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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# better formatting for large floats\n",
"import pandas as pd\n",
"pd.options.display.float_format = \"{:,.2f}\".format"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from risk.swaptions import get_swaption_portfolio\n",
"import datetime\n",
"from utils.db import dbconn\n",
"from analytics import init_ontr\n",
"conn = dbconn('dawndb')\n",
"conn.autocommit = True\n",
"init_ontr()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"portf = get_swaption_portfolio(datetime.date.today(), conn, source_list=['GS'])\n",
"portf"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = portf._todf()\n",
"positions = df.set_index(\"Index\")[[\"Delta\", \"Notional\"]].prod(axis=1).groupby(level=\"Index\").sum()\n",
"positions.name = 'current_delta'\n",
"gamma = df.set_index(\"Index\")[[\"Gamma\", \"Notional\"]].prod(axis=1).groupby(level=\"Index\").sum()\n",
"gamma.name = 'gamma'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"hedges = pd.read_sql_query(\"SELECT security_desc, notional FROM list_cds_positions_by_strat(%s) \"\n",
" \"WHERE folder in ('IGOPTDEL', 'HYOPTDEL')\",\n",
" conn, params=(datetime.date.today(),))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def f(s):\n",
" l = s.split(\" \")\n",
" return f\"{l[1]}{l[3][1:]} {l[4].lower()}r\"\n",
"\n",
"hedges[\"Index\"] = hedges[\"security_desc\"].apply(f)\n",
"hedges = hedges.rename(columns={\"notional\": \"current hedge\"})\n",
"hedges = hedges.set_index(\"Index\")[\"current hedge\"]\n",
"hedges = hedges.reindex(positions.index, fill_value=0.)\n",
"risk = pd.concat([hedges, positions, gamma], axis=1)\n",
"risk['net_delta'] = risk[\"current hedge\"] + risk.current_delta\n",
"risk"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from analytics.scenarios import run_portfolio_scenarios\n",
"from analytics import BlackSwaptionVolSurface, CreditIndex\n",
"import analytics\n",
"import datetime\n",
"import numpy as np\n",
"\n",
"today = datetime.datetime.now()\n",
"yesterday = datetime.date.today() - pd.offsets.BDay()\n",
"\n",
"portf = get_swaption_portfolio(yesterday, conn, source_list=['GS'])\n",
"for i, amt in hedges.iteritems():\n",
" portf.add_trade(CreditIndex(i[:2], i[2:4], '5yr', value_date=yesterday, notional=amt), ('delta', i))\n",
"\n",
"vol_surface = {}\n",
"for trade in portf.swaptions:\n",
" vs = BlackSwaptionVolSurface(trade.index.index_type, trade.index.series, \n",
" value_date=today.date(), interp_method = \"bivariate_linear\")\n",
" vol_surface[(trade.index.index_type, trade.index.series, trade.option_type)] = vs[vs.list(source='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.mark(interp_method=\"bivariate_linear\", source_list=['GS'])\n",
"portf.reset_pv()\n",
"\n",
"#set ref to today's levels\n",
"portf.value_date = today\n",
"portf.mark(interp_method=\"bivariate_linear\", source_list=['GS'])\n",
"\n",
"spread_shock = np.round(np.arange(-.1, .1, .01), 4)\n",
"scens = run_portfolio_scenarios(portf, [today], params=['pnl', 'hy_equiv', 'sigma'],\n",
" spread_shock=spread_shock,\n",
" vol_shock=[0],\n",
" corr_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', value_date = today)\n",
"ig.mark()\n",
"\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, hy_equiv"
]
},
{
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
