno'].values) + ['84398']\n",
    "\n",
    "# Running list of dataframes\n",
    "df_list = []\n",
    "\n",
    "# Multiprocess all possible combinations\n",
    "def helper_func(permno_tuple):\n",
    "    merge_df, daily_merge_df = calc_cov_measures(data_df, *permno_tuple)\n",
    "    return daily_merge_df\n",
    "\n",
    "with Pool(12) as p:\n",
    "    for df in tqdm(p.imap_unordered(helper_func, itertools.product(permno_list, permno_list)),\n",
    "                   total = len(list(itertools.product(permno_list, permno_list)))):\n",
    "        df_list.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "understood-rings",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataframe of realized covariance info\n",
    "rc_df = pd.concat(df_list, ignore_index = True)\n",
    "\n",
    "## Add permno info\n",
    "# x\n",
    "temp_df = cluster_class_df.copy()\n",
    "temp_df.columns = [c + '_x' for c in temp_df.columns]\n",
    "rc_df = rc_df.merge(temp_df, how = 'left', on = 'permno_x')\n",
    "# y\n",
    "temp_df = cluster_class_df.copy()\n",
    "temp_df.columns = [c + '_y' for c in temp_df.columns]\n",
    "rc_df = rc_df.merge(temp_df, how = 'left', on = 'permno_y')\n",
    "\n",
    "# Fix SPY case\n",
    "rc_df['ticker_x'] = np.where(rc_df['permno_x'] == \"84398\", 'SPY', rc_df['ticker_x'])\n",
    "rc_df['ticker_y'] = np.where(rc_df['permno_y'] == \"84398\", 'SPY', rc_df['ticker_y'])\n",
    "\n",
    "# Ensure tickers are sorted by class order\n",
    "rc_df['ticker_x'] = pd.Categorical(rc_df['ticker_x'], list(cluster_class_df['ticker'].values) + ['SPY'])\n",
    "rc_df['ticker_y'] = pd.Categorical(rc_df['ticker_y'], list(cluster_class_df['ticker'].values) + ['SPY'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "injured-columbia",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Portfolios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "prime-seminar",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_cov_measures(data_df, permno_1, permno_2):\n",
    "\n",
    "    merge_df = data_df.query('permno == @permno_1')[['datetime', 'return', 'return_pos', 'return_neg']].merge(\n",
    "        data_df.query('permno == @permno_2')[['datetime', 'return', 'return_pos', 'return_neg']],\n",
    "        how = 'inner', on = 'datetime'\n",
    "    )\n",
    "\n",
    "    # Add intermediate columns to speed up processing\n",
    "    merge_df['return_x_times_y'] = merge_df['return_x']*merge_df['return_y']\n",
    "    merge_df['return_x_2'] = np.square(merge_df['return_x'])\n",
    "    merge_df['return_pos_x_2'] = np.square(merge_df['return_pos_x'])\n",
    "    merge_df['return_neg_x_2'] = np.square(merge_df['return_neg_x'])\n",
    "    merge_df['return_y_2'] = np.square(merge_df['return_y'])\n",
    "    merge_df['return_pos_y_2'] = np.square(merge_df['return_pos_y'])\n",
    "    merge_df['return_neg_y_2'] = np.square(merge_df['return_neg_y'])\n",
    "    merge_df['return_pos_x_times_pos_y'] = merge_df['return_pos_x']*merge_df['return_pos_y']\n",
    "    merge_df['return_pos_x_times_neg_y'] = merge_df['return_pos_x']*merge_df['return_neg_y']\n",
    "    merge_df['return_neg_x_times_pos_y'] = merge_df['return_neg_x']*merge_df['return_pos_y']\n",
    "    merge_df['return_neg_x_times_neg_y'] = merge_df['return_neg_x']*merge_df['return_neg_y']\n",
    "\n",
    "\n",
    "    # Get daily results\n",
    "    daily_merge_df = (\n",
    "        merge_df.groupby(pd.Grouper(key = 'datetime', freq = '1d'), observed = True)\n",
    "        .sum()\n",
    "        .reset_index()\n",
    "    )\n",
    "    daily_merge_df.rename(\n",
    "        columns = {'return_x_times_y': 'real_cov', 'return_pos_x_times_pos_y': 'real_cov_pp', 'return_pos_x_times_neg_y': 'real_cov_pn',\n",
    "                   'return_neg_x_times_pos_y': 'real_cov_np', 'return_neg_x_times_neg_y': 'real_cov_nn',\n",
    "                   'return_x_2': 'real_var_x', 'return_pos_x_2': 'real_var_pos_x', 'return_neg_x_2': 'real_var_neg_x',\n",
    "                   'return_y_2': 'real_var_y', 'return_pos_y_2': 'real_var_pos_y', 'return_neg_y_2': 'real_var_neg_y',}, \n",
    "        inplace = True\n",
    "    )\n",
    "\n",
    "    # Add realized values\n",
    "    daily_merge_df['real_cor'] =  daily_merge_df['real_cov']/np.sqrt(daily_merge_df['real_var_x']*daily_merge_df['real_var_y'])\n",
    "    daily_merge_df['real_cor_pp'] =  daily_merge_df['real_cov_pp']/np.sqrt(daily_merge_df['real_var_x']*daily_merge_df['real_var_y'])\n",
    "    daily_merge_df['real_cor_pn'] =  daily_merge_df['real_cov_pn']/np.sqrt(daily_merge_df['real_var_x']*daily_merge_df['real_var_y'])\n",
