def tensor_repr(fra_data, dtype=None):
"""Creates a tensor representation of the FRA."""
dtype = dtype or tf.float64
res = dict()
res["fixing_date"] = tf.convert_to_tensor(fra_data["fixing_date"],
dtype=tf.int32)
res["fixed_rate"] = tf.convert_to_tensor(fra_data["fixed_rate"],
dtype=dtype)
config = fra_data["config"]
res["config"] = config
res["batch_names"] = fra_data["batch_names"]
currency_list = cashflow_streams.to_list(fra_data["currency"])
discount_curve_type = []
for currency in currency_list:
if config is not None:
if currency in config.discounting_curve:
discount_curve = config.discounting_curve[currency]
discount_curve_type.append(discount_curve)
else:
risk_free = curve_types_lib.RiskFreeCurve(currency=currency)
discount_curve_type.append(risk_free)
else:
# Default discounting is the risk free curve
risk_free = curve_types_lib.RiskFreeCurve(currency=currency)
discount_curve_type.append(risk_free)
discount_curve_type, mask = cashflow_streams.process_curve_types(
discount_curve_type)
res["discount_curve_mask"] = tf.convert_to_tensor(mask, dtype=tf.int32)
res["discount_curve_type"] = discount_curve_type
# Get reset frequency
reset_frequency = tf.convert_to_tensor(fra_data["rate_term"][1], tf.int32)
res["rate_term"] = {
"type": fra_data["rate_term"][0],
"frequency": reset_frequency
}
rate_index = cashflow_streams.to_list(fra_data["rate_index"])
rate_index_curves = []
for currency, r_ind in zip(currency_list, rate_index):
rate_index_curves.append(
curve_types_lib.RateIndexCurve(currency=currency, index=r_ind))
[rate_index_curves,
reference_mask] = cashflow_streams.process_curve_types(rate_index_curves)
res["reference_mask"] = tf.convert_to_tensor(reference_mask, tf.int32)
res["rate_index_curves"] = rate_index_curves
# Extract unique rate indices
res["rate_index"] = [curve.index for curve in rate_index_curves]
res["notional_amount"] = tf.convert_to_tensor(fra_data["notional_amount"],
dtype=dtype)
res["settlement_days"] = tf.convert_to_tensor(fra_data["settlement_days"],
dtype=tf.int32,
name="settlement_days")
res["calendar"] = fra_data["calendar"]
# Extract unique currencies
res["currency"] = [curve.currency for curve in discount_curve_type]
res["daycount_convention"] = fra_data["daycount_convention"]
res["business_day_convention"] = fra_data["business_day_convention"]
res["short_position"] = tf.convert_to_tensor(fra_data["short_position"],
dtype=tf.bool)
return res
def floating_leg_tensor_repr(leg, config, dtype):
"""Creates tensor representation for a floating leg of a swap."""
res = {}
coupon_spec = {}
currency_list = cashflow_streams.to_list(leg.currency)
discount_curve_type = []
for currency in currency_list:
if config is not None:
if currency in config.discounting_curve:
discount_curve = config.discounting_curve[currency]
discount_curve_type.append(discount_curve)
else:
risk_free = curve_types_lib.RiskFreeCurve(currency=currency)
discount_curve_type.append(risk_free)
else:
# Default discounting is the risk free curve
risk_free = curve_types_lib.RiskFreeCurve(currency=currency)
discount_curve_type.append(risk_free)
discount_curve_type, mask = cashflow_streams.process_curve_types(
discount_curve_type)
res["discount_curve_mask"] = tf.convert_to_tensor(mask, dtype=tf.int32)
res["discount_curve_type"] = discount_curve_type
# Get coupon frequency
coupon_frequency = tf.convert_to_tensor(leg.coupon_frequency[1],
tf.int32,
name="floating_coupon_frequency")
coupon_spec["coupon_frequency"] = {
"type": leg.coupon_frequency[0],
"frequency": coupon_frequency
}
# Get reset frequency
reset_frequency = tf.convert_to_tensor(leg.reset_frequency[1], tf.int32)
coupon_spec["reset_frequency"] = {
"type": leg.reset_frequency[0],
"frequency": reset_frequency
}
floating_rate_type = cashflow_streams.to_list(leg.floating_rate_type)
rate_index_curves = []
for currency, floating_rate_type in zip(currency_list, floating_rate_type):
rate_index_curves.append(
curve_types_lib.RateIndexCurve(currency=currency,
