{
 "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": []
  }
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