    "    daily_merge_df['real_cor_np'] =  daily_merge_df['real_cov_np']/np.sqrt(daily_merge_df['real_var_x']*daily_merge_df['real_var_y'])\n",
    "    daily_merge_df['real_cor_nn'] =  daily_merge_df['real_cov_nn']/np.sqrt(daily_merge_df['real_var_x']*daily_merge_df['real_var_y'])\n",
    "    daily_merge_df['real_cor_n']  =  daily_merge_df['real_cor_pn'] + daily_merge_df['real_cor_np']\n",
    "    daily_merge_df['real_beta']    =  daily_merge_df['real_cov']/daily_merge_df['real_var_y']\n",
    "    daily_merge_df['real_beta_pp'] =  daily_merge_df['real_cov_pp']/daily_merge_df['real_var_y']\n",
    "    daily_merge_df['real_beta_pn'] =  -1*daily_merge_df['real_cov_pn']/daily_merge_df['real_var_y']\n",
    "    daily_merge_df['real_beta_np'] =  -1*daily_merge_df['real_cov_np']/daily_merge_df['real_var_y']\n",
    "    daily_merge_df['real_beta_nn'] =  daily_merge_df['real_cov_nn']/daily_merge_df['real_var_y']\n",
    "    \n",
    "    # Add labels\n",
    "    daily_merge_df['permno_x'] = permno_1\n",
    "    daily_merge_df['permno_y'] = permno_2\n",
    "    \n",
    "    return merge_df, daily_merge_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "initial-cartoon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "72b52d1e12ad4571a37fedb7683e5898",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list of permnos\n",
    "permno_list = list(port_df['permno'].unique()) + ['\"84398\"']\n",
    "\n",
    "# Running list of dataframes\n",
    "df_list = []\n",
    "\n",
    "# Multiprocess all possible combinations\n",
    "def helper_func(permno_tuple):\n",
    "    merge_df, daily_merge_df = calc_cov_measures(port_df, *permno_tuple)\n",
    "    return daily_merge_df\n",
    "\n",
    "with Pool(12) as p:\n",
    "    for df in tqdm(p.imap_unordered(helper_func, itertools.product(permno_list, permno_list)),\n",
    "                   total = len(list(itertools.product(permno_list, permno_list)))):\n",
    "        df_list.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "juvenile-morrison",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataframe of realized covariance info\n",
    "rc_port_df = pd.concat(df_list, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "acknowledged-saturday",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Add ticker info\n",
    "# Fix SPY case\n",
    "rc_port_df['ticker_x'] = np.where(rc_port_df['permno_x'] == \"84398\", 'SPY', rc_port_df['permno_x'])\n",
    "rc_port_df['ticker_y'] = np.where(rc_port_df['permno_y'] == \"84398\", 'SPY', rc_port_df['permno_y'])\n",
    "\n",
    "# Ensure tickers are sorted by class order\n",
    "rc_port_df['ticker_x'] = pd.Categorical(rc_port_df['ticker_x'], ['Good', 'Bad', 'Home', 'FAANG', 'SPY'])\n",
    "rc_port_df['ticker_y'] = pd.Categorical(rc_port_df['ticker_y'], ['Good', 'Bad', 'Home', 'FAANG', 'SPY'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "polished-bradley",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Exhibits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "armed-winner",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "gorgeous-resolution",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_box(ax, x_1, x_2, y_1, y_2, lw = 2.5, color = 'white', ls = 'solid'):\n",
    "    ax.plot([x_1, x_1], [y_1, y_2], lw = lw, color = color, ls = ls, *ax.get_xlim())\n",
    "    ax.plot([x_1, x_2], [y_1, y_1], lw = lw, color = color, ls = ls, *ax.get_xlim())\n",
    "    ax.plot([x_1, x_2], [y_2, y_2], lw = lw, color = color, ls = ls, *ax.get_xlim())\n",
    "    ax.plot([x_2, x_2], [y_1, y_2], lw = lw, color = color, ls = ls, *ax.get_xlim())\n",
    "    \n",
    "def plot_heatmap(rc_df, statistic, date_start, date_end, scale_start, scale_end):\n",
    "    \n",
    "    date_start, date_end = np.min([date_start, date_end]), np.max([date_start, date_end])\n",
    "    \n",
    "    # Params\n",
    "    fig, ax = plt.subplots(figsize = (17,12))\n",
    "    statistic = statistic\n",
    "    plot_data_df = rc_df.loc[rc_df['datetime'].between(str(date_start), str(date_end)), \n",
    "                             ['ticker_x', 'ticker_y', statistic]]\n",
    "#     rc_df.loc[rc_df['datetime'].between(str('20200101'), str('20210101')), \n",
    "#                              ['ticker_x', 'ticker_y', statistic]]\n",
    "    \n",
    "\n",
    "    # Plot\n",
    "    pivot_stat_df = plot_data_df.groupby(['ticker_x', 'ticker_y'])[[statistic]].mean().reset_index().pivot('ticker_x', 'ticker_y', statistic)\n",
    "    sns.heatmap(pivot_stat_df, cmap = 'Spectral', alpha = 0.8, vmin = scale_start, vmax = scale_end, #vmin = -0.5, vmax = 1, \n",
