diff options
Diffstat (limited to 'python')
| -rw-r--r-- | python/analytics/tranche_basket.py | 27 | ||||
| -rw-r--r-- | python/notebooks/Tranche calculator.ipynb | 57 |
2 files changed, 39 insertions, 45 deletions
diff --git a/python/analytics/tranche_basket.py b/python/analytics/tranche_basket.py index abc91bd7..b16c7217 100644 --- a/python/analytics/tranche_basket.py +++ b/python/analytics/tranche_basket.py @@ -1425,5 +1425,28 @@ class MarkitTrancheBasket(TrancheBasket): class ManualTrancheBasket(TrancheBasket): """TrancheBasket with quotes manually provided""" - def _set_tranche_quotes(self, value_date, tranche_quotes): - self.tranche_quotes = tranche_quotes + def _set_tranche_quotes(self, value_date, ref, quotes): + if self.index_type == "HY": + detach = [15, 25, 35, 100] + elif self.index_type == "IG": + detach = [3, 7, 15, 100] + elif self.index_type == "EU": + detach = [3, 6, 12, 100] + else: + detach = [10, 20, 35, 100] + coupon = 500 if (self.index_type == "HY" or self.index_type == "XO") else 100 + if self.index_type == "HY": + ref_type1 = "indexrefprice" + ref_type2 = "indexrefspread" + else: + ref_type1 = "indexrefspread" + ref_type2 = "indexrefprice" + self.tranche_quotes = pd.DataFrame( + { + "detach": np.array(detach), + "trancheupfrontmid": np.array(quotes), + "trancherunningmid": np.full(4, coupon), + ref_type1: np.full(4, ref), + ref_type2: np.full(4, None), + } + ) diff --git a/python/notebooks/Tranche calculator.ipynb b/python/notebooks/Tranche calculator.ipynb index 8649d43e..394618a3 100644 --- a/python/notebooks/Tranche calculator.ipynb +++ b/python/notebooks/Tranche calculator.ipynb @@ -6,14 +6,10 @@ "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 analytics import TrancheBasket, ManualTrancheBasket\n", "from datetime import date" ] }, @@ -23,29 +19,15 @@ "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)})" + "def display_result(basket_index):\n", + " thetas = basket_index.tranche_thetas()\n", + " result = pd.concat([pd.DataFrame(basket_index.rho[0:4], index=thetas.index, columns=['att_corr']),\n", + " pd.DataFrame(basket_index.tranche_pvs().bond_price, index=thetas.index, columns=['price']),\n", + " basket_index.tranche_deltas(),\n", + " thetas],\n", + " axis=1)\n", + " result['net_theta'] = result.theta - new_index.theta()[0] * result.delta\n", + " return result" ] }, { @@ -57,17 +39,11 @@ "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 = ManualTrancheBasket(index_type, series, \"5yr\", value_date=value_date, ref=104.625, quotes=[43, 92.5, 110, 120.27])\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" + "result = display_result(new_index)\n", + "result " ] }, { @@ -96,12 +72,7 @@ "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 = display_result(new_index)\n", "result" ] }, |