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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import datetime\n",
"import globeop_reports as go\n",
"import pandas as pd\n",
"import analytics\n",
"import numpy as np\n",
"\n",
"from pandas.tseries.offsets import BDay, MonthEnd\n",
"from analytics.scenarios import run_portfolio_scenarios\n",
"from utils.db import dbconn\n",
"from risk.portfolio import build_portfolio, generate_vol_surface\n",
"from analytics.basket_index import BasketIndex"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Set dates\n",
"position_date = (datetime.date.today() - MonthEnd(1)).date()\n",
"spread_date = position_date\n",
"analytics._local = False\n",
"analytics.init_ontr(spread_date)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"################################### Run Credit Spread scenarios\n",
"spread_shock = np.array([-100., -25., 1., +25. , 100.])\n",
"spread_shock /= analytics._ontr['HY'].spread\n",
"portf, _ = build_portfolio(position_date, spread_date)\n",
"vol_surface = generate_vol_surface(portf, 5)\n",
"portf.reset_pv()\n",
"scens = run_portfolio_scenarios(portf, date_range=[pd.Timestamp(spread_date)], params=['pnl'],\n",
" spread_shock=spread_shock,\n",
" vol_shock=[0.0],\n",
" corr_shock=[0.0],\n",
" vol_surface=vol_surface)\n",
"\n",
"pnl = scens.xs('pnl', axis=1, level=2)\n",
"pnl = pnl.xs((0.0, 0.0), level=['vol_shock', 'corr_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",
"tranches = ['HYMEZ', 'HYINX', 'HYEQY', 'IGMEZ', 'IGINX', 'IGEQY', 'IGSNR', 'IGINX', 'BSPK', 'XOMEZ', 'XOINX', 'EUMEZ']\n",
"hedges = ['HEDGE_CLO', 'HEDGE_MAC', 'HEDGE_MBS']\n",
"\n",
"synthetic =pd.DataFrame()\n",
"synthetic['options'] = scenarios[set(scenarios.columns).intersection(options)].sum(axis=1)\n",
"synthetic['tranches'] = scenarios[set(scenarios.columns).intersection(tranches)].sum(axis=1)\n",
"synthetic['curve_trades'] = scenarios['curve_trades']\n",
"synthetic['total'] = synthetic.sum(axis = 1)\n",
"nav = go.get_net_navs()\n",
"scenarios.sum(axis=1)\n",
"scenarios.sum(axis=1).to_clipboard()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"################################### JTD\n",
"_, portf = build_portfolio(position_date, spread_date)\n",
"jtd_i = []\n",
"for t in portf.indices:\n",
" bkt = BasketIndex(t.index_type, t.series, [t.tenor])\n",
" spreads = pd.DataFrame(bkt.spreads() * 10000, index=pd.Index(bkt.tickers, name='ticker'), columns=['spread'])\n",
" jump = pd.merge(spreads, bkt.jump_to_default() * t.notional, left_index=True, right_index=True)\n",
" jtd_i.append(jump.rename(columns={jump.columns[1]: 'jtd'}))\n",
"jtd_t = []\n",
"for t in portf.tranches:\n",
" jump = pd.concat([t.singlename_spreads().reset_index(['seniority', 'doc_clause'], drop=True), t.jump_to_default().rename('jtd')], axis=1)\n",
" jtd_t.append(jump.drop(['weight', 'recovery'], axis=1))\n",
"\n",
"ref_names = pd.read_sql_query(\"select ticker, referenceentity from refentity\", dbconn('serenitasdb'), index_col='ticker')\n",
"jump = pd.concat([pd.concat(jtd_t), pd.concat(jtd_i)])\n",
"jump = jump.merge(ref_names, left_index=True, right_index=True)\n",
"jump = jump.groupby('referenceentity').agg({'spread': np.mean, 'jtd': np.sum}).sort_values(by='jtd', ascending=True)\n",
"jump.to_clipboard()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.8.1 64-bit",
"language": "python",
"name": "python38164bitc40c8740e5d542d7959acb14be96f4f3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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|