    "               ax = ax, annot=True, square = True, annot_kws={\"fontsize\":10})\n",
    "\n",
    "    # Lines for clusters\n",
    "    plot_box(ax, 0, 5, 0, 5  , color = 'k')\n",
    "    plot_box(ax, 5, 9, 5, 9  , color = 'k')\n",
    "    plot_box(ax, 9, 11, 9, 11, color = 'k')\n",
    "    plot_box(ax, 9, 14, 9, 14, color = 'k', ls = '--')\n",
    "    plot_box(ax, 0, 14, 0, 14, color = 'k', lw = 1)\n",
    "\n",
    "    # Adjustments\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_ylabel('')\n",
    "    ax.set_title(f\"{statistic.replace('_', ' ').title()}: {date_start}-{date_end}\")\n",
    "    \n",
    "    plt.close(fig)\n",
    "    \n",
    "    return fig, ax, pivot_stat_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "invalid-psychology",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_heatmap_grid(rc_df, statistic, date_start, date_end, scale_start, scale_end):\n",
    "\n",
    "    # Dates\n",
    "    date_start, date_end = np.min([date_start, date_end]), np.max([date_start, date_end])\n",
    "    \n",
    "    # Set up plot\n",
    "    fig, axs = plt.subplots(figsize = (20,5), nrows = 1, ncols = 4, \n",
    "                            gridspec_kw = {\"width_ratios\": (1, 1, 1, 0.05), \"wspace\": 0.1})\n",
    "#     gs = axs[1, 2].get_gridspec()\n",
    "#     # remove the underlying axes\n",
    "#     for ax in axs[0:, -1]:\n",
    "#         ax.remove()\n",
    "#     axbig = fig.add_subplot(gs[0:, -1])\n",
    "\n",
    "    # Grid of plots\n",
    "    stats_list = ['real_cor_pp', 'real_cor_n', 'real_cor_nn']\n",
    "    names_list = ['$\\\\mathbf{P}_t$', '$\\\\mathbf{M}_t^+ + \\\\mathbf{M}_t^-$', '$\\\\mathbf{N}_t$']\n",
    "    pivot_stat_dfs = []\n",
    "    for i in range(len(stats_list)):\n",
    "\n",
    "        # Get data\n",
    "        statistic = stats_list[i]\n",
    "        plot_data_df = rc_df.loc[rc_df['datetime'].between(str(date_start), str(date_end)), \n",
    "                                 ['ticker_x', 'ticker_y', statistic]]\n",
    "        pivot_stat_df = plot_data_df.groupby(['ticker_x', 'ticker_y'])[[statistic]].mean().reset_index().pivot('ticker_x', 'ticker_y', statistic)\n",
    "        pivot_stat_dfs.append(pivot_stat_df)\n",
    "\n",
    "        # Plots\n",
    "        ax = axs.reshape(-1)[i]\n",
    "        sns.heatmap(pivot_stat_df, cmap = 'Spectral', alpha = 0.8, vmin = scale_start, vmax = scale_end, #vmin = -0.5, vmax = 1, \n",
    "                   ax = ax, cbar_ax = axs.reshape(-1)[-1], square = True)#, annot = True, annot_kws={\"fontsize\":6})\n",
    "        ax.set_title(f'{names_list[i]}')\n",
    "\n",
    "        # Lines for clusters\n",
    "        plot_box(ax, 0, 5, 0, 5, lw = 1)\n",
    "        plot_box(ax, 5, 9, 5, 9, lw = 1)\n",
    "        plot_box(ax, 9, 11, 9, 11, lw = 1)\n",
    "        plot_box(ax, 9, 14, 9, 14, lw = 1, ls = '--')\n",
    "        plot_box(ax, 0, 14, 0, 14, lw = 1)\n",
    "\n",
    "\n",
    "    # # Adjustments\n",
    "    for ax in axs[:3].reshape(-1):\n",
    "        ax.set_xlabel('')\n",
    "        ax.set_ylabel('')\n",
    "        \n",
    "    plt.close(fig)\n",
    "\n",
    "    return fig, axs, pivot_stat_dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "caring-grass",
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_heatmap_latex(\n",
    "    pivot_stat_df, table_name, output_filename, date_start, date_end, additional_notes=\"\"\n",
    "):\n",
    "    \n",
    "    pivot_stat_df.columns.name = None\n",
    "    pivot_stat_df.index.name = None\n",
    "\n",
    "    latex_header = (r\"\"\"\n",
    "    \\begin{table}[!h] \\centering\n",
    "    \\caption{table_name}\n",
    "    \\small\n",
    "    \\begin{threeparttable}\n",
    "    \\begin{tabular}{@{\\extracolsep{1pt}}l|ccccccccccccccc}\n",
    "    \\toprule\n",
    "    \\toprule\n",
    "    \"\"\"\n",
    "    .replace(\"table_name\", table_name)\n",
    "    )\n",
    "    \n",
    "    latex_footer = (\n",
    "        r\"\"\"\n",
    "    \\bottomrule\n",
    "    \\bottomrule\n",
    "    \\end{tabular}\n",
    "    \\begin{tablenotes}[flushleft]\n",
    "    \\small\n",
    "    \\item\\textit{Note:} The sample consists of 5-minute intradaily returns for each stock from date_start-date_end. additional_notes\n",
    "    \\end{tablenotes}\n",
    "    \\end{threeparttable}\n",
    "    \\end{table}\n",
    "    \"\"\"\n",
    "    .replace(\"date_start\", str(date_start))\n",
    "    .replace(\"additional_notes\", additional_notes)\n",