index=floating_rate_type))
[rate_index_curves,
reference_mask] = cashflow_streams.process_curve_types(rate_index_curves)
res["reference_mask"] = tf.convert_to_tensor(reference_mask, tf.int32)
res["rate_index_curves"] = rate_index_curves
coupon_spec["notional_amount"] = tf.convert_to_tensor(leg.notional_amount,
dtype=dtype)
coupon_spec["settlement_days"] = tf.convert_to_tensor(
leg.settlement_days, dtype=tf.int32, name="settlement_days")
coupon_spec["calendar"] = leg.calendar
coupon_spec["currency"] = [curve.currency for curve in discount_curve_type]
coupon_spec["daycount_convention"] = leg.daycount_convention
coupon_spec["businessday_rule"] = leg.businessday_rule
coupon_spec["floating_rate_type"] = floating_rate_type
coupon_spec["spread"] = tf.convert_to_tensor(leg.spread, dtype=dtype)
res["coupon_spec"] = coupon_spec
return res
def __init__(self,
coupon_spec: coupon_specs.FloatCouponSpecs,
discount_curve_type: _CurveType,
start_date: types.DateTensor = None,
end_date: types.DateTensor = None,
discount_curve_mask: types.IntTensor = None,
rate_index_curves: curve_types_lib.RateIndexCurve = None,
reference_mask: types.IntTensor = None,
first_coupon_date: Optional[types.DateTensor] = None,
penultimate_coupon_date: Optional[types.DateTensor] = None,
schedule_fn: Optional[Callable[..., Any]] = None,
schedule: Optional[types.DateTensor] = None,
dtype: Optional[types.Dtype] = None,
name: Optional[str] = None):
"""Initializes a batch of floating cashflow streams.
Args:
coupon_spec: An instance of `FloatCouponSpecs` specifying the
details of the coupon payment for the cashflow stream.
discount_curve_type: An instance of `CurveType` or a list of those.
If supplied as a list and `discount_curve_mask` is not supplid,
the size of the list should be the same as the number of priced
instruments.
start_date: A `DateTensor` of `batch_shape` specifying the starting dates
of the accrual of the first coupon of the cashflow stream. The shape of
the input correspond to the number of streams being created.
Either this of `schedule` should be supplied
Default value: `None`
end_date: A `DateTensor` of `batch_shape`specifying the end dates for
accrual of the last coupon in each cashflow stream. The shape of the
input should be the same as that of `start_date`.
Either this of `schedule` should be supplied
Default value: `None`
discount_curve_mask: An optional integer `Tensor` of values ranging from
`0` to `len(discount_curve_type)` and of shape `batch_shape`. Identifies
a mapping between `discount_curve_type` list and the underlying
instruments.
Default value: `None`.
rate_index_curves: An instance of `RateIndexCurve` or a list of those.
If supplied as a list and `reference_mask` is not supplid,
the size of the list should be the same as the number of priced
instruments. Defines the index curves for each instrument. If not
supplied, `coupon_spec.floating_rate_type` is used to identify the
curves.
Default value: `None`.
reference_mask: An optional integer `Tensor` of values ranging from
`0` to `len(rate_index_curves)` and of shape `batch_shape`. Identifies
a mapping between `rate_index_curves` list and the underlying
instruments.
Default value: `None`.
first_coupon_date: An optional `DateTensor` specifying the payment dates
of the first coupon of the cashflow stream. Use this input for cashflows
with irregular first coupon. Should be of the same shape as
`start_date`.
Default value: None which implies regular first coupon.
penultimate_coupon_date: An optional `DateTensor` specifying the payment
dates of the penultimate (next to last) coupon of the cashflow
stream. Use this input for cashflows with irregular last coupon.
Should be of the same shape as `end_date`.
Default value: None which implies regular last coupon.
schedule_fn: A callable that accepts `start_date`, `end_date`,
`coupon_frequency`, `settlement_days`, `first_coupon_date`, and
`penultimate_coupon_date` as `Tensor`s and returns coupon payment
days.
