{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from analytics.curve_trades import curve_pos\n",
    "from analytics import Index, Portfolio\n",
    "\n",
    "import datetime\n",
    "import exploration.VaR as var\n",
    "import pandas as pd"
   ]
  },
  {
   "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))\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))\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)\n",
    "df = df[df.strategy == 'HEDGE_MBS']\n",
    "portf = Portfolio([Index.from_name(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_EX_DEF.csv\"\n",
    "margin_df = pd.read_csv(\"/home/serenitas/Daily/SG_reports/\" + filename, index_col='Currency')\n",
    "morg_hedge_im = mort_hedge_var + mort_hedge_var/(mort_hedge_var + ig_curve_var) * margin_df.loc[('USD', 'SG IMR')]\n",
    "morg_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": [
    "#95%tile \n",
    "df, spread, dur = var.rel_spread_diff(report_date)\n",
    "stress = pd.DataFrame()\n",
    "stress.at[('2SD_widen', 'spread')] = df.quantile(.975) \n",
    "stress.at[('2SD_tighten', 'spread')] = df.quantile(.025) \n",
    "stress.at[('worst_widen', 'spread')] = df.max()\n",
    "stress['pts'] = -stress * spread * dur/100\n",
    "stress['nav_impact'] = bond_HY_equiv * stress['pts']\n",
    "stress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "port.cleared_cds_margins(report_date, percentile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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