{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# better formatting for large floats\n",
    "import pandas as pd\n",
    "pd.options.display.float_format = \"{:,.2f}\".format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from risk.swaptions import get_swaption_portfolio\n",
    "import datetime\n",
    "from utils.db import dbconn\n",
    "from analytics import init_ontr\n",
    "conn = dbconn('dawndb')\n",
    "conn.autocommit = True\n",
    "init_ontr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "portf = get_swaption_portfolio(datetime.date.today(), conn, source_list=['GS'])\n",
    "portf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = portf._todf()\n",
    "positions = df.set_index(\"Index\")[[\"Delta\", \"Notional\"]].prod(axis=1).groupby(level=\"Index\").sum()\n",
    "positions.name = 'current_delta'\n",
    "gamma = df.set_index(\"Index\")[[\"Gamma\", \"Notional\"]].prod(axis=1).groupby(level=\"Index\").sum()\n",
    "gamma.name = 'gamma'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hedges = pd.read_sql_query(\"SELECT security_desc, notional FROM list_cds_positions_by_strat(%s) \"\n",
    "                           \"WHERE folder in ('IGOPTDEL', 'HYOPTDEL')\",\n",
    "                           conn, params=(datetime.date.today(),))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(s):\n",
    "    l = s.split(\" \")\n",
    "    return f\"{l[1]}{l[3][1:]} {l[4].lower()}r\"\n",
    "\n",
    "hedges[\"Index\"] = hedges[\"security_desc\"].apply(f)\n",
    "hedges = hedges.rename(columns={\"notional\": \"current hedge\"})\n",
    "hedges = hedges.set_index(\"Index\")[\"current hedge\"]\n",
    "hedges = hedges.reindex(positions.index, fill_value=0.)\n",
    "risk = pd.concat([hedges, positions, gamma], axis=1)\n",
    "risk['net_delta'] = risk[\"current hedge\"] + risk.current_delta\n",
    "risk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from analytics.scenarios import run_portfolio_scenarios\n",
    "from analytics import BlackSwaptionVolSurface, CreditIndex\n",
    "import analytics\n",
    "import datetime\n",
    "import numpy as np\n",
    "\n",
    "today = datetime.datetime.now()\n",
    "yesterday = datetime.date.today() - pd.offsets.BDay()\n",
    "\n",
    "portf = get_swaption_portfolio(yesterday, conn, source_list=['GS'])\n",
    "for i, amt in hedges.iteritems():\n",
    "    portf.add_trade(CreditIndex(i[:2], i[2:4], '5yr', value_date=yesterday, notional=amt), ('delta', i))\n",
    "\n",
    "vol_surface = {}\n",
    "for trade in portf.swaptions:\n",
    "    vs = BlackSwaptionVolSurface(trade.index.index_type, trade.index.series, \n",
    "                                 value_date=today.date(), interp_method = \"bivariate_linear\")\n",
    "    vol_surface[(trade.index.index_type, trade.index.series, trade.option_type)] = vs[vs.list(source='GS', option_type=trade.option_type)[-1]]\n",
    "\n",
    "#Set original_pv as of yesterday's EOD levels, don't reset PV after this time\n",
    "portf.mark(interp_method=\"bivariate_linear\", source_list=['GS'])\n",
    "portf.reset_pv()\n",
    "\n",
    "#set ref to today's levels\n",
    "portf.value_date = today\n",
    "portf.mark(interp_method=\"bivariate_linear\", source_list=['GS'])\n",
    "\n",
    "spread_shock = np.round(np.arange(-.1, .1, .01), 4)\n",
    "scens = run_portfolio_scenarios(portf, [today], params=['pnl', 'hy_equiv', 'sigma'],\n",
    "                                spread_shock=spread_shock,\n",
    "                                vol_shock=[0],\n",
    "                                corr_shock=[0],\n",
    "                                vol_surface=vol_surface)\n",
    "pnl = scens.xs('pnl', level = 2, axis=1).sum(axis=1)\n",
    "hy_equiv = scens.xs('hy_equiv', level = 2, axis=1).sum(axis=1)\n",
    "\n",
    "ig = CreditIndex('IG', 32, '5yr', value_date = today)\n",
    "ig.mark()\n",
    "\n",
    "pnl.index = pnl.index.set_levels((1+pnl.index.get_level_values('spread_shock')) * ig.spread, level = 'spread_shock')\n",
    "hy_equiv.index = pnl.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pnl, hy_equiv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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