{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import globeop_reports as go\n",
    "import pandas as pd\n",
    "import analytics\n",
    "import numpy as np\n",
    "\n",
    "from pandas.tseries.offsets import BDay, MonthEnd\n",
    "from analytics.scenarios import run_portfolio_scenarios\n",
    "from utils.db import dbconn\n",
    "from risk.portfolio import build_portfolio, generate_vol_surface\n",
    "from analytics.basket_index import BasketIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set dates\n",
    "position_date = (datetime.date.today() - MonthEnd(1)).date()\n",
    "spread_date = position_date\n",
    "analytics._local = False\n",
    "analytics.init_ontr(spread_date)\n",
    "path = '/home/serenitas/Daily/Risk/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################### Run Credit Spread scenarios\n",
    "spread_shock = np.array([-100., -25., 1., +25. , 100.])\n",
    "spread_shock /= analytics._ontr['HY'].spread\n",
    "portf, _ = build_portfolio(position_date, spread_date)\n",
    "vol_surface = generate_vol_surface(portf, 5)\n",
    "portf.reset_pv()\n",
    "scens = run_portfolio_scenarios(portf, date_range=[pd.Timestamp(spread_date)], params=['pnl'],\n",
    "                                spread_shock=spread_shock,\n",
    "                                vol_shock=[0.0],\n",
    "                                corr_shock=[0.0],\n",
    "                                vol_surface=vol_surface)\n",
    "scens.sum(axis=1).to_csv(path+'csscen_'+position_date.strftime(\"%Y%m%d\")+'.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################### JTD\n",
    "_, portf = build_portfolio(position_date, spread_date)\n",
    "jtd_i = []\n",
    "for t in portf.indices:\n",
    "    bkt = BasketIndex(t.index_type, t.series, [t.tenor])\n",
    "    spreads = pd.DataFrame(bkt.spreads() * 10000, index=pd.Index(bkt.tickers, name='ticker'), columns=['spread'])\n",
    "    jump = pd.merge(spreads, bkt.jump_to_default() * t.notional, left_index=True, right_index=True)\n",
    "    jtd_i.append(jump.rename(columns={jump.columns[1]: 'jtd'}))\n",
    "jtd_t = []\n",
    "for t in portf.tranches:\n",
    "    jump = pd.concat([t.singlename_spreads().reset_index(['seniority', 'doc_clause'], drop=True), t.jump_to_default().rename('jtd')], axis=1)\n",
    "    jtd_t.append(jump.drop(['weight', 'recovery'], axis=1))\n",
    "\n",
    "ref_names = pd.read_sql_query(\"select ticker, referenceentity from refentity\", dbconn('serenitasdb'), index_col='ticker')\n",
    "jump = pd.concat([pd.concat(jtd_t), pd.concat(jtd_i)])\n",
    "jump = jump.merge(ref_names, left_index=True, right_index=True)\n",
    "jump = jump.groupby('referenceentity').agg({'spread': np.mean, 'jtd': np.sum}).sort_values(by='jtd', ascending=True)\n",
    "jump.to_csv(path+'jtd_'+position_date.strftime(\"%Y%m%d\")+'.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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