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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(2)), (datetime.date.today() - BMonthEnd(1)), 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'],\n",
" spread_shock=spread_shock,\n",
" vol_shock= [0.0],\n",
" corr_shock=[0.0],\n",
" vol_surface=vol_surface)\n",
" scenarios.append(scens.sum(axis=1))\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": {},
"outputs": [],
"source": [
"for position_date in pd.date_range((datetime.date.today() - BMonthEnd(12)), (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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.1 64-bit",
"language": "python",
"name": "python39164bitf8da796bd4214fb9a205dc5a90db6a8a"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1-final"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
