Market Events.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "\n",
    "from xbbg import blp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time Period of Events\n",
    "TODAY_DATE = pd.to_datetime('today')\n",
    "END_OF_WEEK = (pd.datetime('today') + pd.DateOffset(days = (4 - TODAY_DATE.weekday())))\n",
    "\n",
    "# Minimum Market Cap\n",
    "MINIMUM_MKT_CAP = 1e11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pull all tickers for the S&P 500\n",
    "def get_sp500_tickers():\n",
    "    sp500_constituents  = blp.bds('SPX INDEX', \n",
    "                                  'INDX_MEMBERS', \n",
    "                                  'DVD_Start_Dt' = dt.datetime.today().strftime('%Y%m%d'), \n",
    "                                  'DVD_End_Dt' = dt.datetime.today().strftime('%Y%m%d')\n",
    "                                 )\n",
    "    bbg_names = [ticker + ' Equity' for ticker in bbg_names]\n",
    "    return bbg_names\n",
    "\n",
    "# Pull all of the S&P 500 earnings events \n",
    "def get_sp500_earnings_this_week():\n",
    "    sp500_tickers = get_sp500_tickers()\n",
    "    \n",
    "    earnings_data = blp.bdp(sp500_tickers, \n",
    "                            ['SECURITY_NAME', \n",
    "                             'TICKER', \n",
    "                             'EXPECTED_REPORT_DT', \n",
    "                             'EXPECTED_REPORT_TIME', \n",
    "                             'CUR_MKT_CAP']\n",
    "                           )\n",
    "    \n",
    "    earnings_data['expected_report_dt'] = pd.to_datetime(earnings_data['expected_report_dt'])\n",
    "    \n",
    "    filter = earnings_data['expected_report_dt'] <= pd.Timestamp(END_OF_WEEK)\n",
    "    filter &= earnings_data['expected_report_dt'] >= pd.Timestamp(TODAY_DATE)\n",
    "    filter &= earnings_data['cur_mkt_cap'] >= MINIMUM_MKT_CAP\n",
    "    filter &= earnings_data['cur_mkt_cap'] >= MINIMUM_MKT_CAP\n",
    "    earnings_data = earnings_data[filter]\n",
    "    \n",
    "    earnings_data['cur_mkt_cap'] = (earnings_data['cur_mkt_cap']/(1e9)).astype(int).apply(lambda x:\"{:,}\".format(x))\n",
    "    earnings_data['Day'] = pd.to_datetime(earnings_data['expected_report_dt']).dt.day_name()\n",
    "    earnings_data.sort_values(['expected_report_dt', 'cur_mkt_cap'], ascending = [True, False], inplace = True)\n",
    "    earnings_data.reset_index(drop = True, inplace = True)\n",
    "    earnings_data.rename(columns = {'security_name': 'Company', \n",
    "                                    'ticker':'Ticker', \n",
    "                                    'expected_report_dt':'Earnings Date', \n",
    "                                    'expected_report_time':'Release Time (Eastern)', \n",
    "                                    'cur_mkt_cap':'Market Cap (B)'}, \n",
    "                         inplace = True)\n",
    "    earnings_data = earnings_data[['Company', \n",
    "                                   'Ticker', \n",
    "                                   'Earnings Date', \n",
    "                                   'Day', \n",
    "                                   'Release Time (Eastern)', \n",
    "                                   'Market Cap (B)']]\n",
    "    \n",
    "    return earnings_data\n",
    "\n",
    "\n",
    "def get_fed_hikes():\n",
    "    fed_hike = blp.bdp(['FDTR Index'], ['ECO_RELEASE_DT', 'ECO_RELEASE_TIME'])\n",
    "    fed_hike = fed_hike[(fed_hike['eco_release_dt'] <= END_OF_WEEK) \n",