Default value: `None`.
schedule: A `DateTensor` of coupon payment dates.
Default value: `None`.
dtype: `tf.Dtype` of the input and output real `Tensor`s.
Default value: None which maps to the default dtype inferred by
TensorFlow.
name: Python str. The name to give to the ops created by this class.
Default value: `None` which maps to 'floating_cashflow_stream'.
"""
self._name = name or "floating_cashflow_stream"
with tf.name_scope(self._name):
curve_list = to_list(discount_curve_type)
[
self._discount_curve_type,
self._mask
] = process_curve_types(curve_list, discount_curve_mask)
self._first_coupon_date = None
self._penultimate_coupon_date = None
if schedule is None:
if (start_date is None) or (end_date is None):
raise ValueError("If `schedule` is not supplied both "
"`start_date` and `end_date` should be supplied")
if schedule is None:
if isinstance(start_date, tf.Tensor):
self._start_date = dateslib.dates_from_tensor(
start_date)
else:
self._start_date = dateslib.convert_to_date_tensor(
start_date)
if isinstance(start_date, tf.Tensor):
self._end_date = dateslib.dates_from_tensor(
end_date)
else:
self._end_date = dateslib.convert_to_date_tensor(
end_date)
self._first_coupon_date = first_coupon_date
self._penultimate_coupon_date = penultimate_coupon_date
if self._first_coupon_date is not None:
if isinstance(start_date, tf.Tensor):
self._first_coupon_date = dateslib.dates_from_tensor(
first_coupon_date)
else:
self._first_coupon_date = dateslib.convert_to_date_tensor(
first_coupon_date)
if self._penultimate_coupon_date is not None:
if isinstance(start_date, tf.Tensor):
self._penultimate_coupon_date = dateslib.dates_from_tensor(
penultimate_coupon_date)
else:
self._penultimate_coupon_date = dateslib.convert_to_date_tensor(
penultimate_coupon_date)
# Ignored and weekends only is used
calendar = dateslib.create_holiday_calendar(
weekend_mask=dateslib.WeekendMask.SATURDAY_SUNDAY)
# Convert coupon and reset frequencies to PeriodTensor
coupon_frequency = _get_attr(coupon_spec, "coupon_frequency")
# Update coupon frequency
if isinstance(coupon_frequency, period_pb2.Period):
coupon_frequency = market_data_utils.get_period(
_get_attr(coupon_spec, "coupon_frequency"))
if isinstance(coupon_frequency, (list, tuple)):
coupon_frequency = market_data_utils.period_from_list(
*_get_attr(coupon_spec, "coupon_frequency"))
if isinstance(coupon_frequency, dict):
coupon_frequency = market_data_utils.period_from_dict(
_get_attr(coupon_spec, "coupon_frequency"))
# Update reset frequency
reset_frequency = _get_attr(coupon_spec, "reset_frequency")
if isinstance(reset_frequency, period_pb2.Period):
reset_frequency = market_data_utils.get_period(
_get_attr(coupon_spec, "reset_frequency"))
if isinstance(reset_frequency, (list, tuple)):
reset_frequency = market_data_utils.period_from_list(
*_get_attr(coupon_spec, "reset_frequency"))
if isinstance(reset_frequency, dict):
reset_frequency = market_data_utils.period_from_dict(
_get_attr(coupon_spec, "reset_frequency"))
self._reset_frequency = reset_frequency
businessday_rule = _get_attr(coupon_spec, "businessday_rule")
roll_convention, eom = market_data_utils.get_business_day_convention(
businessday_rule)
notional = tf.convert_to_tensor(
_get_attr(coupon_spec, "notional_amount"),
dtype=dtype,
name="notional")
self._dtype = dtype or notional.dtype
daycount_convention = _get_attr(coupon_spec, "daycount_convention")
daycount_fn = market_data_utils.get_daycount_fn(
_get_attr(coupon_spec, "daycount_convention"), self._dtype)
self._daycount_convention = daycount_convention
