{ "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)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 }