2 files changed, 105 insertions, 0 deletions
diff --git a/python/analytics/__init__.py b/python/analytics/__init__.py
index 031df9cb..e23b7e64 100644
--- a/python/analytics/__init__.py
+++ b/python/analytics/__init__.py
@@ -1,3 +1,4 @@
 from .index import Index, ForwardIndex
 from .option import BlackSwaption, Swaption, VolatilitySurface, ATMstrike
 from .portfolio import Portfolio
+from .basket_index import BasketIndex
diff --git a/python/analytics/basket_index.py b/python/analytics/basket_index.py
new file mode 100644
index 00000000..407f93bd
--- /dev/null
+++ b/python/analytics/basket_index.py
@@ -0,0 +1,104 @@
+import index_data as data
+from dateutil.relativedelta import relativedelta
+from pyisda.credit_index import CreditIndex
+from typing import List
+import pandas as pd
+import datetime
+from scipy.optimize import brentq
+
+class BasketIndex(CreditIndex):
+    index_type: str
+    series: int
+    recovery: float
+    step_in_date: pd.Timestamp
+    value_date: pd.Timestamp
+    tweaks: List[float]
+
+    def __init__(self, index_type: str, series: int, tenors: List[str], *args,
+                 trade_date: pd.Timestamp = pd.Timestamp.today().normalize()):
+        self.index_type = index_type
+        self.series = series
+        if index_type == 'IG':
+            self.recovery = 0.4
+        else:
+            self.recovery = 0.3
+        self.index_quotes = (data.get_index_quotes(index_type, series,
+                                                   tenors, years=None)['closeprice'].
+                             unstack().
+                             groupby(level='date', as_index=False).nth(0).
+                             reset_index(['index', 'series'], drop=True))
+        self.index_desc = pd.read_sql_query("SELECT tenor, maturity, coupon * 1e-4 AS coupon, " \
+                                            "issue_date "\
+                                            "FROM index_maturity " \
+                                            "WHERE index=%s AND series=%s",
+                                            data._serenitas_engine,
+                                            index_col='tenor',
+                                            params=(index_type, series),
+                                            parse_dates=['maturity', 'issue_date'])
+        self.index_quotes.columns = self.index_desc.loc[self.index_quotes.columns, "maturity"]
+        self.index_quotes = 1 - self.index_quotes / 100
+        self.issue_date = self.index_desc.issue_date[0]
+        maturities = self.index_quotes.columns.sort_values().to_pydatetime()
+        self.index_desc = self.index_desc.reset_index().set_index('maturity')
+        curves, args = data.get_singlenames_curves(index_type, series, trade_date)
+        _, jp_yc, _, step_in_date, value_date, _ = args
+        self.yc = jp_yc
+        self.step_in_date = step_in_date
+        self.value_date = value_date
+        self._trade_date = trade_date
+        self.tweaks = []
+        super().__init__(self.issue_date, maturities, curves)
+
+    @property
+    def trade_date(self):
+        return self._trade_date
+
+    @trade_date.setter
+    def trade_date(self, d: pd.Timestamp):
+        curves, args = data.get_singlenames_curves(self.index_type, self.series, d)
+        _, jp_yc, _, step_in_date, value_date, _ = args
+        self.yc = jp_yc
+        self.step_in_date = step_in_date
+        self.value_date = value_date
+        self._trade_date = d
+        self.curves = curves
+
+    def pv(self, maturity: pd.Timestamp, epsilon=0.):
+        coupon = self.index_desc.loc[maturity, 'coupon']
+        return super().pv(self.step_in_date, self.value_date, maturity, self.yc,
+                          self.recovery, coupon, epsilon)
+
+    def duration(self, maturity):
+        return super().duration(self.step_in_date, self.value_date, maturity, self.yc)
+
+    def theta(self, maturity):
+        if self.step_in_date.date() > maturity - relativedelta(years=1):
+            return np.NaN
+        else:
+            coupon = self.index_desc.loc[maturity, 'coupon']
+            index_quote = self.index_quotes.loc[self.trade_date,maturity]
+            return super().theta(self.step_in_date, self.value_date, maturity,
+                              self.yc, self.recovery, coupon, index_quote)
+    def tweak(self):
+        """ tweak the singlename curves to match index quotes"""
+        quotes = self.index_quotes.loc[self.trade_date]
+        self.tweaks = []
+        for m, index_quote in quotes.iteritems():
+            coupon = self.index_desc.loc[m, 'coupon']
+            lo, hi = -0.3, 0.3
+            while lo > -1:
+                try:
+                    eps = brentq(lambda epsilon: self.pv(m, epsilon) -
+                                 index_quote, lo, hi)
+                except ValueError:
+                    lo *= 1.1
+                    hi *= 1.1
+                else:
+                    break
+            else:
+                print("couldn't calibrate for date: {} and maturity: {}".
+                      format(self.trade_date.date(), m.date()))
+                self.tweaks.append(np.NaN)
+                continue
+            self.tweaks.append(eps)
+            self.tweak_portfolio(eps, m)