    "    )\n",
    "    if date_end != date_start:\n",
    "        latex_footer = latex_footer.replace(\"date_end\", str(date_end))\n",
    "    else:\n",
    "        latex_footer = latex_footer.replace(\"-date_end\", '')\n",
    "        \n",
    "\n",
    "    latex_raw_table = pivot_stat_df.round(2).to_latex().split(\"\\n\")\n",
    "    latex_table = latex_header + \" \\n\".join(latex_raw_table[2:-3]) + latex_footer\n",
    "\n",
    "    with open(output_filename, \"+w\") as f:\n",
    "        f.write(latex_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "widespread-october",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "direct-wilderness",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac842132a6174460afe386839703ab93",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='statistic', options=('real_cor', 'real_cor_pp', 'real_cor_pn', 're…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap widget\n",
    "def widget_f(statistic, date_start, date_end, scale_start, scale_end):\n",
    "    fig, _, _ = plot_heatmap(rc_df, statistic, date_start, date_end, scale_start, scale_end)\n",
    "    display(fig)\n",
    "    return\n",
    "\n",
    "widgets.interact(widget_f,\n",
    "                 statistic = ['real_cor', 'real_cor_pp', 'real_cor_pn', 'real_cor_np', 'real_cor_nn'], \n",
    "                 date_start=widgets.IntSlider(min=2002, max=2021, step=1, value=2002),\n",
    "                 date_end=widgets.IntSlider(min=2002, max=2022, step=1, value=2021),\n",
    "                 scale_start=widgets.FloatSlider(min=-1, max=1, step=0.1, value=-0.4),\n",
    "                 scale_end=widgets.FloatSlider(min=-1, max=1, step=0.1, value=0.7),\n",
    "                );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "computational-rally",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Full sample\n",
    "date_start, date_end = 2002, 2021\n",
    "date_filename = 'fullsample'\n",
    "\n",
    "# Main heatmap of correlations\n",
    "fig, ax, pivot_stat_df = plot_heatmap(rc_df, \"real_cor\", date_start, date_end, -0.4, 0.7)\n",
    "ax.set_title(\"\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(f\"../../exhibits/real_corr_heat_{date_filename}.pdf\")\n",
    "format_heatmap_latex(\n",
    "    pivot_stat_df.copy(),\n",
    "    f'Realized Correlations ({date_start}-{date_end-1})',\n",
    "    f\"../../exhibits/real_corr_{date_filename}.tex\",\n",
    "    date_start,\n",
    "    date_end-1,\n",
    "    additional_notes=\"\",\n",
    ")\n",
    "\n",
    "# Grid heatmap of correlations\n",
    "fig, ax, pivot_stat_dfs = plot_heatmap_grid(rc_df, \"real_cor\", date_start, date_end, -0.4, 0.7)\n",
    "fig.savefig(f\"../../exhibits/real_semicorr_heat_{date_filename}.pdf\", bbox_inches = 'tight')\n",
    "stats_list = ['real_cor_pp', 'real_cor_n', 'real_cor_nn']\n",
    "names_list = ['$\\\\mathbf{P}_t$', '$\\\\mathbf{M}_t^+ + \\\\mathbf{M}_t^-$', '$\\\\mathbf{N}_t$']\n",
    "for i in range(len(pivot_stat_dfs)):\n",
    "    format_heatmap_latex(\n",
    "        pivot_stat_dfs[i].copy(),\n",
    "        f'Realized Semiorrelations {names_list[i]} ({date_start}-{date_end-1})',\n",
    "        f\"../../exhibits/real_corr_{stats_list[i].split('_')[-1]}_{date_filename}.tex\",\n",
    "        date_start,\n",
    "        date_end-1,\n",
    "        additional_notes=\"\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "stunning-romantic",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Full sample\n",
    "date_start, date_end = 2020, 2021\n",
    "date_filename = 'just2020'\n",
    "\n",
    "# Main heatmap of correlations\n",
    "fig, ax, pivot_stat_df = plot_heatmap(rc_df, \"real_cor\", date_start, date_end, -0.4, 0.7)\n",
    "ax.set_title(\"\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(f\"../../exhibits/real_corr_heat_{date_filename}.pdf\")\n",
    "format_heatmap_latex(\n",
    "    pivot_stat_df.copy(),\n",
    "    f'Realized Correlations ({date_start})',\n",
    "    f\"../../exhibits/real_corr_{date_filename}.tex\",\n",
    "    date_start,\n",
    "    date_end-1,\n",
    "    additional_notes=\"\",\n",
    ")\n",
    "\n",
    "# Grid heatmap of correlations\n",
    "fig, ax, pivot_stat_dfs = plot_heatmap_grid(rc_df, \"real_cor\", date_start, date_end, -0.4, 0.7)\n",
    "fig.savefig(f\"../../exhibits/real_semicorr_heat_{date_filename}.pdf\", bbox_inches = 'tight')\n",
    "stats_list = ['real_cor_pp', 'real_cor_n', 'real_cor_nn']\n",
    "names_list = ['$\\\\mathbf{P}_t$', '$\\\\mathbf{M}_t^+ + \\\\mathbf{M}_t^-$', '$\\\\mathbf{N}_t$']\n",
