diff options
Diffstat (limited to 'python/analytics')
| -rw-r--r-- | python/analytics/basket_index.py | 12 | ||||
| -rw-r--r-- | python/analytics/index.py | 22 |
2 files changed, 24 insertions, 10 deletions
diff --git a/python/analytics/basket_index.py b/python/analytics/basket_index.py index f41494fc..e099ef5d 100644 --- a/python/analytics/basket_index.py +++ b/python/analytics/basket_index.py @@ -1,6 +1,6 @@ from index_data import (get_index_quotes, get_singlenames_curves, get_tranche_quotes, _serenitas_engine) -from tranche_functions import credit_schedule +from .tranche_functions import credit_schedule from dateutil.relativedelta import relativedelta from pyisda.credit_index import CreditIndex from typing import List @@ -35,6 +35,14 @@ class BasketIndex(CreditIndex): index_col='tenor', params=(index_type, series), parse_dates=['maturity', 'issue_date']) + self._index_version = pd.read_sql_query("SELECT lastdate, indexfactor, cumulativeloss " \ + "FROM index_version " \ + "WHERE index=%s AND series = %s" \ + "ORDER BY lastdate", + _serenitas_engine, + index_col='lastdate', + params=(index_type, series), + parse_dates=['lastdate']) self.issue_date = self.index_desc.issue_date[0] maturities = self.index_desc.maturity.sort_values().dt.to_pydatetime() self.index_desc = self.index_desc.reset_index().set_index('maturity') @@ -128,7 +136,7 @@ class TrancheBasket(BasketIndex): self.maturity = index_desc.loc[tenor].maturity self.start_date, self.cs = credit_schedule(trade_date, tenor[:-1], 1, self.yc) self.K_orig = [0] + [q['detach'] for q in self.tranche_quotes] - self.K = adjust_attachments(self.K_orig, self + #self.K = adjust_attachments(self.K_orig, self def _get_quotes(self): refprice = self.tranche_quotes[0]['indexrefprice'] diff --git a/python/analytics/index.py b/python/analytics/index.py index 789bf692..ae570c10 100644 --- a/python/analytics/index.py +++ b/python/analytics/index.py @@ -7,7 +7,6 @@ import pandas as pd import warnings from dateutil.relativedelta import relativedelta - from pyisda.legs import ContingentLeg, FeeLeg from quantlib.settings import Settings from quantlib.time.api import Date, Actual365Fixed @@ -52,7 +51,7 @@ def _key_from_index(index): class Index(object): """ minimal class to represent a credit index """ __slots__ = ['fixed_rate', 'notional', '_start_date', '_end_date', - 'recovery', 'factor', '_fee_leg', '_default_leg', + 'recovery', '_factor', '_fee_leg', '_default_leg', '_trade_date', '_yc', '_sc', '_risky_annuity', '_spread', '_price', 'name', 'issue_date', '_quote_is_price', '_direction', 'currency', '_step_in_date', '_accrued', @@ -75,7 +74,7 @@ class Index(object): self._start_date = start_date self._end_date = end_date self.recovery = recovery - self.factor = 1 + self._factor = () self._fee_leg = FeeLeg(self._start_date, end_date, True, 1., 1.) self._default_leg = ContingentLeg(self._start_date, end_date, True) @@ -360,6 +359,13 @@ class Index(object): (index_type, series, tenor, self.trade_date)) rec = run.fetchone() self.spread = rec.closespread + @property + def factor(self): + for lastdate, factor in getattr(self, '_factor'): + if lastdate >= self.trade_date: + return factor + else: + return 1 @classmethod def from_name(cls, index=None, series=None, tenor=None, trade_date=datetime.date.today(), @@ -378,7 +384,8 @@ class Index(object): raise ValueError("Not enough information to load the index.") try: df = pd.read_sql_query(sql_str, - _engine, parse_dates=['lastdate', 'issue_date'], + _engine, + parse_dates=['lastdate', 'issue_date'], params=params) maturity = df.maturity[0] coupon = df.coupon[0] @@ -386,9 +393,7 @@ class Index(object): tenor = df.tenor[0] index_type = index.upper() if index else df.loc[0,'index'] series = series if series else df.series[0] - df.loc[df.lastdate.isnull(),'lastdate'] = maturity - factor = df.loc[df.lastdate >= pd.Timestamp(trade_date), - 'indexfactor'].iat[0]/100 + df.loc[df.lastdate.isnull(), 'lastdate'] = maturity except exc.DataError as e: print(e) return None @@ -396,7 +401,8 @@ class Index(object): recovery = 0.4 if index_type == "IG" else 0.3 instance = cls(trade_date, maturity, recovery, coupon, notional, index_type=="HY", df.issue_date[0]) - instance.factor = factor + instance._factor = tuple((ld.date(), factor / 100) for ld, factor in \ + df[['lastdate', 'indexfactor']].itertuples(index=False)) instance.direction = "Buyer" tenor = tenor.upper() if tenor.endswith("R"): |