self._settlement_days = tf.convert_to_tensor(
_get_attr(coupon_spec, "settlement_days"),
dtype=tf.int32,
name="settlement_days")
spread = tf.convert_to_tensor(_get_attr(coupon_spec, "spread"),
dtype=self._dtype,
name="spread")
if schedule is not None:
coupon_dates = dateslib.convert_to_date_tensor(schedule)
elif schedule_fn is None:
coupon_dates = _generate_schedule(
start_date=self._start_date,
end_date=self._end_date,
coupon_frequency=coupon_frequency,
roll_convention=roll_convention,
calendar=calendar,
settlement_days=self._settlement_days,
end_of_month=eom,
first_coupon_date=self._first_coupon_date,
penultimate_coupon_date=self._penultimate_coupon_date)
else:
if first_coupon_date is not None:
first_coupon_date = self._first_coupon_date.to_tensor()
if penultimate_coupon_date is not None:
penultimate_coupon_date = self._penultimate_coupon_date.to_tensor()
coupon_dates = schedule_fn(
start_date=self._start_date.to_tensor(),
end_date=self._end_date.to_tensor(),
coupon_frequency=coupon_frequency.quantity(),
settlement_days=self._settlement_days,
first_coupon_date=first_coupon_date,
penultimate_coupon_date=penultimate_coupon_date)
# Convert to DateTensor if the result comes from a tf.function
coupon_dates = dateslib.convert_to_date_tensor(coupon_dates)
# Extract batch shape
self._batch_shape = tf.shape(coupon_dates.ordinal())[:-1]
accrual_start_dates = coupon_dates[..., :-1]
coupon_start_dates = coupon_dates[..., :-1]
coupon_end_dates = coupon_dates[..., 1:]
accrual_end_dates = accrual_start_dates + reset_frequency.expand_dims(
axis=-1)
# Adjust for irregular coupons
accrual_end_dates = dateslib.DateTensor.concat(
[coupon_end_dates[..., :1],
accrual_end_dates[..., 1:-1],
coupon_end_dates[..., -1:]], axis=-1)
daycount_fractions = daycount_fn(
start_date=coupon_start_dates,
end_date=coupon_end_dates)
self._num_cashflows = tf.shape(daycount_fractions)[-1]
self._coupon_start_dates = coupon_start_dates
self._coupon_end_dates = coupon_end_dates
self._accrual_start_date = accrual_start_dates
self._accrual_end_date = accrual_end_dates
self._notional = notional
self._daycount_fractions = daycount_fractions
self._spread = spread
self._currency = _get_attr(coupon_spec, "currency")
self._daycount_fn = daycount_fn
# Construct the reference curve object
# Extract all rate_curves
self._floating_rate_type = to_list(
_get_attr(coupon_spec, "floating_rate_type"))
self._currency = to_list(self._currency)
if rate_index_curves is None:
rate_index_curves = []
for currency, floating_rate_type in zip(self._currency,
self._floating_rate_type):
rate_index_curves.append(curve_types_lib.RateIndexCurve(
currency=currency, index=floating_rate_type))
[
self._reference_curve_type,
self._reference_mask
] = process_curve_types(rate_index_curves, reference_mask)
def __init__(self,
short_position: types.BoolTensor,
currency: types.CurrencyProtoType,
fixing_date: types.DateTensor,
fixed_rate: types.FloatTensor,
notional_amount: types.FloatTensor,
daycount_convention: types.DayCountConventionsProtoType,
business_day_convention: types.BusinessDayConventionProtoType,
calendar: types.BankHolidaysProtoType,
rate_term: period_pb2.Period,
rate_index: rate_indices.RateIndex,
settlement_days: Optional[types.IntTensor] = 0,
fra_config: ForwardRateAgreementConfig = None,
batch_names: Optional[types.StringTensor] = None,
dtype: Optional[types.Dtype] = None,
name: Optional[str] = None):
"""Initializes the batch of FRA contracts.
Args:
short_position: Whether the contract holder lends or borrows the money.
