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{
"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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