    "for i in range(len(pivot_stat_dfs)):\n",
    "    format_heatmap_latex(\n",
    "        pivot_stat_dfs[i].copy(),\n",
    "        f'Realized Semiorrelations {names_list[i]} ({date_start})',\n",
    "        f\"../../exhibits/real_corr_{stats_list[i].split('_')[-1]}_{date_filename}.tex\",\n",
    "        date_start,\n",
    "        date_end-1,\n",
    "        additional_notes=\"\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "vocational-vanilla",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Full sample\n",
    "date_start, date_end = 2019, 2020\n",
    "date_filename = 'just2019'\n",
    "\n",
    "# Main heatmap of correlations\n",
    "fig, ax, pivot_stat_df = plot_heatmap(rc_df, \"real_cor\", date_start, date_end, -0.4, 0.7)\n",
    "ax.set_title(\"\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(f\"../../exhibits/real_corr_heat_{date_filename}.pdf\")\n",
    "format_heatmap_latex(\n",
    "    pivot_stat_df.copy(),\n",
    "    f'Realized Correlations ({date_start})',\n",
    "    f\"../../exhibits/real_corr_{date_filename}.tex\",\n",
    "    date_start,\n",
    "    date_end-1,\n",
    "    additional_notes=\"\",\n",
    ")\n",
    "\n",
    "# Grid heatmap of correlations\n",
    "fig, ax, pivot_stat_dfs = plot_heatmap_grid(rc_df, \"real_cor\", date_start, date_end, -0.4, 0.7)\n",
    "fig.savefig(f\"../../exhibits/real_semicorr_heat_{date_filename}.pdf\", bbox_inches = 'tight')\n",
    "stats_list = ['real_cor_pp', 'real_cor_n', 'real_cor_nn']\n",
    "names_list = ['$\\\\mathbf{P}_t$', '$\\\\mathbf{M}_t^+ + \\\\mathbf{M}_t^-$', '$\\\\mathbf{N}_t$']\n",
    "for i in range(len(pivot_stat_dfs)):\n",
    "    format_heatmap_latex(\n",
    "        pivot_stat_dfs[i].copy(),\n",
    "        f'Realized Semiorrelations {names_list[i]} ({date_start})',\n",
    "        f\"../../exhibits/real_corr_{stats_list[i].split('_')[-1]}_{date_filename}.tex\",\n",
    "        date_start,\n",
    "        date_end-1,\n",
    "        additional_notes=\"\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collect-angel",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Portfolios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "verbal-scene",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Full sample\n",
    "date_start, date_end = 2002, 2021\n",
    "date_filename = 'fullsample'\n",
    "\n",
    "# Main heatmap of correlations\n",
    "fig, ax, pivot_stat_df = plot_heatmap(rc_port_df, \"real_cor\", date_start, date_end, -0.4, 0.7)\n",
    "ax.set_title(\"\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(f\"../../exhibits/real_corr_heat_{date_filename}_port.pdf\")\n",
    "format_heatmap_latex(\n",
    "    pivot_stat_df.copy(),\n",
    "    f'Realized Correlations for Equally-Weighted Portfolios ({date_start}-{date_end-1})',\n",
    "    f\"../../exhibits/real_corr_{date_filename}_port.tex\",\n",
    "    date_start,\n",
    "    date_end-1,\n",
    "    additional_notes=r\"The good, bad, and home portfolios refer to Covid stock clusters from Bollerslev et al. (2021c).\",\n",
    ")\n",
    "\n",
    "# Grid heatmap of correlations\n",
    "fig, ax, pivot_stat_dfs = plot_heatmap_grid(rc_port_df, \"real_cor\", date_start, date_end, -0.4, 0.7)\n",
    "fig.savefig(f\"../../exhibits/real_semicorr_heat_{date_filename}_port.pdf\", bbox_inches = 'tight')\n",
    "stats_list = ['real_cor_pp', 'real_cor_n', 'real_cor_nn']\n",
    "names_list = ['$\\\\mathbf{P}_t$', '$\\\\mathbf{M}_t^+ + \\\\mathbf{M}_t^-$', '$\\\\mathbf{N}_t$']\n",
    "for i in range(len(pivot_stat_dfs)):\n",
    "    format_heatmap_latex(\n",
    "        pivot_stat_dfs[i].copy(),\n",
    "        f'Realized Semiorrelations for Equally-Weighted Portfolios {names_list[i]} ({date_start}-{date_end-1})',\n",
    "        f\"../../exhibits/real_corr_{stats_list[i].split('_')[-1]}_{date_filename}_port.tex\",\n",
    "        date_start,\n",
    "        date_end-1,\n",
    "        additional_notes=r\"The good, bad, and home portfolios refer to Covid stock clusters from Bollerslev et al. (2021c).\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "young-sampling",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Full sample\n",
    "date_start, date_end = 2020, 2021\n",
    "date_filename = 'just2020'\n",
    "\n",
    "# Main heatmap of correlations\n",
    "fig, ax, pivot_stat_df = plot_heatmap(rc_port_df, \"real_cor\", date_start, date_end, -0.4, 0.7)\n",
    "ax.set_title(\"\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(f\"../../exhibits/real_corr_heat_{date_filename}_port.pdf\")\n",
    "format_heatmap_latex(\n",
    "    pivot_stat_df.copy(),\n",
