{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import analytics.tranche_basket as bkt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from analytics.scenarios import run_tranche_scenarios, run_portfolio_scenarios, run_tranche_scenarios_rolldown\n",
    "from analytics import DualCorrTranche, TrancheBasket, ManualTrancheBasket\n",
    "from utils.db import dbconn\n",
    "from datetime import date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_tranche_quotes(index_type, ref, quotes):\n",
    "    if index_type == 'HY':\n",
    "        detach = [15, 25, 35, 100] \n",
    "    elif index_type == 'IG':\n",
    "        detach = [3, 7, 15, 100] \n",
    "    elif index_type == 'EU':\n",
    "        detach = [3, 6, 12, 100] \n",
    "    else:\n",
    "        detach = [10, 20, 35, 100]\n",
    "    coupon = 500 if (index_type == 'HY' or index_type == 'XO') else 100\n",
    "    if index_type == 'HY':\n",
    "        ref_type1 = 'indexrefprice'\n",
    "        ref_type2 = 'indexrefspread'\n",
    "        ref_2 = None\n",
    "    else:\n",
    "        ref_type1 = 'indexrefspread'\n",
    "        ref_type2 = 'indexrefprice'\n",
    "        ref_2 = None\n",
    "    return pd.DataFrame({\"detach\": np.array(detach), \n",
    "                         \"trancheupfrontmid\": np.array(quotes), \n",
    "                         \"trancherunningmid\": np.full(4, coupon),\n",
    "                          ref_type1: np.full(4, ref),\n",
    "                          ref_type2: np.full(4, ref_2)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_type = 'HY'\n",
    "series = 35\n",
    "value_date = date.today()\n",
    "df = build_tranche_quotes(index_type, 104.625, [43, 92.5, 110, 120.27])\n",
    "new_index = ManualTrancheBasket(index_type, series, \"5yr\", value_date=value_date, tranche_quotes=df)\n",
    "new_index.tweak()\n",
    "new_index.build_skew()\n",
    "result = pd.concat([pd.DataFrame(new_index.rho[0:4], index=new_index.tranche_thetas().index, columns=['att_corr']),\n",
    "           pd.DataFrame(new_index.tranche_pvs().bond_price, index=new_index.tranche_thetas().index, columns=['price']),\n",
    "           new_index.tranche_deltas(),\n",
    "           new_index.tranche_thetas()],\n",
    "           axis=1)\n",
    "result['net_theta'] = result.theta - new_index.theta()[0] * result.delta\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Implied SS\n",
    "implied_ss = ((new_index.index_pv().bond_price - new_index.accrued()) -\n",
    "              ((new_index.K[1]-new_index.K[0]) * result.price[0] +\n",
    "              (new_index.K[2]-new_index.K[1]) * result.price[1] +\n",
    "              (new_index.K[3]-new_index.K[2]) * result.price[2]))/(new_index.K[4] - new_index.K[3])\n",
    "implied_ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Build previous series skew to price this new series\n",
    "base_index = TrancheBasket(index_type, series-2, \"5yr\")\n",
    "base_index.tweak()\n",
    "base_index.build_skew()\n",
    "\n",
    "new_index.rho = base_index.map_skew(new_index)\n",
    "result = pd.concat([pd.DataFrame(new_index.rho[0:4], index=new_index.tranche_thetas().index, columns=['att_corr']),\n",
    "           pd.DataFrame(new_index.tranche_pvs().bond_price, index=new_index.tranche_thetas().index, columns=['price']),\n",
    "           new_index.tranche_deltas(),\n",
    "           new_index.tranche_thetas()],\n",
    "           axis=1)\n",
    "result['net_theta'] = result.theta - new_index.theta()[0] * result.delta\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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