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{
"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, Index, BlackSwaptionVolSurface, Portfolio, ProbSurface\n",
"from db import dbengine\n",
"from datetime import date\n",
"from graphics import plot_time_color_map\n",
"\n",
"dawnengine = dbengine('dawndb')\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 = Index.from_name(index, series, '5yr')\n",
"option_delta.spread = 65\n",
"option1 = BlackSwaption(option_delta, date(2018, 10, 17), 60, option_type=\"payer\")\n",
"option1.sigma = .398\n",
"option1.direction = 'Long'\n",
"option2 = BlackSwaption(option_delta, date(2018, 10, 17), 100, option_type=\"payer\")\n",
"option2.sigma = .609\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",
"portf = Portfolio([option1, option2, option_delta])\n",
"portf.value_date = value_date\n",
"portf.reset_pv()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Run Swaption sensitivities\n",
"#Set Shock range\n",
"shock_min = -.3\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='20B')\n",
"#date_range = [earliest_expiry - pd.offsets.BDay()]\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Get current Tranche positions\n",
"sql_string = \"select * from list_tranche_marks(%s)\"\n",
"pos = pd.read_sql_query(sql_string, dawnengine, params=(value_date,), parse_dates=['maturity'])\n",
"tranche_port = []\n",
"for i, r in pos.iterrows():\n",
" tranche_port.append(bkt.TrancheBasket(r.p_index, r.p_series, '5yr'))\n",
" tranche_port[i].build_skew()\n",
"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)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#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": [
"dawnengine.dispose()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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"file_extension": ".py",
"mimetype": "text/x-python",
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|