    "    f'Realized Correlations for Equally-Weighted Portfolios ({date_start})',\n",
    "    f\"../../exhibits/real_corr_{date_filename}_port.tex\",\n",
    "    date_start,\n",
    "    date_end-1,\n",
    "    additional_notes=r\"The good, bad, and home portfolios refer to Covid stock clusters from Bollerslev et al. (2021c).\",\n",
    ")\n",
    "\n",
    "# Grid heatmap of correlations\n",
    "fig, ax, pivot_stat_dfs = plot_heatmap_grid(rc_port_df, \"real_cor\", date_start, date_end, -0.4, 0.7)\n",
    "fig.savefig(f\"../../exhibits/real_semicorr_heat_{date_filename}_port.pdf\", bbox_inches = 'tight')\n",
    "stats_list = ['real_cor_pp', 'real_cor_n', 'real_cor_nn']\n",
    "names_list = ['$\\\\mathbf{P}_t$', '$\\\\mathbf{M}_t^+ + \\\\mathbf{M}_t^-$', '$\\\\mathbf{N}_t$']\n",
    "for i in range(len(pivot_stat_dfs)):\n",
    "    format_heatmap_latex(\n",
    "        pivot_stat_dfs[i].copy(),\n",
    "        f'Realized Semiorrelations for Equally-Weighted Portfolios {names_list[i]} ({date_start})',\n",
    "        f\"../../exhibits/real_corr_{stats_list[i].split('_')[-1]}_{date_filename}_port.tex\",\n",
    "        date_start,\n",
    "        date_end-1,\n",
    "        additional_notes=r\"The good, bad, and home portfolios refer to Covid stock clusters from Bollerslev et al. (2021c).\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "excessive-theta",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Full sample\n",
    "date_start, date_end = 2019, 2020\n",
    "date_filename = 'just2019'\n",
    "\n",
    "# Main heatmap of correlations\n",
    "fig, ax, pivot_stat_df = plot_heatmap(rc_port_df, \"real_cor\", date_start, date_end, -0.4, 0.7)\n",
    "ax.set_title(\"\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(f\"../../exhibits/real_corr_heat_{date_filename}_port.pdf\")\n",
    "format_heatmap_latex(\n",
    "    pivot_stat_df.copy(),\n",
    "    f'Realized Correlations for Equally-Weighted Portfolios ({date_start})',\n",
    "    f\"../../exhibits/real_corr_{date_filename}_port.tex\",\n",
    "    date_start,\n",
    "    date_end-1,\n",
    "    additional_notes=r\"The good, bad, and home portfolios refer to Covid stock clusters from Bollerslev et al. (2021c).\",\n",
    ")\n",
    "\n",
    "# Grid heatmap of correlations\n",
    "fig, ax, pivot_stat_dfs = plot_heatmap_grid(rc_port_df, \"real_cor\", date_start, date_end, -0.4, 0.7)\n",
    "fig.savefig(f\"../../exhibits/real_semicorr_heat_{date_filename}_port.pdf\", bbox_inches = 'tight')\n",
    "stats_list = ['real_cor_pp', 'real_cor_n', 'real_cor_nn']\n",
    "names_list = ['$\\\\mathbf{P}_t$', '$\\\\mathbf{M}_t^+ + \\\\mathbf{M}_t^-$', '$\\\\mathbf{N}_t$']\n",
    "for i in range(len(pivot_stat_dfs)):\n",
    "    format_heatmap_latex(\n",
    "        pivot_stat_dfs[i].copy(),\n",
    "        f'Realized Semiorrelations for Equally-Weighted Portfolios {names_list[i]} ({date_start})',\n",
    "        f\"../../exhibits/real_corr_{stats_list[i].split('_')[-1]}_{date_filename}_port.tex\",\n",
    "        date_start,\n",
    "        date_end-1,\n",
    "        additional_notes=r\"The good, bad, and home portfolios refer to Covid stock clusters from Bollerslev et al. (2021c).\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "african-webcam",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Betas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "fleet-system",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_beta_table(rc_df, date_start, date_end, scale_start, scale_end):\n",
    "\n",
    "    # Params\n",
    "#     grid_kws = {\"height_ratios\": (.9, .05), \"hspace\": .3}\n",
    "#     fig, (ax, cbar_ax) = plt.subplots(figsize = (15,7), nrows = 2, gridspec_kw=grid_kws)\n",
    "    fig, ax = plt.subplots(figsize = (17,5))\n",
    "    plot_data_df = rc_df.loc[rc_df[\"datetime\"].between(str(date_start), str(date_end))].query('permno_y == \"84398\"')[\n",
    "        [\"ticker_x\", \"real_beta\", \"real_beta_pp\", \"real_beta_np\", \"real_beta_pn\", \"real_beta_nn\"]\n",
    "    ].groupby(['ticker_x']).mean().query('ticker_x != \"SPY\"')\n",
    "\n",
    "    # Plot\n",
    "    # pivot_stat_df = plot_data_df.pivot('ticker_x', 'ticker_y', statistic)\n",
    "    plot_data_df.columns = ['$\\\\beta$', '$\\\\beta^{\\\\mathbf{P}}$', '$\\\\beta^{\\\\mathbf{M^+}}$', \n",
    "                            '$\\\\beta^{\\\\mathbf{M^-}}$', '$\\\\beta^{\\\\mathbf{N}}$']\n",
    "    sns.heatmap(plot_data_df.T, cmap = 'viridis', alpha = 0.8, vmin = scale_start, vmax = scale_end, \n",
    "               ax = ax, annot=True, square = True, annot_kws={\"fontsize\":10})\n",