Default value: `True` which means that the contract holder lends the
money at the fixed rate.
currency: The denominated currency.
fixing_date: A `DateTensor` specifying the dates on which forward
rate will be fixed.
fixed_rate: A `Tensor` of real dtype specifying the fixed rate
payment agreed at the initiation of the individual contracts. The shape
should be broadcastable with `fixed_rate`.
notional_amount: A `Tensor` of real dtype broadcastable with fixed_rate
specifying the notional amount for each contract. When the notional is
specified as a scalar, it is assumed that all contracts have the same
notional.
daycount_convention: A `DayCountConvention` to determine how cashflows
are accrued for each contract. Daycount is assumed to be the same for
all contracts in a given batch.
business_day_convention: A business count convention.
calendar: A calendar to specify the weekend mask and bank holidays.
rate_term: A tenor of the rate (usually Libor) that determines the
floating cashflow.
rate_index: A type of the floating leg. An instance of
`core.rate_indices.RateIndex`.
settlement_days: An integer `Tensor` of the shape broadcastable with the
shape of `fixing_date`.
fra_config: Optional `ForwardRateAgreementConfig`.
batch_names: A string `Tensor` of instrument names. Should be of shape
`batch_shape + [2]` specying name and instrument type. This is useful
when the `from_protos` method is used and the user needs to identify
which instruments got batched together.
dtype: `tf.Dtype` of the input and output real `Tensor`s.
Default value: `None` which maps to `float64`.
name: Python str. The name to give to the ops created by this class.
Default value: `None` which maps to 'forward_rate_agreement'.
"""
self._name = name or "forward_rate_agreement"
with tf.name_scope(self._name):
if batch_names is not None:
self._names = tf.convert_to_tensor(batch_names,
name="batch_names")
else:
self._names = None
self._dtype = dtype or tf.float64
ones = tf.constant(1, dtype=self._dtype)
self._short_position = tf.where(short_position,
ones,
-ones,
name="short_position")
self._notional_amount = tf.convert_to_tensor(
notional_amount, dtype=self._dtype, name="notional_amount")
self._fixed_rate = tf.convert_to_tensor(fixed_rate,
dtype=self._dtype,
name="fixed_rate")
settlement_days = tf.convert_to_tensor(settlement_days)
# Business day roll convention and the end of month flag
roll_convention, eom = market_data_utils.get_business_day_convention(
business_day_convention)
# TODO(b/160446193): Calendar is ignored at the moment
calendar = dateslib.create_holiday_calendar(
weekend_mask=dateslib.WeekendMask.SATURDAY_SUNDAY)
self._fixing_date = dateslib.convert_to_date_tensor(fixing_date)
self._accrual_start_date = calendar.add_business_days(
self._fixing_date,
settlement_days,
roll_convention=roll_convention)
self._day_count_fn = market_data_utils.get_daycount_fn(
daycount_convention)
period = market_data_utils.get_period(rate_term)
self._accrual_end_date = calendar.add_period_and_roll(
self._accrual_start_date,
period,
roll_convention=roll_convention)
if eom:
self._accrual_end_date = self._accrual_end_date.to_end_of_month(
)
self._daycount_fractions = self._day_count_fn(
start_date=self._accrual_start_date,
end_date=self._accrual_end_date,
dtype=self._dtype)
self._settlement_days = settlement_days
self._roll_convention = roll_convention
# Get discount and reference curves
rate_index_curve = curve_types.RateIndexCurve(currency=currency,
index=rate_index)
reference_curve_type = rate_index_curve
if fra_config is not None:
try:
self._discount_curve_type = fra_config.discounting_index[
currency]
except KeyError:
risk_free = curve_types.RiskFreeCurve(currency=currency)
self._discount_curve_type = curve_types.CurveType(
type=risk_free)
else:
# Default discounting is the risk free curve
risk_free = curve_types.RiskFreeCurve(currency=currency)
self._discount_curve_type = risk_free
self._reference_curve_type = reference_curve_type
self._batch_shape = self._daycount_fractions.shape.as_list()[:-1]
def __init__(self,
short_position: types.BoolTensor,
currency: types.CurrencyProtoType,
fixing_date: types.DateTensor,
fixed_rate: types.FloatTensor,
notional_amount: types.FloatTensor,
daycount_convention: types.DayCountConventionsProtoType,
business_day_convention: types.BusinessDayConventionProtoType,
calendar: types.BankHolidaysProtoType,
rate_term: period_pb2.Period,
rate_index: rate_indices.RateIndex,
settlement_days: Optional[types.IntTensor] = 0,
discount_curve_type: curve_types_lib.CurveType = None,
discount_curve_mask: types.IntTensor = None,
rate_index_curves: curve_types_lib.RateIndexCurve = None,
reference_mask: types.IntTensor = None,
config: Union[ForwardRateAgreementConfig, Dict[str,
Any]] = None,
batch_names: Optional[types.StringTensor] = None,
dtype: Optional[types.Dtype] = None,
name: Optional[str] = None):
"""Initializes the batch of FRA contracts.
