{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import globeop_reports as go\n",
    "import pandas as pd\n",
    "import analytics\n",
    "import numpy as np\n",
    "\n",
    "from pandas.tseries.offsets import BDay, BMonthEnd\n",
    "from analytics.scenarios import run_portfolio_scenarios\n",
    "from risk.portfolio import build_portfolio, generate_vol_surface\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "position_date = (datetime.date.today() - BMonthEnd(1)).date()\n",
    "spread_date = position_date\n",
    "analytics._local = False\n",
    "analytics.init_ontr(spread_date)\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": [
    "spread_shock = np.array([-100., -25., 1., +25. , 100.])\n",
    "spread_shock /= analytics._ontr['HY'].spread\n",
    "portf, _ = build_portfolio(position_date, spread_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",
    "scens = scens.sum(axis=1)\n",
    "scens.to_csv(base_dir / f\"csscen_{position_date:%Y%m%d}.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jump to default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, portf = build_portfolio(position_date, spread_date)\n",
    "jtd = portf.jtd_single_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from analytics.index_data import load_all_curves\n",
    "from utils.db import serenitas_pool\n",
    "conn = serenitas_pool.getconn()\n",
    "surv_curves = load_all_curves(conn, spread_date)\n",
    "serenitas_pool.putconn(conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "jtd_sabo = jtd[[jtd.columns[0]]].join(surv_curves.groupby(level=0).first()[['name', 'company_id']])\n",
    "jtd_sabo.columns = ['jtd', 'name', 'company_id']\n",
    "jtd_sabo.to_csv(base_dir / f\"jtd_{position_date:%Y%m%d}.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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