1 files changed, 17 insertions, 16 deletions
diff --git a/python/notebooks/Single Names Monitoring.ipynb b/python/notebooks/Single Names Monitoring.ipynb
index 8c3c8be1..0c32e617 100644
--- a/python/notebooks/Single Names Monitoring.ipynb
+++ b/python/notebooks/Single Names Monitoring.ipynb
@@ -8,14 +8,12 @@
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
-    "import analytics.basket_index as bkt\n",
+    "from analytics.basket_index import MarkitBasketIndex\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "from quantlib.time.api import Schedule, Rule, Date, Period, WeekendsOnly\n",
-    "from quantlib.settings import Settings\n",
     "from db import dbengine\n",
     "from ipywidgets import widgets\n",
-    "engine  = dbengine('serenitasdb')"
+    "engine = dbengine('serenitasdb')"
    ]
   },
   {
@@ -49,8 +47,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "trade_date = (pd.datetime.today() - pd.offsets.BDay(1)).normalize().date()\n",
-    "index = w.value\n",
+    "trade_date = (pd.datetime.today() - pd.offsets.BDay(1)).date()\n",
+    "index_type = w.value\n",
     "series = w_1.value"
    ]
   },
@@ -61,8 +59,8 @@
    "outputs": [],
    "source": [
     "sql_string = \"select * from index_members(%s, %s)\"\n",
-    "df = pd.read_sql_query(sql_string, engine, params=(index + str(series), trade_date), index_col=['markit_ticker'])\n",
-    "df1 = pd.read_sql_query(sql_string, engine, params=(index + str(series-1), trade_date), index_col=['markit_ticker'])"
+    "df = pd.read_sql_query(sql_string, engine, params=(index_type + str(series), trade_date), index_col=['markit_ticker'])\n",
+    "df1 = pd.read_sql_query(sql_string, engine, params=(index_type + str(series-1), trade_date), index_col=['markit_ticker'])"
    ]
   },
   {
@@ -91,13 +89,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dp_top = pd.DataFrame()\n",
     "date_range = pd.bdate_range(trade_date - 52 * pd.offsets.Week(), trade_date, freq='5B')\n",
+    "index = MarkitBasketIndex(index_type, series, ['5yr'])\n",
+    "default_prob = {}\n",
+    "maturity = np.array([np.datetime64(index.maturities[0]).view('int') + 134774])\n",
     "for d in date_range:\n",
-    "    temp = bkt.MarkitBasketIndex(index, series, ['5yr',], value_date=d.date())\n",
-    "    temp1 = temp.survival_matrix(np.array([np.datetime64(temp.maturities[0]).view('int') + 134774]))\n",
-    "    temp1 = pd.DataFrame(1 - temp1[0], index=temp1[1], columns=[d.date()])\n",
-    "    dp_top = pd.concat([dp_top, temp1], axis=1)"
+    "    index.value_date = d\n",
+    "    surv_prob, tickers = index.survival_matrix(maturity)\n",
+    "    default_prob[d] = pd.Series(1 - np.ravel(surv_prob), index=tickers)\n",
+    "default_prob = pd.concat(default_prob)"
    ]
   },
   {
@@ -107,9 +107,10 @@
    "outputs": [],
    "source": [
     "#Top 20 highest cumulative\n",
-    "dp_top = dp_top.sort_values(trade_date, ascending=False)[0:20]\n",
-    "dp_top = dp_top.join(df1.name).set_index('name', drop=True)\n",
-    "dp_top.iloc[:,-1]"
+    "top20 = default_prob.unstack(-1)[default_prob[trade_date].nlargest(20).index]\n",
+    "top20.index.name='date'\n",
+    "top20.columns.name='tickers'\n",
+    "top20.plot(title=f'market implied default probabilities to {index.maturities[0]}')"
    ]
   },
   {