{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import portfolio_var as port\n",
    "from analytics import Swaption, BlackSwaption, Index, VolatilitySurface, Portfolio\n",
    "from analytics.scenarios import run_swaption_scenarios, run_index_scenarios, run_portfolio_scenarios\n",
    "import datetime\n",
    "import pandas as pd\n",
    "from pandas.tseries.offsets import BDay, BMonthEnd\n",
    "\n",
    "#import exploration.swaption_calendar_spread as spread\n",
    "import exploration.swaption_calendar_spread as spread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Oct ME Bond HY Equiv\n",
    "report_date = (datetime.date.today() + BMonthEnd(-1)).date()\n",
    "bond_HY_equiv = -.12088\n",
    "percentile = .95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The 95%tile \n",
    "df, spread, dur = port.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']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": [
    "port.index_curve_margins(report_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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