    "                        & (fed_hike['eco_release_dt'] >= TODAY_DATE)]\n",
    "    fed_hike['Company'] = 'FOMC Rate Decision'\n",
    "    fed_hike['Ticker'] = 'FED'\n",
    "    fed_hike['Day'] = pd.to_datetime(fed_hike['expected_report_dt']).dt.day_name()\n",
    "    fed_hike['Market Cap (B)'] = np.inf\n",
    "    \n",
    "    fed_hike.rename(columns = {'eco_release_dt':'Earnings Date', \n",
    "                               'eco_release_time':'Release Time (Eastern)'}, \n",
    "                    inplace = True\n",
    "                   )\n",
    "    \n",
    "    return fed_hike\n",
    "\n",
    "\n",
    "def get_tsy_auctions():\n",
    "    \n",
    "    tsy_auctions = blp.bdp(['USB2YBC Index', \n",
    "                            'USB5YBC Index', \n",
    "                            'USN10YBC Index', \n",
    "                            'USBD30YBC Index'], \n",
    "                           ['ECO_RELEASE_DT', \n",
    "                            'ECO_RELEASE_TIME']\n",
    "                          )\n",
    "    tsy_auctions['Company'] = 'TSY Auction'\n",
    "    tsy_auctions['Ticker'] = ['2Y', '5Y', '10Y', '30Y']\n",
    "    tsy_auctions['Day'] = pd.to_datetime(tsy_auctions['eco_release_dt']).dt.day_name()\n",
    "    tsy_auctions['Market Cap (B)'] = np.inf\n",
    "    tsy_auctions = tsy_auctions[(tsy_auctions['eco_release_dt'] <= END_OF_WEEK) \n",
    "                                & (tsy_auctions['eco_release_dt'] >= TODAY_DATE)]\n",
    "    tsy_auctions.rename(columns = {'eco_release_dt':'Earnings Date', \n",
    "                                   'eco_release_time':'Release Time (Eastern)'}, \n",
    "                        inplace = True\n",
    "                       )\n",
    "    \n",
    "    if (len(tsy_auctions) == 0):\n",
    "        return None\n",
    "    \n",
    "    tsy_amounts = blp.bdp(['WIT2 Govt', \n",
    "                           'WIT5 Govt', \n",
    "                           'WIT10 Govt', \n",
    "                           'WIT30 Govt'], \n",
    "                          ['BQ_FACE_AMT', \n",
    "                           'MTY_YEARS', \n",
    "                           'AMT_ISSUED']\n",
    "                         )\n",
    "    tsy_amounts.reset_index(inplace = True)\n",
    "    tsy_amounts.columns = ['Ticker', 'Face', 'Years', 'Notional']\n",
    "    tsy_amounts = tsy_amounts[['Ticker', 'Notional']]\n",
    "    tsy_amounts['Ticker'] = ['10Y', '2Y', '30Y', '5Y']\n",
    "    tsy_amounts['Notional'] = (tsy_amounts['Notional']/(1e9))\n",
    "    tsy_amounts.columns = ['Ticker', 'Market Cap (B)']\n",
    "    \n",
    "    tsy_auctions = tsy_auctions.merge(tsy_amounts, how = 'left', on = 'Ticker', suffixes = ('_', ''))\n",
    "    tsy_auctions = tsy_auctions.drop('Market Cap (B)_', axis = 1)\n",
    "    tsy_auctions['Market Cap (B)'] = tsy_auctions['Market Cap (B)'].astype(int)\n",
    "    \n",
    "    return tsy_auctions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "earnings = get_sp500_earnings_this_week()\n",
    "fed_hike = get_fed_hikes()\n",
    "tsy_auction = get_tsy_auctions()\n",
    "all_events = pd.concat([earnings, fed_hike, tsy_auction])\n",
    "all_events.sort_values(['Earnings Date', 'Market Cap (B)'], ascending = [True, False])\n",
    "all_events.reset_index(drop = True, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_events"
   ]
  }
 ],
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