path: root/python/notebooks/swaption_scenarios.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import analytics\n",
    "import math\n",
    "\n",
    "from graphics import plot_color_map\n",
    "from analytics import Swaption, BlackSwaption, BlackSwaptionVolSurface, CreditIndex, Portfolio\n",
    "from analytics.scenarios import run_swaption_scenarios, run_index_scenarios, run_portfolio_scenarios, run_portfolio_scenarios_module\n",
    "from scipy.interpolate import SmoothBivariateSpline\n",
    "from utils.db import dbconn, dbengine\n",
    "from risk.swaptions import get_swaption_portfolio\n",
    "\n",
    "conn = dbconn('dawndb')\n",
    "dawn_engine = dbengine('dawndb')\n",
    "conn.autocommit=True\n",
    "analytics.init_ontr()\n",
    "pd.options.display.float_format = \"{:,.2f}\".format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "portf = get_swaption_portfolio(datetime.date.today() - pd.offsets.BDay(), conn, source_list=['GS'])\n",
    "\n",
    "hedges = pd.read_sql_query(\"SELECT security_id as redcode, maturity, notional, folder FROM list_cds_positions_by_strat(%s) \"\n",
    "                           \"WHERE folder in ('IGOPTDEL', 'HYOPTDEL')\",\n",
    "                           conn, params=(datetime.date.today(),))\n",
    "\n",
    "for i, r in hedges.iterrows():\n",
    "    strategy = r.pop(\"folder\")\n",
    "    trade_index = CreditIndex(**r, value_date=datetime.date.today() - pd.offsets.BDay())\n",
    "    trade_index.mark()\n",
    "    portf.add_trade(trade_index, (strategy, i))\n",
    "\n",
    "vol_surface = {}\n",
    "for trade in portf.swaptions:\n",
    "    k = (trade.index.index_type, trade.index.series, trade.option_type)\n",
    "    if k not in vol_surface:\n",
    "        vs = BlackSwaptionVolSurface(trade.index.index_type, trade.index.series, \n",
    "                                     value_date=datetime.date.today(), interp_method = \"bivariate_linear\")\n",
    "        vol_surface[k] = vs[vs.list('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.reset_pv()\n",
    "\n",
    "#set ref to today's levels\n",
    "portf.value_date = datetime.date.today()\n",
    "portf.mark(interp_method=\"bivariate_linear\", source_list=['GS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spread_shock = np.round(np.arange(-.1, .1, .01), 4)\n",
    "scens = run_portfolio_scenarios(portf, [datetime.datetime.now()], params=['pnl', 'hy_equiv', 'sigma'],\n",
    "                                spread_shock=spread_shock,\n",
    "                                vol_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')\n",
    "ig.mark()\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.reset_index([\"date\", \"vol_shock\"], drop=True).to_frame(\"pnl\").plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hy_equiv.reset_index([\"date\", \"vol_shock\"], drop=True).to_frame(\"hy_equiv\").plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pnl, hy_equiv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Trade Analysis - pre-trade analytics\n",
    "index = 'IG'\n",
    "series = 32\n",
    "option_delta = CreditIndex(index, series, '5yr') \n",
    "option_delta.spread = 60\n",
    "option1 = BlackSwaption(option_delta, datetime.date(2019, 9, 17), 90, option_type=\"payer\") \n",
    "option2 = BlackSwaption(option_delta, datetime.date(2019, 11, 19), 90, option_type=\"payer\") \n",
    "option1.sigma = .6\n",
    "option2.sigma = .58\n",
    "option1.notional = 100_000_000 \n",
    "option2.notional = 100_000_000 \n",
    "option1.direction = 'Long' \n",
    "option2.direction = 'Short' \n",
    "option_delta.notional = option1.delta * option1.notional + option2.delta * option2.notional\n",
    "portf = Portfolio([option1, option2, option_delta], trade_ids=['opt1', 'opt2', 'delta'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_range = pd.bdate_range(portf.value_date, portf.value_date + pd.tseries.offsets.BDay(30), freq='3B')\n",
    "vol_shock = np.arange(-.15, .31, 0.01)\n",
    "spread_shock = np.arange(-.2, 2, 0.01)\n",
    "vol_surface = {}\n",
    "for trade in portf.swaptions:\n",
    "    vs = BlackSwaptionVolSurface(trade.index.index_type, trade.index.series, \n",
