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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from analytics.curve_trades import curve_pos, on_the_run\n",
"from analytics.index_data import get_index_quotes\n",
"from analytics.scenarios import run_portfolio_scenarios\n",
"from analytics import Swaption, BlackSwaption, CreditIndex, BlackSwaptionVolSurface, Portfolio, ProbSurface, DualCorrTranche\n",
"from db import dbconn\n",
"\n",
"import datetime\n",
"import exploration.VaR as var\n",
"import pandas as pd\n",
"\n",
"conn = dbconn('dawndb')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"date = (datetime.date.today() - pd.tseries.offsets.BDay(1)).date()\n",
"report_date = (date + pd.tseries.offsets.BMonthEnd(-1)).date()\n",
"index_type = \"IG\"\n",
"quantile = .025"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#IG Curve VaR\n",
"portf = curve_pos(date, index_type)\n",
"ig_curve_var = abs(var.hist_var(portf, quantile=quantile, years=5))\n",
"ig_curve_var"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#EU Curve VaR\n",
"index_type = \"EU\"\n",
"portf = curve_pos(date, index_type)\n",
"eu_curve_var = abs(var.hist_var(portf, quantile=quantile, years=5))\n",
"eu_curve_var"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Mortgage Hedge VaR - use IG spread relative move for VaR\n",
"df = var.get_pos(date, 'HEDGE_MBS')\n",
"portf = Portfolio([CreditIndex(row.p_index, row.p_series, row.tenor,\n",
" report_date, -row.notional)\n",
" for row in df[['p_index', 'tenor', 'p_series', 'notional']].\n",
" itertuples(index=False)])\n",
"portf.mark()\n",
"mort_hedge_var = abs(var.hist_var(portf, index_type = \"IG\", quantile=quantile, years=3))\n",
"mort_hedge_var"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Import the IM at the FCM account: calculate the IM share of different strategies as a share of VaR\n",
"filename = date.strftime('%Y%m%d') + \"_OTC_MARGIN.csv\"\n",
"margin_df = pd.read_csv(\"/home/serenitas/Daily/SG_reports/\" + filename, index_col='System Currency')\n",
"mortg_hedge_im = mort_hedge_var + mort_hedge_var/(mort_hedge_var + ig_curve_var) * margin_df.loc[('USD', 'SG Settlement Margin')]\n",
"mortg_hedge_im"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Oct ME Bond HY Equiv\n",
"bond_HY_equiv = -.12088\n",
"percentile = .95"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Calculate amount of stress for reports\n",
"df = get_index_quotes('HY', list(range(on_the_run('HY') - 10, on_the_run('HY') + 1)),\n",
" tenor=['5yr'], years=5)\n",
"df = df.xs('5yr', level='tenor')['close_spread'].groupby(['date', 'series']).last()\n",
"\n",
"widen, tighten = [], []\n",
"#approximately 1,3,6 months move (22 each months)\n",
"for days in [22, 66, 132]: \n",
" calc = df.unstack().pct_change(freq= str(days)+'B').stack().groupby('date').last()\n",
" widen.append(calc.max())\n",
" tighten.append(calc.min())\n",
"pd.DataFrame([widen, tighten], columns=['1M', '3M', '6M'], index=['widen', 'tighten'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Current tranche and swaptions positions\n",
"t_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",
" \"AND trade_date <= %s\")\n",
"swaption_sql_string = (\"select id, security_desc from swaptions where date(expiration_date) \"\n",
" \"> %s and swap_type = 'CD_INDEX_OPTION' \"\n",
" \"AND trade_date <= %s\")\n",
"index_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' AND termination_cp IS null \"\n",
" \"AND folder = 'IGOPTDEL' OR folder = 'HYOPTDEL' \"\n",
" \"AND trade_date <= %s\")\n",
"conn = dbconn('dawndb')\n",
"with conn.cursor() as c:\n",
" #Get Tranche Trade Ids\n",
" c.execute(t_sql_string, (date,))\n",
" t_trade_ids = [dealid for dealid, ntl in c if ntl != 0]\n",
" #Get Swaption Trade Ids\n",
" c.execute(swaption_sql_string, (date, date))\n",
" swaption_trades = c.fetchall()\n",
" #Get Index/deltas Trade Ids\n",
" c.execute(index_sql_string, (date,))\n",
" index_trade_ids = [dealid for dealid, ntl in c if ntl != 0]\n",
" \n",
"portf = Portfolio([DualCorrTranche.from_tradeid(dealid) for dealid in t_trade_ids],\n",
" t_trade_ids)\n",
"for row in swaption_trades:\n",
" option_delta = CreditIndex(row[1].split()[1], row[1].split()[3][1:], '5yr', date)\n",
" option_delta.mark()\n",
" portf.add_trade(BlackSwaption.from_tradeid(row[0], option_delta), 'opt_' + str(row[0]))\n",
"for index_id in index_trade_ids:\n",
" portf.add_trade(CreditIndex.from_tradeid(index_id), 'index_' + str(index_id))\n",
" \n",
"#Update manually - positive notional = long risk\n",
"non_trancheSwap_risk_notional = 33763230\n",
"portf.add_trade(CreditIndex('HY', on_the_run('HY'), '5yr', value_date = date, notional = -non_trancheSwap_risk_notional), 'port')\n",
" \n",
"portf.value_date = date\n",
"portf.mark()\n",
"portf.reset_pv()\n",
" \n",
"vs = BlackSwaptionVolSurface(portf.swaptions[0].index.index_type, portf.swaptions[0].index.series, value_date=date)\n",
"vol_surface = vs[vs.list(option_type='payer')[-1]]\n",
"vol_shock = [0]\n",
"corr_shock = [0]\n",
"spread_shock = widen + tighten\n",
"date_range = [pd.Timestamp(date)]\n",
"\n",
"scens = run_portfolio_scenarios(portf, date_range, params=[\"pnl\"],\n",
" spread_shock=spread_shock,\n",
" vol_shock=vol_shock,\n",
" corr_shock=corr_shock,\n",
" vol_surface=vol_surface)\n",
"\n",
"scens.sum(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"var.cleared_cds_margins(report_date)"
]
}
],
"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",
"nbconvert_exporter": "python",
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|