Args:
short_position: Whether the contract holder lends or borrows the money.
Default value: `True` which means that the contract holder lends the
money at the fixed rate.
currency: The denominated currency.
fixing_date: A `DateTensor` specifying the dates on which forward
rate will be fixed.
fixed_rate: A `Tensor` of real dtype specifying the fixed rate
payment agreed at the initiation of the individual contracts. The shape
should be broadcastable with `fixed_rate`.
notional_amount: A `Tensor` of real dtype broadcastable with fixed_rate
specifying the notional amount for each contract. When the notional is
specified as a scalar, it is assumed that all contracts have the same
notional.
daycount_convention: A `DayCountConvention` to determine how cashflows
are accrued for each contract. Daycount is assumed to be the same for
all contracts in a given batch.
business_day_convention: A business count convention.
calendar: A calendar to specify the weekend mask and bank holidays.
rate_term: A tenor of the rate (usually Libor) that determines the
floating cashflow.
rate_index: A type of the floating leg. An instance of
`core.rate_indices.RateIndex`.
settlement_days: An integer `Tensor` of the shape broadcastable with the
shape of `fixing_date`.
discount_curve_type: An optional instance of `CurveType` or a list of
those. If supplied as a list and `discount_curve_mask` is not supplied,
the size of the list should be the same as the number of priced
instruments. Defines discount curves for the instruments.
Default value: `None`, meaning that discount curves are inferred
from `currency` and `config`.
discount_curve_mask: An optional integer `Tensor` of values ranging from
`0` to `len(discount_curve_type) - 1` and of shape `batch_shape`.
Identifies a mapping between `discount_curve_type` list and the
underlying instruments.
Default value: `None`.
rate_index_curves: An instance of `RateIndexCurve` or a list of those.
If supplied as a list and `reference_mask` is not supplid,
the size of the list should be the same as the number of priced
instruments. Defines the index curves for each instrument. If not
supplied, `coupon_spec.floating_rate_type` is used to identify the
curves.
Default value: `None`.
reference_mask: An optional integer `Tensor` of values ranging from
`0` to `len(rate_index_curves) - 1` and of shape `batch_shape`.
Identifies a mapping between `rate_index_curves` list and the underlying
instruments.
Default value: `None`.
config: Optional `ForwardRateAgreementConfig` or a dictionary.
If dictionary, then the keys should be the same as the field names of
`ForwardRateAgreementConfig`.
batch_names: A string `Tensor` of instrument names. Should be of shape
`batch_shape + [2]` specying name and instrument type. This is useful
when the `from_protos` method is used and the user needs to identify
which instruments got batched together.
dtype: `tf.Dtype` of the input and output real `Tensor`s.
Default value: `None` which maps to `float64`.
name: Python str. The name to give to the ops created by this class.
Default value: `None` which maps to 'forward_rate_agreement'.
