{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import analytics.tranche_basket as bkt\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from analytics.scenarios import run_tranche_scenarios, run_portfolio_scenarios, run_tranche_scenarios_rolldown\n", "from analytics import Swaption, BlackSwaption, CreditIndex, BlackSwaptionVolSurface, Portfolio, ProbSurface\n", "from analytics import DualCorrTranche\n", "from db import dbconn\n", "from datetime import date\n", "from graphics import plot_color_map\n", "\n", "value_date = (pd.datetime.today() - pd.offsets.BDay(2)).date()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Construct IG Swaption Portfolio\n", "index = 'IG'\n", "series = 30\n", "option_delta = CreditIndex(index, series, '5yr', value_date=value_date)\n", "option_delta.spread = 60\n", "option1 = BlackSwaption(option_delta, date(2018, 11, 21), 62.5, option_type=\"payer\")\n", "option1.sigma = .404\n", "option1.direction = 'Long'\n", "option2 = BlackSwaption(option_delta, date(2018, 11, 21), 85, option_type=\"payer\")\n", "option2.sigma = .588\n", "option2.direction = 'Short'\n", "option1.notional = 100_000_000\n", "option2.notional = 300_000_000\n", "option_delta.notional = option1.notional * option1.delta + option2.notional * option2.delta\n", "\n", "#Get current Tranche positions\n", "sql_string = (\"SELECT id, sum(notional * case when protection='Buyer' then -1 else 1 end) \"\n", " \"OVER (partition by security_id, attach) AS ntl_agg \"\n", " \"FROM cds WHERE swap_type='CD_INDEX_TRANCHE' AND termination_cp IS NULL\")\n", "conn = dbconn('dawndb')\n", "with conn.cursor() as c:\n", " c.execute(sql_string)\n", " trade_ids = [dealid for dealid, ntl in c if ntl != 0]\n", "portf = Portfolio([DualCorrTranche.from_tradeid(dealid) for dealid in trade_ids],\n", " trade_ids)\n", "portf.trades.extend([option1, option2, option_delta])\n", "portf.trade_ids.extend(['opt1', 'opt2', 'delta'])\n", "\n", "spread_shock = np.arange(-.3, 1.1, .1)\n", "corr_shock = np.arange(0, .1, 0.1)\n", "vol_shock = np.arange(-.1, .3, 0.1)\n", "earliest_expiry = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date\n", "date_range = pd.date_range(value_date, earliest_expiry, periods=5)\n", "vs = BlackSwaptionVolSurface(index, series, value_date=value_date)\n", "ps = ProbSurface(index, series, value_date=value_date)\n", "vol_surface = vs[vs.list(option_type='payer')[-1]]\n", "portf.value_date = value_date\n", "portf.mark()\n", "portf.reset_pv()\n", "\n", "scens = run_portfolio_scenarios(portf, date_range, params=[\"pnl\"],\n", " spread_shock=spread_shock,\n", " corr_shock=corr_shock,\n", " vol_shock=vol_shock,\n", " vol_surface=vol_surface)\n", "scens = round(scens,2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sort_order = [True, False]\n", "output = scens.xs((0,0), level=['corr_shock', 'vol_shock']).sum(axis=1)\n", "(1+output.index.get_level_values(1)) * portf.swaptions[0].ref\n", "output.name = 'pnl'\n", "plot_color_map(output, sort_order)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#negative notional == sell protection\n", "hy_tranche = DualCorrTranche('HY', 29, '5yr', attach=0, detach=15, corr_attach=np.nan, \n", " corr_detach=.35, tranche_running=500, notional=-10000000)\n", "portf1 = Portfolio([hy_tranche], [1])\n", "scens = run_portfolio_scenarios(portf1, date_range, params=[\"pnl\"],\n", " spread_shock=spread_shock,\n", " corr_shock=corr_shock,\n", " vol_shock=vol_shock,\n", " vol_surface=vol_surface)\n", "scens.xs((0,0), level=['corr_shock', 'vol_shock'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "##\n", "scens_more = run_portfolio_scenarios(portf, date_range, params=['pnl', 'delta'],\n", " spread_shock=spread_shock,\n", " corr_shock=corr_shock,\n", " vol_shock=vol_shock,\n", " vol_surface=vol_surface)\n", "#swaption delta is in protection terms: switch to risk terms\n", "swaption_scens.delta = -swaption_scens.delta" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pos['basket'] = tranche_port\n", "#Set Shock Range\n", "spread_range = (1+ spread_shock) * option_delta.spread\n", "#Run tranche scenarios\n", "temp = []\n", "for i, r in pos.iterrows():\n", " df = run_tranche_scenarios_rolldown(r.basket, spread_range, date_range, corr_map=False)\n", " temp.append(r.notional*df.xs(str(r.attach) + \"-\" + str(r.detach), axis=1, level=1))\n", "tranches_scens = sum(temp)\n", "#Create snapshot of the the first scenario date\n", "total_scens = swaption_scens.reset_index().merge(tranches_scens.reset_index(), \n", " left_on=['date', 'spread'], \n", " right_on=['date', 'spread_range'], \n", " suffixes=['_s', '_t'])\n", "total_scens['pnl'] = total_scens['pnl_s'] + total_scens['pnl_t']\n", "total_scens['delta'] = total_scens['delta_s'] + total_scens['delta_t']\n", "total_scens_single_date = total_scens.set_index('date').xs(date_range[0])\n", "total_scens_single_date = total_scens_single_date.set_index('spread', drop=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#tranche positions delta at different spreads\n", "ax = total_scens_single_date.delta_t.plot(title = 'delta vs. spread levels')\n", "ax.ticklabel_format(style='plain')\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Tranche + Swaptions positions delta at different spreads\n", "ax1 = total_scens_single_date.delta.plot()\n", "ax1.ticklabel_format(style='plain')\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "total_scens_single_date[['delta', 'delta_t', 'delta_s']].plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "total_scens_single_date[['pnl', 'pnl_t', 'pnl_s']].plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "total_scens.date = pd.to_datetime(total_scens.date)\n", "total_scens = total_scens.set_index(['date'])\n", "plot_time_color_map(total_scens, spread_range, attr=\"pnl\")\n", "#plot_time_color_map(df, shock_range, attr=\"final_delta\", color_map= 'rainbow', centered = False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#PNL of just the swaptions\n", "plot_time_color_map(swaption_scens, spread_range, attr=\"pnl\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Construct levered Super senior hedged with swaption\n", "index = 'IG'\n", "series = 30\n", "option_delta = Index.from_name(index, series, '5yr')\n", "option_delta.spread = 62\n", "option_delta.notional = 1\n", "option1 = BlackSwaption(option_delta, date(2018, 7, 19), 75, option_type=\"payer\")\n", "option1.sigma = .52\n", "option1.direction = 'Long'\n", "option1.notional = 2_000_000_000\n", "\n", "#If we have two options instead of just one\n", "option2 = BlackSwaption(option_delta, date(2018, 7, 19), 90, option_type=\"payer\")\n", "option2.sigma = .68\n", "option2.direction = 'Long'\n", "option2.notional = 6_000_000_000\n", "\n", "option3 = BlackSwaption(option_delta, date(2018, 12, 19), 55, option_type=\"receiver\")\n", "option3.sigma = .373\n", "option3.direction = 'Short'\n", "option3.notional = 5_000_000_000\n", "\n", "#portf = Portfolio([option1, option_delta])\n", "portf = Portfolio([option1, option2, option3, option_delta])\n", "portf.value_date = value_date\n", "portf.reset_pv()\n", "#Run Swaption sensitivities\n", "#Set Shock range\n", "shock_min = -.5\n", "shock_max = 1.25\n", "spread_shock = np.arange(shock_min, shock_max, 0.05)\n", "#Set Date range\n", "earliest_expiry = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date\n", "date_range = pd.bdate_range(value_date, earliest_expiry - pd.offsets.BDay(), freq='10B')\n", "#Setup Vol Surface\n", "vs = BlackSwaptionVolSurface(index,series, value_date=value_date)\n", "ps = ProbSurface(index,series, value_date=value_date)\n", "vol_surface = vs[vs.list(option_type='payer')[-1]]\n", "swaption_scens = run_portfolio_scenarios(portf, date_range, spread_shock, np.array([0]),\n", " vol_surface, params=[\"pnl\", \"delta\"])\n", "#swaption