    "\n",
    "    # Adjustments\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_ylabel('')\n",
    "    ax.set_title(f\"Realized Betas: {date_start}-{date_end}\")\n",
    "\n",
    "    # Lines for clusters\n",
    "    plot_box(ax, 0, 5, 0, 5)\n",
    "    plot_box(ax, 5, 9, 0, 5)\n",
    "    plot_box(ax, 9, 11, 0, 5)\n",
    "    \n",
    "    # Fix layout\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    return fig, ax, plot_data_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stretch-uncertainty",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "dependent-underground",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "04623b2ed11e4da1bfe9cbbdd1751eff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=2000, description='date_start', max=2021, min=2000), IntSlider(value=202…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap widget\n",
    "# Heatmap widget\n",
    "def widget_f(date_start, date_end, scale_start, scale_end):\n",
    "    return plot_beta_table(rc_df, date_start, date_end, scale_start, scale_end)\n",
    "\n",
    "widgets.interact(widget_f, \n",
    "                 date_start=widgets.IntSlider(min=2000, max=2021, step=1, value=2000),\n",
    "                 date_end=widgets.IntSlider(min=2000, max=2021, step=1, value=20201),\n",
    "                 scale_start=widgets.FloatSlider(min=-3, max=2, step=0.1, value=0),\n",
    "                 scale_end=widgets.FloatSlider(min=-1, max=2, step=0.1, value=1.3),\n",
    "                );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "referenced-trouble",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrrrrrrrrrrrr}\n",
      "\\toprule\n",
      "ticker_x &     BA &    OXY &    RTX &    SLB &    SPG &   BIIB &   BKNG &   CHTR &   GILD &   AMZN &   NFLX &     FB &   AAPL &   GOOG \\\\\n",
      "\\midrule\n",
      "$\\beta$                &  0.979 &  1.010 &  0.904 &  1.074 &  0.782 &  1.079 &  1.003 &  0.677 &  1.039 &  1.312 &  1.253 &  1.307 &  1.240 &  1.148 \\\\\n",
      "$\\beta^{\\mathbf{P}}$   &  0.618 &  0.675 &  0.544 &  0.724 &  0.563 &  0.742 &  0.728 &  0.583 &  0.694 &  0.801 &  0.932 &  0.838 &  0.729 &  0.681 \\\\\n",
      "$\\beta^{\\mathbf{M^+}}$ &  0.119 &  0.172 &  0.086 &  0.186 &  0.168 &  0.197 &  0.226 &  0.243 &  0.176 &  0.135 &  0.309 &  0.189 &  0.105 &  0.099 \\\\\n",
      "$\\beta^{\\mathbf{M^-}}$ &  0.115 &  0.158 &  0.080 &  0.172 &  0.154 &  0.185 &  0.214 &  0.231 &  0.162 &  0.128 &  0.289 &  0.169 &  0.104 &  0.093 \\\\\n",
      "$\\beta^{\\mathbf{N}}$   &  0.595 &  0.664 &  0.525 &  0.709 &  0.540 &  0.719 &  0.716 &  0.568 &  0.683 &  0.774 &  0.919 &  0.827 &  0.720 &  0.659 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 2002-2020\n",
    "# Main heatmap of betas\n",
    "fig, ax, plot_data_df = plot_beta_table(rc_df, 2002, 2021, 0, 1.3)\n",
    "ax.set_title('')\n",
    "plt.tight_layout()\n",
    "plt.savefig('../../exhibits/real_beta_heat_fullsample.pdf')\n",
    "\n",
    "print(plot_data_df.T.round(3).to_latex(escape = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "medium-sleep",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrrrrrrrrrrrr}\n",
      "\\toprule\n",
      "ticker_x &     BA &    OXY &    RTX &    SLB &    SPG &   BIIB &   BKNG &   CHTR &   GILD &   AMZN &   NFLX &     FB &   AAPL &   GOOG \\\\\n",
      "\\midrule\n",
      "$\\beta$                &  1.336 &  1.494 &  1.048 &  1.184 &  1.089 &  0.707 &  0.747 &  0.688 &  0.563 &  1.169 &  1.139 &  1.255 &  1.341 &  1.021 \\\\\n",
      "$\\beta^{\\mathbf{P}}$   &  0.866 &  1.090 &  0.659 &  0.854 &  0.848 &  0.554 &  0.525 &  0.475 &  0.445 &  0.647 &  0.731 &  0.711 &  0.725 &  0.590 \\\\\n",
      "$\\beta^{\\mathbf{M^+}}$ &  0.238 &  0.363 &  0.147 &  0.276 &  0.332 &  0.212 &  0.183 &  0.129 &  0.192 &  0.079 &  0.177 &  0.093 &  0.061 &  0.070 \\\\\n",
      "$\\beta^{\\mathbf{M^-}}$ &  0.204 &  0.328 &  0.129 &  0.246 &  0.276 &  0.166 &  0.150 &  0.120 &  0.147 &  0.070 &  0.155 &  0.084 &  0.063 &  0.074 \\\\\n",
      "$\\beta^{\\mathbf{N}}$   &  0.913 &  1.095 &  0.665 &  0.850 &  0.849 &  0.531 &  0.554 &  0.462 &  0.456 &  0.672 &  0.740 &  0.722 &  0.740 &  0.575 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 2020 Only\n",
    "# Main heatmap of betas\n",
    "fig, ax, plot_data_df = plot_beta_table(rc_df, 2020, 2021, 0, 1.3)\n",
    "ax.set_title('')\n",
    "plt.tight_layout()\n",
    "plt.savefig('../../exhibits/real_beta_heat_just2020.pdf')\n",
    "\n",
