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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import datetime\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"from graphics import plot_time_color_map, plot_color_map\n",
"from analytics import Swaption, BlackSwaption, BlackSwaptionVolSurface, Index, Portfolio\n",
"from analytics.scenarios import run_swaption_scenarios, run_index_scenarios, run_portfolio_scenarios\n",
"from scipy.interpolate import SmoothBivariateSpline\n",
"from db import dbengine"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Delta Chart: Red = Long Risk, Blue = Short Risk"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def plot_trade_scenarios(portf, shock_min=-.15, shock_max=.2, period=-1, vol_time_roll=True):\n",
" portf.reset_pv()\n",
" earliest_date = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date\n",
" date_range = pd.bdate_range(portf.indices[0].value_date,\n",
" earliest_date - pd.tseries.offsets.BDay(), freq='3B')\n",
" vol_shock = np.arange(-0.15, 0.3, 0.01)\n",
" spread_shock = np.arange(shock_min, shock_max, 0.01)\n",
" index = portf.indices[0].name.split()[1]\n",
" series = portf.indices[0].name.split()[3][1:]\n",
" vs = BlackSwaptionVolSurface(index, series, value_date=portf.indices[0].value_date)\n",
" vol_surface = vs[vs.list(option_type='payer')[-1]]\n",
"\n",
" df = run_portfolio_scenarios(portf, date_range, spread_shock, vol_shock, vol_surface,\n",
" params=[\"pnl\",\"delta\"])\n",
"\n",
" hy_plot_range = 100 + (500 - portf.indices[0].spread * (1 + spread_shock)) * \\\n",
" abs(portf.indices[0].DV01) / portf.indices[0].notional * 100\n",
"\n",
" shock = hy_plot_range if index == 'HY' else portf.indices[0].spread * (1 + spread_shock)\n",
"\n",
" plot_time_color_map(df[round(df.vol_shock,2)==0], shock, 'pnl', index=index)\n",
" plot_time_color_map(df[round(df.vol_shock,2)==.2], shock, 'pnl', index=index)\n",
" plot_color_map(df.loc[date_range[period]], shock, vol_shock, 'pnl', index=index)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Ad hoc\n",
"option_delta = Index.from_name('IG', 30, '5yr', value_date=datetime.date(2018, 5, 17))\n",
"option_delta.spread = 61\n",
"option1 = BlackSwaption(option_delta, datetime.date(2018, 8, 15), 60, option_type=\"payer\")\n",
"option2 = BlackSwaption(option_delta, datetime.date(2018, 8, 15), 80, option_type=\"payer\")\n",
"option3 = BlackSwaption(option_delta, datetime.date(2018, 8, 15), 80, option_type=\"payer\")\n",
"option1.sigma = .381\n",
"option2.sigma = .545\n",
"option3.sigma = .69\n",
"option1.notional = 100_000_000\n",
"option2.notional = 300_000_000\n",
"option3.notional = 1\n",
"option1.direction = 'Long'\n",
"option2.direction = 'Short'\n",
"option3.direction = 'Long'\n",
"#option_delta.notional = 1\n",
"option_delta.notional = option1.notional * option1.delta + option2.notional * option2.delta + option3.notional * option3.delta\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, option3, option_delta])\n",
"#Plot Scenarios Inputs: Portfolio, spread shock tightening%, spread shock widening%, snapshot period)\n",
"portf"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot_trade_scenarios(portf, -.15, .8, -4, vol_time_roll=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Dec Jan 2017 Trade\n",
"option_delta = Index.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])\n",
"#plot_trade_scenarios(portf)\n",
"\n",
"#Feb 2017: Sell May Buy April Calendar Trade\n",
"option_delta = Index.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])\n",
"#plot_trade_scenarios(portf)\n",
"\n",
"#April 2017: Sell May Buy June Calendar Trade\n",
"option_delta = Index.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])\n",
"#plot_trade_scenarios(portf)\n",
"\n",
"#June July 2017 Calendar Trade\n",
"option_delta_pf = Index.from_tradeid(874)\n",
"option_delta2_pf = Index.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])\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 = Index.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])\n",
"#plot_trade_scenarios(portf)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Look at steepness of volatility - 90 days, .75 vs .25 payer deltas\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 = 'IG'\"\n",
"df = pd.read_sql_query(sql_str, dbengine('serenitasdb'), \n",
" index_col=['quotedate'], parse_dates={'quotedate': {'utc': True}})\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(90, .75) - f(90, .25))[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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
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},
"language_info": {
"codemirror_mode": {
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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|
