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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from analytics.curve_trades import curve_pos\n",
"from analytics import Index, Portfolio\n",
"\n",
"import datetime\n",
"import exploration.VaR as var\n",
"import pandas as pd"
]
},
{
"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))\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))\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)\n",
"df = df[df.strategy == 'HEDGE_MBS']\n",
"portf = Portfolio([Index.from_name(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_EX_DEF.csv\"\n",
"margin_df = pd.read_csv(\"/home/serenitas/Daily/SG_reports/\" + filename, index_col='Currency')\n",
"morg_hedge_im = mort_hedge_var + mort_hedge_var/(mort_hedge_var + ig_curve_var) * margin_df.loc[('USD', 'SG IMR')]\n",
"morg_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": [
"#95%tile \n",
"df, spread, dur = var.rel_spread_diff(report_date)\n",
"stress = pd.DataFrame()\n",
"stress.at[('2SD_widen', 'spread')] = df.quantile(.975) \n",
"stress.at[('2SD_tighten', 'spread')] = df.quantile(.025) \n",
"stress.at[('worst_widen', 'spread')] = df.max()\n",
"stress['pts'] = -stress * spread * dur/100\n",
"stress['nav_impact'] = bond_HY_equiv * stress['pts']\n",
"stress"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"port.cleared_cds_margins(report_date, percentile)"
]
},
{
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
