01-01_sample_sp500_constituents.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sample - SP500 Constituents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*[Source](https://wrds-web.wharton.upenn.edu/wrds/ds/comp/index/constituents.cfm)*\r\n",
    "\r\n",
    "*TIC: i0003 (SP&500)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "NAME = '01-01_sample_sp500_constituents'\r\n",
    "PROJECT = 'conference-calls-sentiment'\r\n",
    "PYTHON_VERSION = '3.7.0'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "workdir = re.sub(\"(?<={})[\\w\\W]*\".format(PROJECT), \"\", os.getcwd())\r\n",
    "os.chdir(workdir)\r\n",
    "\r\n",
    "pipeline = os.path.join('2_pipeline', NAME)\r\n",
    "if not os.path.exists(pipeline):\r\n",
    "    os.makedirs(pipeline)\r\n",
    "    for folder in ['out', 'store', 'tmp']:\r\n",
    "        os.makedirs(os.path.join(pipeline, folder))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Main Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp500_raw = pd.read_csv(os.path.join('0_data', 'sp500_constituents', 'sp500_constituents_2000-2019.csv'))\r\n",
    "sp500_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp500 = (sp500_raw.copy()\r\n",
    "                  .assign(start_date=lambda x: pd.to_datetime(x['from'], format=r'%Y%m%d'),\r\n",
    "                          end_date=lambda x: pd.to_datetime(x['thru'], format=r'%Y%m%d'))\r\n",
    "                  .assign(end_date=lambda x: x['end_date'].fillna(pd.Timestamp('2020-12-31')))  # Missing thru value means firm is still in the S&P500\r\n",
    "                  .sort_values(['gvkey', 'from'])\r\n",
    "                  .reset_index(drop=True)\r\n",
    "                  .rename(columns={'co_conm':'coname',\r\n",
    "                                   'co_tic': 'ticker',\r\n",
    "                                   'co_sic': 'sic',\r\n",
    "                                   'co_cik': 'cik'})\r\n",
    "                  .filter(['gvkey', 'start_date', 'end_date', 'coname', 'ticker', 'cik', 'sic']))\r\n",
    "sp500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save as .feather\r\n",
    "sp500.to_feather(os.path.join(pipeline, 'out', 'sp500_consituents_2000-2020.feather'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save lists with company identifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store S&P500 GVKEYs\r\n",
    "gvkeys = sp500['gvkey'].unique()\r\n",
    "with open(os.path.join(pipeline, 'store', 'sp500_gvkeys.txt'), 'w') as f:\r\n",
    "    for gvkey in gvkeys:\r\n",
    "        f.write(f'{str(gvkey)}\\n')\r\n",
    "\r\n",
    "# Store S&P500 ticker\r\n",
    "tickers = sp500['ticker'].unique()\r\n",
    "with open(os.path.join(pipeline, 'store', 'sp500_tickers.txt'), 'w') as f:\r\n",
    "    for ticker in tickers:\r\n",
    "        f.write(f'{str(ticker)}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\r\n",
    "import paper_style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": "<Figure size 864x864 with 1 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,12), facecolor='w')\r\n",
    "(sp500.apply(lambda x: range(x['start_date'].year, x['end_date'].year+1), axis=1)\r\n",
    "      .explode()\r\n",
    "      .value_counts()\r\n",
    "      .sort_index(ascending=False)\r\n",
    "      .plot.barh(ax=ax, color='steelblue'))\r\n",
    "ax.set_title('Number of S&P Constituents', fontsize=18)\r\n",
    "ax.axvline(500, c='r', lw=0.75, zorder=0)\r\n",
    "ax.grid(axis='y')"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "4ad8bff83ca7a203c8f5c1f2f3a00576dbcaaca98e752b3367803050219b2dc1"
  },
  "kernelspec": {
   "display_name": "Python 3.7.0 64-bit ('ccs': conda)",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": ""
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}