"""
self._name = name or "forward_rate_agreement"
with tf.name_scope(self._name):
if batch_names is not None:
self._names = tf.convert_to_tensor(batch_names,
name="batch_names")
else:
self._names = None
self._dtype = dtype or tf.float64
ones = tf.constant(1, dtype=self._dtype)
self._short_position = tf.where(short_position,
ones,
-ones,
name="short_position")
self._notional_amount = tf.convert_to_tensor(
notional_amount, dtype=self._dtype, name="notional_amount")
self._fixed_rate = tf.convert_to_tensor(fixed_rate,
dtype=self._dtype,
name="fixed_rate")
settlement_days = tf.convert_to_tensor(settlement_days)
# Business day roll convention and the end of month flag
roll_convention, eom = market_data_utils.get_business_day_convention(
business_day_convention)
# TODO(b/160446193): Calendar is ignored at the moment
calendar = dateslib.create_holiday_calendar(
weekend_mask=dateslib.WeekendMask.SATURDAY_SUNDAY)
if isinstance(fixing_date, types.IntTensor):
self._fixing_date = dateslib.dates_from_tensor(fixing_date)
else:
self._fixing_date = dateslib.convert_to_date_tensor(
fixing_date)
self._accrual_start_date = calendar.add_business_days(
self._fixing_date,
settlement_days,
roll_convention=roll_convention)
self._day_count_fn = market_data_utils.get_daycount_fn(
daycount_convention)
period = rate_term
if isinstance(rate_term, period_pb2.Period):
period = market_data_utils.get_period(rate_term)
if isinstance(rate_term, dict):
period = market_data_utils.period_from_dict(rate_term)
self._accrual_end_date = calendar.add_period_and_roll(
self._accrual_start_date,
period,
roll_convention=roll_convention)
if eom:
self._accrual_end_date = self._accrual_end_date.to_end_of_month(
)
self._daycount_fractions = self._day_count_fn(
start_date=self._accrual_start_date,
end_date=self._accrual_end_date,
dtype=self._dtype)
self._settlement_days = settlement_days
self._roll_convention = roll_convention
# Get discount and reference curves
self._currency = cashflow_streams.to_list(currency)
self._rate_index = cashflow_streams.to_list(rate_index)
# Get a mask for the reference curves
if rate_index_curves is None:
rate_index_curves = []
if len(self._currency) != len(self._rate_index):
raise ValueError(
"When rate_index_curves` is not supplied, number of currencies "
"and rate indices should be the same `but it is {0} and "
"{1}".format(len(self._currency),
len(self._rate_index)))
for currency, rate_index in zip(self._currency,
self._rate_index):
rate_index_curves.append(
curve_types_lib.RateIndexCurve(currency=currency,
index=rate_index))
[self._reference_curve_type, self._reference_mask
] = cashflow_streams.process_curve_types(rate_index_curves,
reference_mask)
# Get a mask for the discount curves
self._config = _process_config(config)
if discount_curve_type is None:
curve_list = []
for currency in self._currency:
if currency in self._config.discounting_curve:
discount_curve_type = self._config.discounting_curve[
currency]
else:
# Default discounting is the risk free curve
discount_curve_type = curve_types_lib.RiskFreeCurve(
currency=currency)
curve_list.append(discount_curve_type)
else:
curve_list = cashflow_streams.to_list(discount_curve_type)
# Get masks for discount and reference curves
[self._discount_curve_type, self._mask
] = cashflow_streams.process_curve_types(curve_list,
discount_curve_mask)
# Get batch shape
self._batch_shape = self._daycount_fractions.shape.as_list()[:-1]
def __init__(self,
start_date: types.DateTensor,
end_date: types.DateTensor,
coupon_spec: coupon_specs.FloatCouponSpecs,
discount_curve_type: _CurveType,
first_coupon_date: Optional[types.DateTensor] = None,
penultimate_coupon_date: Optional[types.DateTensor] = None,
dtype: Optional[types.Dtype] = None,
name: Optional[str] = None):
"""Initializes a batch of floating cashflow streams.
Args:
start_date: A `DateTensor` of `batch_shape` specifying the starting dates
of the accrual of the first coupon of the cashflow stream. The shape of
the input correspond to the number of streams being created.
end_date: A `DateTensor` of `batch_shape`specifying the end dates for
accrual of the last coupon in each cashflow stream. The shape of the
input should be the same as that of `start_date`.
coupon_spec: An instance of `FloatCouponSpecs` specifying the
details of the coupon payment for the cashflow stream.
discount_curve_type: An instance of `CurveType`.
first_coupon_date: An optional `DateTensor` specifying the payment dates
of the first coupon of the cashflow stream. Use this input for cashflows
with irregular first coupon. Should be of the same shape as
`start_date`.
Default value: None which implies regular first coupon.
penultimate_coupon_date: An optional `DateTensor` specifying the payment
dates of the penultimate (next to last) coupon of the cashflow
stream. Use this input for cashflows with irregular last coupon.
Should be of the same shape as `end_date`.
Default value: None which implies regular last coupon.
dtype: `tf.Dtype` of the input and output real `Tensor`s.
Default value: None which maps to the default dtype inferred by
TensorFlow.
name: Python str. The name to give to the ops created by this class.
