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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\n",
+    "from utils.db import dbconn\n",
+    "from datetime import date\n",
+    "\n",
+    "value_date = (date.today() - pd.offsets.BDay(1)).date()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index_type = 'HY'\n",
+    "series = 35\n",
+    "tenor = '5yr'\n",
+    "value_date = date.today()\n",
+    "price = 103.875\n",
+    "at_det = [0, 15, 25, 35, 100] if index_type == 'HY' else ['0', '3', '7', '15', '100']\n",
+    "tranche_prices= [41.4, 90.6, 109.6, 119.7]\n",
+    "\n",
+    "#Build another skew to price this new series\n",
+    "base_index = TrancheBasket(\"HY\", 33, \"5yr\")\n",
+    "base_index.tweak()\n",
+    "base_index.build_skew()\n",
+    "skew=base_index.skew"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = []\n",
+    "for i in range(3):\n",
+    "    #set up\n",
+    "    rho_floor = tranche.rho[1] if i > 0 else 0.2\n",
+    "    rho_min = rho_floor\n",
+    "    rho_max =  rho_floor + 0.4\n",
+    "    tranche = DualCorrTranche(index_type, series, tenor, attach=at_det[i], detach=at_det[i+1], corr_attach = rho_floor, corr_detach = rho_min + (rho_max -rho_min)/2, tranche_running = 500, value_date=value_date)\n",
+    "    tranche._index.tweak([price])\n",
+    "    #now loop to find it\n",
+    "    for j in range(20):\n",
+    "        if tranche.price <= tranche_prices[i]:\n",
+    "            rho_min = tranche.rho[1]\n",
+    "        else:\n",
+    "            rho_max = tranche.rho[1]\n",
+    "        tranche.rho[1] = rho_min + (rho_max - rho_min)/2\n",
+    "    results.append([tranche.rho[1], tranche.price, tranche.delta, tranche.gamma, tranche.theta(skew=skew), tranche.delta * float(tranche._index.theta())])\n",
+    "ss_corr = tranche.rho[1]\n",
+    "tranche = DualCorrTranche(index_type, series, tenor, attach=at_det[i+1], detach=100, corr_attach = ss_corr, corr_detach = .999, tranche_running = 500, value_date=value_date)\n",
+    "tranche._index.tweak([price])\n",
+    "results.append([tranche.rho[1], tranche.price, tranche.delta, tranche.gamma, tranche.theta(skew=skew), tranche.delta * float(tranche._index.theta())])\n",
+    "results = pd.DataFrame(results, columns = ['corr', 'price', 'delta', 'gamma', 'theta', 'delta * index_theta'])\n",
+    "results['theta_per_delta'] = results['theta'] / results['delta']\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Using another skew\n",
+    "mapped_results = []\n",
+    "for i in range(3):\n",
+    "    tranche = DualCorrTranche(index_type, series, tenor, attach=at_det[i], detach=at_det[i+1], corr_attach = np.nan, corr_detach = 0.1, tranche_running = 500, value_date=value_date)\n",
+    "    tranche._index.tweak([price])\n",
+    "    tranche.mark(skew=skew)\n",
+    "    mapped_results.append([tranche.rho[1], tranche.price, tranche.delta, tranche.gamma, tranche.theta(skew=skew), tranche.corr01/tranche.notional])\n",
+    "ss_corr = tranche.rho[1]\n",
+    "tranche = DualCorrTranche(index_type, series, tenor, attach=at_det[i+1], detach=100, corr_attach = ss_corr, corr_detach = .999, tranche_running = 500, value_date=value_date)\n",
+    "tranche._index.tweak([price])\n",
+    "mapped_results.append([tranche.rho[1], tranche.price, tranche.delta, tranche.gamma, tranche.theta(skew=skew), np.nan])\n",
+    "mapped_results = pd.DataFrame(mapped_results, columns = ['corr', 'price', 'delta', 'gamma', 'theta', 'corr01'])\n",
+    "mapped_results['theta_per_delta'] = mapped_results['theta'] / mapped_results['delta']\n",
+    "mapped_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.1 64-bit",
+   "language": "python",
+   "name": "python38164bitc40c8740e5d542d7959acb14be96f4f3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
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+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}