    "                             value_date=portf.value_date, interp_method = \"bivariate_linear\")\n",
    "    vol_surface[(trade.index.index_type, trade.index.series)] = vs[vs.list(option_type='payer')[-1]]\n",
    "\n",
    "df = run_portfolio_scenarios(portf, date_range, params=[\"pnl\"],\n",
    "                             spread_shock = spread_shock,\n",
    "                             vol_shock = vol_shock,\n",
    "                             vol_surface = vol_surface)\n",
    "df = df.reset_index()\n",
    "df.vol_shock = df.vol_shock.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_trade_scenarios(portf, shock_min=-.15, shock_max=.2, vol_time_roll=True):\n",
    "    portf.reset_pv()\n",
    "    end_date = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date\n",
    "    date_range = pd.bdate_range(portf.value_date,\n",
    "                                end_date - pd.tseries.offsets.BDay(), freq='3B')\n",
    "    vol_shock = np.arange(-.15, .31, 0.01)\n",
    "    spread_shock = np.arange(shock_min, shock_max, 0.01)\n",
    "    index = portf.indices[0].index_type\n",
    "    vol_surface = {}\n",
    "    for trade in portf.swaptions:\n",
    "        vs = BlackSwaptionVolSurface(trade.index.index_type, trade.index.series, \n",
    "                                 value_date=portf.value_date, interp_method = \"bivariate_linear\")\n",
    "        vol_surface[(trade.index.index_type, trade.index.series)] = vs[vs.list(option_type='payer')[-1]]\n",
    "    \n",
    "    df = run_portfolio_scenarios(portf, date_range, params=[\"pnl\",\"delta\"],\n",
    "                                 spread_shock = spread_shock,\n",
    "                                 vol_shock = vol_shock,\n",
    "                                 vol_surface = vol_surface)\n",
    "    df = df.reset_index()\n",
    "    df.vol_shock = df.vol_shock.round(2)\n",
    "\n",
    "    if index == 'HY':\n",
    "        df['price'] = 100 + (500 - portf.indices[0].spread * (1 + df.spread_shock)) \\\n",
    "                    * abs(portf.indices[0].DV01) / portf.indices[0].notional * 100\n",
    "        df = df.set_index(['date', 'price', 'vol_shock'])\n",
    "        sort_order = [True, False]\n",
    "    else:\n",
    "        df['spread'] = portf.indices[0].spread * (1 +  df.spread_shock)\n",
    "        df = df.set_index(['date', 'spread', 'vol_shock'])\n",
    "        sort_order = [True, True]\n",
    "     \n",
    "    pnl = df.xs('pnl', axis=1, level=1).sum(axis=1)\n",
    "    for trade_id, t in portf.items():\n",
    "        if isinstance(t, BlackSwaption):\n",
    "            df[(trade_id, 'delta')] *= -t.notional \n",
    "    delta = df.xs('delta', axis=1, level=1).sum(axis=1).xs(0, level='vol_shock')\n",
    "    delta += sum([x.notional * -1 if x.direction == 'Buyer' else 1 for x in portf.indices])\n",
    "\n",
    "    pnl.name = 'pnl'\n",
    "    delta.name = 'delta'\n",
    "\n",
    "    plot_color_map(pnl.xs(0, level='vol_shock'), sort_order)\n",
    "    plot_color_map(delta, sort_order)\n",
    "    plot_color_map(pnl.loc[date_range[-1]], sort_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_simple_scenario(portf, shock_min=-.15, shock_max=.2):\n",
    "    portf.reset_pv()\n",
    "    end_date = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date\n",
    "    date_range = pd.bdate_range(portf.value_date,\n",
    "                                end_date - pd.tseries.offsets.BDay(), freq='3B')\n",
    "    vol_shock = [0]\n",
    "    spread_shock = np.arange(shock_min, shock_max, 0.01)\n",
    "    index = portf.indices[0].index_type\n",
    "    vol_surface = {}\n",
    "    for trade in portf.swaptions:\n",
    "        vs = BlackSwaptionVolSurface(trade.index.index_type, trade.index.series, \n",
    "                                 value_date=portf.value_date, interp_method = \"bivariate_linear\")\n",
    "        vol_surface[(trade.index.index_type, trade.index.series)] = vs[vs.list(option_type='payer')[-1]]\n",
    "\n",
    "    df = run_portfolio_scenarios(portf, date_range, params=[\"pnl\"],\n",