    "print(plot_data_df.T.round(3).to_latex(escape = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "japanese-video",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrrrrrrrrrrrr}\n",
      "\\toprule\n",
      "ticker_x &     BA &    OXY &    RTX &    SLB &    SPG &   BIIB &   BKNG &   CHTR &   GILD &   AMZN &   NFLX &     FB &   AAPL &   GOOG \\\\\n",
      "\\midrule\n",
      "$\\beta$                &  0.977 &  0.953 &  1.008 &  1.155 &  0.567 &  1.027 &  0.635 &  0.708 &  0.994 &  1.249 &  1.547 &  1.281 &  1.243 &  1.141 \\\\\n",
      "$\\beta^{\\mathbf{P}}$   &  0.721 &  0.760 &  0.626 &  0.899 &  0.523 &  0.797 &  0.517 &  0.562 &  0.688 &  0.721 &  1.002 &  0.799 &  0.734 &  0.679 \\\\\n",
      "$\\beta^{\\mathbf{M^+}}$ &  0.216 &  0.297 &  0.127 &  0.313 &  0.250 &  0.287 &  0.207 &  0.194 &  0.189 &  0.084 &  0.233 &  0.160 &  0.081 &  0.096 \\\\\n",
      "$\\beta^{\\mathbf{M^-}}$ &  0.205 &  0.261 &  0.102 &  0.296 &  0.221 &  0.262 &  0.179 &  0.186 &  0.176 &  0.076 &  0.225 &  0.147 &  0.108 &  0.091 \\\\\n",
      "$\\beta^{\\mathbf{N}}$   &  0.676 &  0.751 &  0.612 &  0.864 &  0.515 &  0.779 &  0.505 &  0.526 &  0.672 &  0.688 &  1.003 &  0.789 &  0.697 &  0.649 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 2019 Only\n",
    "# Main heatmap of betas\n",
    "fig, ax, plot_data_df = plot_beta_table(rc_df, 2019, 2020, 0, 1.3)\n",
    "ax.set_title('')\n",
    "plt.tight_layout()\n",
    "plt.savefig('../../exhibits/real_beta_heat_just2019.pdf')\n",
    "\n",
    "print(plot_data_df.T.round(3).to_latex(escape = False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "musical-midnight",
   "metadata": {},
   "source": [
    "#### Portfolios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "experienced-billy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrr}\n",
      "\\toprule\n",
      "ticker_x &   Good &    Bad &   Home &  FAANG \\\\\n",
      "\\midrule\n",
      "$\\beta$                &  0.993 &  0.950 &  1.287 &  1.252 \\\\\n",
      "$\\beta^{\\mathbf{P}}$   &  0.590 &  0.527 &  0.797 &  0.710 \\\\\n",
      "$\\beta^{\\mathbf{M^+}}$ &  0.092 &  0.049 &  0.150 &  0.080 \\\\\n",
      "$\\beta^{\\mathbf{M^-}}$ &  0.087 &  0.044 &  0.143 &  0.080 \\\\\n",
      "$\\beta^{\\mathbf{N}}$   &  0.581 &  0.515 &  0.783 &  0.702 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 2002-2020\n",
    "# Main heatmap of betas\n",
    "fig, ax, plot_data_df = plot_beta_table(rc_port_df, 2002, 2021, 0, 1.3)\n",
    "ax.set_title('')\n",
    "plt.tight_layout()\n",
    "plt.savefig('../../exhibits/real_beta_heat_fullsample_port.pdf')\n",
    "\n",
    "print(plot_data_df.T.round(3).to_latex(escape = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "sticky-cursor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrr}\n",
      "\\toprule\n",
      "ticker_x &   Good &    Bad &   Home &  FAANG \\\\\n",
      "\\midrule\n",
      "$\\beta$                &  0.676 &  1.233 &  1.154 &  1.185 \\\\\n",
      "$\\beta^{\\mathbf{P}}$   &  0.397 &  0.740 &  0.653 &  0.622 \\\\\n",
      "$\\beta^{\\mathbf{M^+}}$ &  0.076 &  0.146 &  0.092 &  0.037 \\\\\n",
      "$\\beta^{\\mathbf{M^-}}$ &  0.057 &  0.130 &  0.082 &  0.036 \\\\\n",
      "$\\beta^{\\mathbf{N}}$   &  0.413 &  0.769 &  0.675 &  0.637 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 2020\n",
    "# Main heatmap of betas\n",
    "fig, ax, plot_data_df = plot_beta_table(rc_port_df, 2020, 2021, 0, 1.3)\n",
    "ax.set_title('')\n",
    "plt.tight_layout()\n",
    "plt.savefig('../../exhibits/real_beta_heat_just2020_port.pdf')\n",
    "\n",
    "print(plot_data_df.T.round(3).to_latex(escape = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "ruled-initial",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrr}\n",
      "\\toprule\n",
      "ticker_x &   Good &    Bad &   Home &  FAANG \\\\\n",
      "\\midrule\n",
      "$\\beta$                &  0.841 &  0.932 &  1.398 &  1.292 \\\\\n",
      "$\\beta^{\\mathbf{P}}$   &  0.514 &  0.558 &  0.809 &  0.701 \\\\\n",
      "$\\beta^{\\mathbf{M^+}}$ &  0.093 &  0.093 &  0.106 &  0.045 \\\\\n",
      "$\\beta^{\\mathbf{M^-}}$ &  0.084 &  0.082 &  0.107 &  0.049 \\\\\n",
      "$\\beta^{\\mathbf{N}}$   &  0.504 &  0.549 &  0.802 &  0.685 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 2019\n",
    "# Main heatmap of betas\n",
    "fig, ax, plot_data_df = plot_beta_table(rc_port_df, 2019, 2020, 0, 1.3)\n",
    "ax.set_title('')\n",
    "plt.tight_layout()\n",
    "plt.savefig('../../exhibits/real_beta_heat_just2019_port.pdf')\n",
    "\n",
    "print(plot_data_df.T.round(3).to_latex(escape = False))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}