{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from analytics.curve_trades import curve_pos, on_the_run\n",
    "from analytics.index_data import get_index_quotes\n",
    "from analytics.scenarios import run_portfolio_scenarios\n",
    "from analytics import Swaption, BlackSwaption, CreditIndex, BlackSwaptionVolSurface, Portfolio, ProbSurface, DualCorrTranche\n",
    "from db import dbconn\n",
    "\n",
    "import datetime\n",
    "import exploration.VaR as var\n",
    "import pandas as pd\n",
    "\n",
    "conn = dbconn('dawndb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = (datetime.date.today() - pd.tseries.offsets.BDay(1)).date()\n",
    "report_date = (date + pd.tseries.offsets.BMonthEnd(-1)).date()\n",
    "index_type = \"IG\"\n",
    "quantile = .025"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#IG Curve VaR\n",
    "portf = curve_pos(date, index_type)\n",
    "ig_curve_var = abs(var.hist_var(portf, quantile=quantile, years=5))\n",
    "ig_curve_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#EU Curve VaR\n",
    "index_type = \"EU\"\n",
    "portf = curve_pos(date, index_type)\n",
    "eu_curve_var = abs(var.hist_var(portf, quantile=quantile, years=5))\n",
    "eu_curve_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mortgage Hedge VaR - use IG spread relative move for VaR\n",
    "df = var.get_pos(date, 'HEDGE_MBS')\n",
    "portf = Portfolio([CreditIndex(row.p_index, row.p_series, row.tenor,\n",
    "                                       report_date, -row.notional)\n",
    "                      for row in df[['p_index', 'tenor', 'p_series', 'notional']].\n",
    "                       itertuples(index=False)])\n",
    "portf.mark()\n",
    "mort_hedge_var = abs(var.hist_var(portf, index_type = \"IG\", quantile=quantile, years=3))\n",
    "mort_hedge_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the IM at the FCM account: calculate the IM share of different strategies as a share of VaR\n",
    "filename = date.strftime('%Y%m%d') + \"_OTC_MARGIN.csv\"\n",
    "margin_df = pd.read_csv(\"/home/serenitas/Daily/SG_reports/\" + filename, index_col='System Currency')\n",
    "mortg_hedge_im = mort_hedge_var + mort_hedge_var/(mort_hedge_var + ig_curve_var) * margin_df.loc[('USD', 'SG Settlement Margin')]\n",
    "mortg_hedge_im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Oct ME Bond HY Equiv\n",
    "bond_HY_equiv = -.12088\n",
    "percentile = .95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate amount of stress for reports\n",
    "df = get_index_quotes('HY', list(range(on_the_run('HY') - 10, on_the_run('HY') + 1)),\n",
    "                          tenor=['5yr'], years=5)\n",
    "df = df.xs('5yr', level='tenor')['close_spread'].groupby(['date', 'series']).last()\n",
    "\n",
    "widen, tighten = [], []\n",
    "#approximately 1,3,6 months move (22 each months)\n",
    "for days in [22, 66, 132]: \n",
    "    calc = df.unstack().pct_change(freq= str(days)+'B').stack().groupby('date').last()\n",
    "    widen.append(calc.max())\n",
    "    tighten.append(calc.min())\n",
    "pd.DataFrame([widen, tighten], columns=['1M', '3M', '6M'], index=['widen', 'tighten'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Current tranche and swaptions positions\n",
    "t_sql_string = (\"SELECT id, sum(notional * case when protection='Buyer' then -1 else 1 end) \"\n",
    "              \"OVER (partition by security_id, attach) AS ntl_agg \"\n",
    "              \"FROM cds WHERE swap_type='CD_INDEX_TRANCHE' AND termination_cp IS NULL \"\n",
    "               \"AND trade_date <= %s\")\n",
    "swaption_sql_string = (\"select id, security_desc from swaptions where date(expiration_date) \"\n",
    "                       \"> %s and swap_type = 'CD_INDEX_OPTION' \"\n",
    "                      \"AND trade_date <= %s\")\n",
    "index_sql_string = (\"SELECT id, sum(notional * case when protection='Buyer' then -1 else 1 end) \"\n",
    "                \"OVER (partition by security_id, attach) AS ntl_agg \"\n",
    "                \"FROM cds WHERE swap_type='CD_INDEX' AND termination_cp IS null \"\n",
    "                \"AND folder = 'IGOPTDEL' OR folder = 'HYOPTDEL' \"\n",
    "                \"AND trade_date <= %s\")\n",
    "conn = dbconn('dawndb')\n",
    "with conn.cursor() as c:\n",
    "    #Get Tranche Trade Ids\n",
    "    c.execute(t_sql_string, (date,))\n",
    "    t_trade_ids = [dealid for dealid, ntl in c if ntl != 0]\n",
    "    #Get Swaption Trade Ids\n",
    "    c.execute(swaption_sql_string, (date, date))\n",
    "    swaption_trades = c.fetchall()\n",
    "    #Get Index/deltas Trade Ids\n",
    "    c.execute(index_sql_string, (date,))\n",
    "    index_trade_ids = [dealid for dealid, ntl in c if ntl != 0]\n",
    "    \n",
    "portf = Portfolio([DualCorrTranche.from_tradeid(dealid) for dealid in t_trade_ids],\n",
    "                  t_trade_ids)\n",
    "for row in swaption_trades:\n",
    "    option_delta = CreditIndex(row[1].split()[1], row[1].split()[3][1:], '5yr', date)\n",
    "    option_delta.mark()\n",
    "    portf.add_trade(BlackSwaption.from_tradeid(row[0], option_delta), 'opt_' + str(row[0]))\n",
    "for index_id in index_trade_ids:\n",
    "    portf.add_trade(CreditIndex.from_tradeid(index_id), 'index_' + str(index_id))\n",
    "    \n",
    "#Update manually - positive notional = long risk\n",
    "non_trancheSwap_risk_notional = 33763230\n",
    "portf.add_trade(CreditIndex('HY', on_the_run('HY'), '5yr', value_date = date, notional = -non_trancheSwap_risk_notional), 'port')\n",
    "    \n",
    "portf.value_date = date\n",
    "portf.mark()\n",
    "portf.reset_pv()\n",
    "    \n",
    "vs = BlackSwaptionVolSurface(portf.swaptions[0].index.index_type, portf.swaptions[0].index.series, value_date=date)\n",
    "vol_surface = vs[vs.list(option_type='payer')[-1]]\n",
    "vol_shock = [0]\n",
    "corr_shock = [0]\n",
    "spread_shock = widen + tighten\n",
    "date_range = [pd.Timestamp(date)]\n",
    "\n",
    "scens = run_portfolio_scenarios(portf, date_range, params=[\"pnl\"],\n",
    "                                spread_shock=spread_shock,\n",
    "                                vol_shock=vol_shock,\n",
    "                                corr_shock=corr_shock,\n",
    "                                vol_surface=vol_surface)\n",
    "\n",
    "scens.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "var.cleared_cds_margins(report_date)"
   ]
  }
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