    "                                 spread_shock = spread_shock,\n",
    "                                 vol_shock = vol_shock,\n",
    "                                 vol_surface = vol_surface)\n",
    "\n",
    "    return df.xs('pnl', axis=1, level=1).sum(axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_trade_scenarios(portf)\n",
    "plot_trade_scenarios(portf, -.15, .5, vol_time_roll=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dec Jan 2017 Trade\n",
    "option_delta = CreditIndex.from_tradeid(864)\n",
    "option1 = BlackSwaption.from_tradeid(3, option_delta)\n",
    "option2 = BlackSwaption.from_tradeid(4, option_delta)\n",
    "portf = Portfolio([option1, option2, option_delta], trade_ids=['opt1', 'opt2', 'delta'])\n",
    "#plot_trade_scenarios(portf)\n",
    "\n",
    "#Feb 2017: Sell May Buy April Calendar Trade\n",
    "option_delta = CreditIndex.from_tradeid(870)\n",
    "option1 = BlackSwaption.from_tradeid(5, option_delta)\n",
    "option2 = BlackSwaption.from_tradeid(6, option_delta)\n",
    "portf = Portfolio([option1, option2, option_delta], trade_ids=['opt1', 'opt2', 'delta'])\n",
    "#plot_trade_scenarios(portf)\n",
    "\n",
    "#April 2017: Sell May Buy June Calendar Trade\n",
    "option_delta = CreditIndex.from_tradeid(874)\n",
    "option1 = BlackSwaption.from_tradeid(7, option_delta)\n",
    "option2 = BlackSwaption.from_tradeid(8, option_delta)\n",
    "portf = Portfolio([option1, option2, option_delta], trade_ids=['opt1', 'opt2', 'delta'])\n",
    "#plot_trade_scenarios(portf)\n",
    "\n",
    "#June July 2017 Calendar Trade\n",
    "option_delta_pf = CreditIndex.from_tradeid(874)\n",
    "option_delta2_pf = CreditIndex.from_tradeid(879)\n",
    "\n",
    "option1_pf = BlackSwaption.from_tradeid(7, option_delta_pf)\n",
    "option2_pf = BlackSwaption.from_tradeid(9, option_delta_pf)\n",
    "option_delta_pf.notional = 50_335_169\n",
    "\n",
    "portf = Portfolio([option1_pf, option2_pf, option_delta_pf], trade_ids=['opt1', 'opt2', 'delta'])\n",
    "portf.value_date = datetime.date(2017, 5, 17)\n",
    "portf.mark()\n",
    "#plot_trade_scenarios(portf)\n",
    "\n",
    "#July 2017: Buy Sept HY payer spread\n",
    "option_delta = CreditIndex.from_tradeid(891)\n",
    "option1 = BlackSwaption.from_tradeid(10, option_delta)\n",
    "option2 = BlackSwaption.from_tradeid(11, option_delta)\n",
    "portf = Portfolio([option1, option2, option_delta], trade_ids=['opt1', 'opt2', 'delta'])\n",
    "#plot_trade_scenarios(portf)\n",
    "\n",
    "#March 2019: May Bull Risky\n",
    "option_delta = CreditIndex.from_tradeid(1063)\n",
    "option1 = BlackSwaption.from_tradeid(41, option_delta)\n",
    "option2 = BlackSwaption.from_tradeid(40, option_delta)\n",
    "portf = Portfolio([option1, option2, option_delta], trade_ids=['opt1', 'opt2', 'delta'])\n",
    "results = calc_simple_scenario(portf, shock_min=-.3, shock_max=.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Look at steepness of volatility - 30 days, .85 vs .15 payer deltas on HY\n",
    "days = 30\n",
    "delta1 = .85\n",
    "delta2 = .15\n",
    "index = 'HY'\n",
    "\n",
    "sql_str = \"select b.quotedate, b.ref, b.ref_id, b.expiry, a.delta_pay, a.vol from \" \\\n",
    "          \"swaption_quotes a join swaption_ref_quotes b on a.ref_id = b.ref_id and index = %s\"\n",
    "df = pd.read_sql_query(sql_str, dbengine('serenitasdb'), \n",
    "     index_col=['quotedate'], parse_dates={'quotedate': {'utc': True}}, params=[index])\n",
    "df['days_expiry'] =  (df.expiry - df.index.date).dt.days\n",
    "r_1 = []\n",
    "for i, g in df.groupby(pd.Grouper(freq='D', level='quotedate')):\n",
    "    r = []\n",
    "    for i_1, g_1 in g.groupby(['days_expiry', 'delta_pay']):\n",
    "        r.append([i_1[0], i_1[1], g_1['vol'].mean()])\n",
    "    if len(r) > 0:\n",
    "        temp = np.dstack(r)\n",
    "        f = SmoothBivariateSpline(temp[0][0], temp[0][1], temp[0][2])\n",
    "        r = (f(days, delta1) - f(days, delta2))[0][0]\n",
    "        r_1.append([i, r])\n",
    "    else:\n",
    "        pass\n",
    "df_1 = pd.DataFrame(r_1, columns=['date', 'steepness'])\n",