delta is in protection terms: switch to risk terms\n", "swaption_scens.delta = -swaption_scens.delta\n", "\n", "notional = 30_000_000_000\n", "t = bkt.TrancheBasket('IG', '29', '3yr')\n", "t.build_skew()\n", "#get back to 17bps, .36 delta\n", "port_spread = 67\n", "#t.rho = np.array([np.nan, 0.39691196, 0.48904597, 0.8, np.nan])\n", "t.tweak(port_spread)\n", "spread_range = (1+ spread_shock) * port_spread\n", "tranches_scens = run_tranche_scenarios_rolldown(t, spread_range, date_range, corr_map=False)\n", "tranches_scens = notional*tranches_scens.xs('15-100', axis=1, level=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#IG Bullish Risk Reversal vs. shorting IG 7-15 risk\n", "index = 'IG'\n", "series = 30\n", "option_delta = Index.from_name(index, series, '5yr', value_date)\n", "option_delta.spread = 62\n", "option1 = BlackSwaption(option_delta, date(2018, 9, 19), 60, option_type=\"receiver\")\n", "option2 = BlackSwaption(option_delta, date(2018, 9, 19), 90, option_type=\"payer\")\n", "option1.sigma = .344\n", "option2.sigma = .585\n", "option1.notional = 200_000_000\n", "option2.notional = 400_000_000\n", "option1.direction = 'Long'\n", "option2.direction = 'Short'\n", "option_delta.notional = 1\n", "option_delta.direction = 'Seller' if option_delta.notional > 0 else 'Buyer'\n", "option_delta.notional = abs(option_delta.notional)\n", "portf = Portfolio([option1, option2, option_delta])\n", "#Plot Scenarios Inputs: Portfolio, spread shock tightening%, spread shock widening%, snapshot period)\n", "portf\n", "\n", "portf.reset_pv()\n", "#Run Swaption sensitivities\n", "#Set Shock range\n", "shock_min = -.5\n", "shock_max = 1\n", "spread_shock = np.arange(shock_min, shock_max, 0.1)\n", "#Set Date range\n", "earliest_expiry = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date\n", "date_range = pd.bdate_range(value_date, earliest_expiry - pd.offsets.BDay(), freq='10B')\n", "#Setup Vol Surface\n", "vs = BlackSwaptionVolSurface(index,series, value_date=value_date)\n", "ps = ProbSurface(index,series, value_date=value_date)\n", "vol_surface = vs[vs.list(option_type='payer')[-1]]\n", "swaption_scens = run_portfolio_scenarios(portf, date_range, spread_shock, np.array([0]),\n", " vol_surface, params=[\"pnl\", \"delta\"])\n", "#swaption delta is in protection terms: switch to risk terms\n", "swaption_scens.delta = -swaption_scens.delta\n", "\n", "notional = -100_000_000\n", "t = bkt.TrancheBasket('IG', '29', '5yr')\n", "t.build_skew()\n", "spread_range = (1+ spread_shock) * option_delta.spread\n", "tranches_scens = run_tranche_scenarios_rolldown(t, spread_range, date_range, corr_map=False)\n", "tranches_scens = notional*tranches_scens.xs('7-15', axis=1, level=1)\n", "\n", "#Create snapshot of the the first scenario date\n", "total_scens = swaption_scens.reset_index().merge(tranches_scens.reset_index(), \n", " left_on=['date', 'spread'], \n", " right_on=['date', 'spread_range'], \n", " suffixes=['_s', '_t'])\n", "total_scens['pnl'] = total_scens['pnl_s'] + total_scens['pnl_t']\n", "total_scens['delta'] = total_scens['delta_s'] + total_scens['delta_t']\n", "total_scens_single_date = total_scens.set_index('date').xs(date_range[0])\n", "total_scens_single_date = total_scens_single_date.set_index('spread', drop=True)\n", "\n", "#tranche positions delta at different spreads\n", "ax = total_scens_single_date.delta_t.plot(title = 'delta vs. spread levels')\n", "ax.ticklabel_format(style='plain')\n", "plt.tight_layout()\n", "\n", "#Tranche + Swaptions positions delta at different spreads\n", "ax1 = total_scens_single_date.delta.plot()\n", "ax1.ticklabel_format(style='plain')\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dawnengine.dispose()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.7.0" } }, "nbformat": 4, "nbformat_minor": 2 }