def get_risk_free_rate(self, start_date, end_date):
tenor = risk_free_helper.get_tenor_for(start_date, end_date)
yc = self._data_source.get_yield_curve(start_date, start_date, [tenor])
if yc is None or yc.empty:
return 0
rate = yc.values[0, 0]
return 0 if np.isnan(rate) else rate
def get_risk_free_rate(self, start_date, end_date):
tenor = risk_free_helper.get_tenor_for(start_date, end_date)
yc = self._data_source.get_yield_curve(start_date, start_date, [tenor])
if yc is None or yc.empty:
return 0
rate = yc.values[0, 0]
return 0 if np.isnan(rate) else rate
def get_risk_free_rate(self, start_date, end_date):
tenor = risk_free_helper.get_tenor_for(start_date, end_date)
tenor = tenor[-1] + tenor[:-1]
d = start_date.year * 10000 + start_date.month * 100 + start_date.day
pos = self._dates.searchsorted(d)
if pos > 0 and (pos == len(self._dates) or self._dates[pos] != d):
pos -= 1
col = self._table.cols[tenor]
while pos >= 0 and np.isnan(col[pos]):
# data is missing ...
pos -= 1
return self._table.cols[tenor][pos]
def get_risk_free_rate(self, start_date, end_date):
tenor = risk_free_helper.get_tenor_for(start_date, end_date)
tenor = tenor[-1] + tenor[:-1]
d = start_date.year * 10000 + start_date.month * 100 + start_date.day
pos = self._dates.searchsorted(d)
if pos > 0 and (pos == len(self._dates) or self._dates[pos] != d):
pos -= 1
col = self._table.cols[tenor]
while pos >= 0 and np.isnan(col[pos]):
# data is missing ...
pos -= 1
return self._table.cols[tenor][pos]
def get_risk_free_rate(self, start_date, end_date):
mongo_dates = self._yield_curve['dates'].find({}, {
"_id": 0
}).sort('date', pymongo.ASCENDING)
_dates = np.array([np.uint32(d['date']) for d in mongo_dates])
tenor = risk_free_helper.get_tenor_for(start_date, end_date)
tenor = tenor[-1] + tenor[:-1]
mongo_data = self._yield_curve[tenor].find({}, {"_id": 0})
_table = np.array([d['data'] for d in mongo_data])
d = start_date.year * 10000 + start_date.month * 100 + start_date.day
pos = _dates.searchsorted(d)
if pos > 0 and (pos == len(_dates) or _dates[pos] != d):
pos -= 1
while pos >= 0 and np.isnan(_table[pos]):
# data is missing ...
pos -= 1
return _table[pos]