    "df_1.set_index('date').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set up Portfolio\n",
    "from risk.swaptions import get_swaption_portfolio\n",
    "from risk.tranches import get_tranche_portfolio\n",
    "rundate = datetime.date(2019,6,21)\n",
    "\n",
    "portf = get_swaption_portfolio(rundate, conn)\n",
    "\n",
    "#index positions\n",
    "df = pd.read_sql_query(\"SELECT * from list_cds_positions_by_strat(%s)\",\n",
    "                       dawn_engine, params=(rundate,))\n",
    "df = df[df.folder.str.contains(\"OPT\")]\n",
    "for t in df.itertuples(index=False):\n",
    "    portf.add_trade(CreditIndex(redcode=t.security_id, maturity=t.maturity, notional=t.notional),\n",
    "                   (t.folder, t.security_desc))\n",
    "\n",
    "portf.value_date = rundate\n",
    "portf.mark(interp_method=\"bivariate_linear\")\n",
    "portf.reset_pv()\n",
    "\n",
    "#------------------------Calc Scenarios\n",
    "vol_surface = {}\n",
    "for trade in portf.swaptions:\n",
    "    vs = BlackSwaptionVolSurface(trade.index.index_type, trade.index.series, \n",
    "                                 value_date=rundate, interp_method = \"bivariate_linear\")\n",
    "    vol_surface[(trade.index.index_type, trade.index.series)] = vs[vs.list(option_type='payer')[-1]]\n",
    "vol_shock = [0]\n",
    "corr_shock = [0]\n",
    "spread_shock = np.round(np.arange(-.2, 1, .05), 3)\n",
    "scens = run_portfolio_scenarios(portf, [pd.Timestamp(rundate)], params=['pnl', 'delta'],\n",
    "                                spread_shock=spread_shock,\n",
    "                                vol_shock=vol_shock,\n",
    "                                corr_shock=[0],\n",
    "                                vol_surface=vol_surface)\n",
    "\n",
    "pnl = scens.xs('pnl', axis=1, level=2)\n",
    "pnl = pnl.xs(0, level='vol_shock')\n",
    "\n",
    "scenarios = (pnl.\n",
    "          reset_index(level=['date'], drop=True).\n",
    "          groupby(level=0, axis=1).sum())\n",
    "\n",
    "options = ['HYOPTDEL', 'HYPAYER', 'HYREC', 'IGOPTDEL', 'IGPAYER', 'IGREC']\n",
    "scenarios['options'] = scenarios[set(scenarios.columns).intersection(options)].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now say that it widens by X percentage, then rebalance, then do the shock again\n",
    "rundate = datetime.date(2019,6,21)\n",
    "x = 1.2\n",
    "for t in portf.swaptions:\n",
    "    t.index.spread *= x\n",
    "    vs = vol_surface[(t.index.index_type, t.index.series)]\n",
    "    t.sigma = max(0.2, float(vs(t.T, math.log(t.moneyness))))\n",
    "for t in portf.indices:\n",
    "    t.spread *= x\n",
    "pnl = portf.pnl\n",
    "\n",
    "analytics.init_ontr(value_date=rundate)\n",
    "rebal = analytics._ontr()\n",
    "rebal.notional = portf.hy_equiv\n",
    "rebal.direction = 'Seller'\n",
    "\n",
    "rebal.spread *= x\n",
    "portf.add_trade(rebal, ('rebalance', 'HYOPTDEL'))\n",
    "portf.reset_pv()\n",
    "\n",
    "swaptions_scens = portf.swaptions[0].shock(params=['pnl', 'pv'],\n",
    "                                spread_shock=spread_shock,\n",
    "                                vol_shock=vol_shock,\n",
    "                                vol_surface=vol_surface)\n",
    "\n",
    "#------------------------Calc Scenarios\n",
    "scens = run_portfolio_scenarios(portf, [pd.Timestamp(rundate)], params=['pnl', 'pv'],\n",
    "                                spread_shock=spread_shock,\n",
    "                                vol_shock=vol_shock,\n",
    "                                corr_shock=[0],\n",
    "                                vol_surface=vol_surface)\n",
    "\n",
    "pnl = scens.xs('pnl', axis=1, level=2)\n",
    "pnl = pnl.xs(0, level='vol_shock')\n",
    "\n",
    "scenarios = (pnl.\n",
    "          reset_index(level=['date'], drop=True).\n",
    "          groupby(level=0, axis=1).sum())\n",
    "\n",
    "options = ['HYOPTDEL', 'HYPAYER', 'HYREC', 'IGOPTDEL', 'IGPAYER', 'IGREC', 'rebalance']\n",
    "scenarios['options'] = scenarios[set(scenarios.columns).intersection(options)].sum(axis=1)"
   ]
  }
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