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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import datetime\n",
"import pandas as pd\n",
"import serenitas.analytics\n",
"import numpy as np\n",
"\n",
"from pandas.tseries.offsets import BDay, BMonthEnd\n",
"from serenitas.analytics.scenarios import run_portfolio_scenarios\n",
"from risk.portfolio import build_portfolio, generate_vol_surface\n",
"from pathlib import Path\n",
"\n",
"from serenitas.analytics.index_data import load_all_curves\n",
"from serenitas.utils.db import serenitas_pool"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run_date = datetime.date.today()\n",
"serenitas.analytics._local = False\n",
"base_dir = Path('/home/serenitas/Daily/Risk/')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run credit spread scenarios"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"scenarios = []\n",
"for position_date in pd.date_range((datetime.date.today() - BMonthEnd(6)), (datetime.date.today() - BMonthEnd()), freq=\"BM\"):\n",
" spread_date = position_date\n",
" spread_shock = np.array([-100., -25., 1., +25. , 100.])\n",
" serenitas.analytics.init_ontr(spread_date)\n",
" spread_shock /= serenitas.analytics._ontr['HY'].spread\n",
" portf, _ = build_portfolio(position_date.date(), spread_date.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', 'hy_equiv'],\n",
" spread_shock=spread_shock,\n",
" vol_shock=[0.0],\n",
" corr_shock=[0.0],\n",
" vol_surface=vol_surface)\n",
"\n",
" strategies = {}\n",
" strategies['options'] = ['HYOPTDEL', 'HYPAYER', 'HYREC', \n",
" 'IGOPTDEL', 'IGPAYER', 'IGREC']\n",
" strategies['tranches'] = ['HYSNR', 'HYMEZ', 'HYINX', 'HYEQY', \n",
" 'IGSNR', 'IGMEZ', 'IGINX', 'IGEQY', \n",
" 'EUSNR', 'EUMEZ', 'EUINX', 'EUEQY', \n",
" 'XOSNR', 'XOMEZ', 'XOINX', 'XOEQY', \n",
" 'BSPK']\n",
"\n",
" scens = scens.xs((0.0, 0.0), level=['vol_shock', 'corr_shock'])\n",
" scens.columns.names=['strategy', 'trade_id', 'scen_type']\n",
"\n",
" results = {}\n",
" for i, g in scens.groupby(level='scen_type', axis =1):\n",
" temp = g.groupby(level='strategy', axis =1).sum()\n",
" for key, item in strategies.items():\n",
" exist_columns = set(temp.columns).intersection(item)\n",
" temp[key] = temp[exist_columns].sum(axis=1)\n",
" temp.drop(exist_columns, axis=1, inplace=True)\n",
" temp['total'] = temp.sum(axis = 1)\n",
" results[i] = temp\n",
" scenarios.append(pd.concat(results))\n",
"pd.concat(scenarios).to_csv(base_dir / f\"csscen_{run_date:%Y%m%d}.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Jump to default"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"for position_date in pd.date_range((datetime.date.today() - BMonthEnd(2)), (datetime.date.today() - BMonthEnd(1)), freq=\"BM\"):\n",
" spread_date = position_date\n",
" _, portf = build_portfolio(position_date.date(), spread_date.date())\n",
" jtd = portf.jtd_single_names()\n",
" conn = serenitas_pool.getconn()\n",
" surv_curves = load_all_curves(conn, spread_date.date())\n",
" serenitas_pool.putconn(conn)\n",
" surv_curves['spread'] = surv_curves['curve'].apply(lambda sc: sc.to_series(forward=False)[5] * (1-sc.recovery_rates[5]))\n",
" jtd_sabo = jtd[[jtd.columns[0]]].join(surv_curves.groupby(level=0).first()[['name', 'company_id', 'spread']])\n",
" jtd_sabo.columns = ['jtd', 'name', 'company_id', 'spread']\n",
" jtd_sabo = jtd_sabo.groupby(['company_id', 'name']).sum()\n",
" jtd_sabo.to_csv(base_dir / f\"jtd_{position_date:%Y%m%d}.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.1 64-bit",
"language": "python",
"name": "python39164bitf8da796bd4214fb9a205dc5a90db6a8a"
},
"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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},
"nbformat": 4,
"nbformat_minor": 4
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|