Default value: `None` which maps to 'floating_cashflow_stream'.
"""
self._name = name or "floating_cashflow_stream"
with tf.name_scope(self._name):
curve_list = to_list(discount_curve_type)
[self._discount_curve_type,
self._mask] = process_curve_types(curve_list)
self._first_coupon_date = None
self._penultimate_coupon_date = None
self._start_date = dateslib.convert_to_date_tensor(start_date)
self._end_date = dateslib.convert_to_date_tensor(end_date)
if self._first_coupon_date is not None:
self._first_coupon_date = dateslib.convert_to_date_tensor(
first_coupon_date)
if self._penultimate_coupon_date is not None:
self._penultimate_coupon_date = dateslib.convert_to_date_tensor(
penultimate_coupon_date)
# Ignored and weekends only is used
calendar = dateslib.create_holiday_calendar(
weekend_mask=dateslib.WeekendMask.SATURDAY_SUNDAY)
# Convert coupon and reset frequencies to PeriodTensor
coupon_frequency = coupon_spec.coupon_frequency
if isinstance(coupon_frequency, (period_pb2.Period, list, tuple)):
coupon_frequency = market_data_utils.get_period(
coupon_spec.coupon_frequency)
reset_frequency = coupon_spec.reset_frequency
if isinstance(reset_frequency, (period_pb2.Period, list, tuple)):
reset_frequency = market_data_utils.get_period(
coupon_spec.reset_frequency)
self._reset_frequency = reset_frequency
businessday_rule = coupon_spec.businessday_rule
roll_convention, eom = market_data_utils.get_business_day_convention(
businessday_rule)
notional = tf.convert_to_tensor(coupon_spec.notional_amount,
dtype=dtype,
name="notional")
self._dtype = dtype or notional.dtype
daycount_convention = coupon_spec.daycount_convention
daycount_fn = market_data_utils.get_daycount_fn(
coupon_spec.daycount_convention)
self._daycount_convention = daycount_convention
self._settlement_days = tf.convert_to_tensor(
coupon_spec.settlement_days,
dtype=tf.int32,
name="settlement_days")
spread = tf.convert_to_tensor(coupon_spec.spread,
dtype=self._dtype,
name="spread")
coupon_dates = _generate_schedule(
start_date=self._start_date,
end_date=self._end_date,
coupon_frequency=coupon_frequency,
roll_convention=roll_convention,
calendar=calendar,
settlement_days=self._settlement_days,
end_of_month=eom,
first_coupon_date=self._first_coupon_date,
penultimate_coupon_date=self._penultimate_coupon_date)
# Extract batch shape
self._batch_shape = coupon_dates.shape.as_list()[:-1]
accrual_start_dates = coupon_dates[..., :-1]
coupon_start_dates = coupon_dates[..., :-1]
coupon_end_dates = coupon_dates[..., 1:]
accrual_end_dates = accrual_start_dates + reset_frequency.expand_dims(
axis=-1)
# Adjust for irregular coupons
accrual_end_dates = dateslib.DateTensor.concat([
coupon_end_dates[..., :1], accrual_end_dates[..., 1:-1],
coupon_end_dates[..., -1:]
],
axis=-1)
daycount_fractions = daycount_fn(start_date=coupon_start_dates,
end_date=coupon_end_dates,
dtype=self._dtype)
self._num_cashflows = daycount_fractions.shape.as_list()[-1]
self._coupon_start_dates = coupon_start_dates
self._coupon_end_dates = coupon_end_dates
self._accrual_start_date = accrual_start_dates
self._accrual_end_date = accrual_end_dates
self._notional = notional
self._daycount_fractions = daycount_fractions
self._spread = spread
self._currency = coupon_spec.currency
self._daycount_fn = daycount_fn
# Construct the reference curve object
# Extract all rate_curves
self._floating_rate_type = to_list(coupon_spec.floating_rate_type)
self._currency = to_list(self._currency)
rate_index_curves = []
for currency, floating_rate_type in zip(self._currency,
self._floating_rate_type):
rate_index_curves.append(
curve_types_lib.RateIndexCurve(currency=currency,
index=floating_rate_type))
[self._reference_curve_type,
self._reference_mask] = process_curve_types(rate_index_curves)
