def __init__(self,
start_date: Union[dateslib.DateTensor, List[List[int]]],
maturity_date: Union[dateslib.DateTensor, List[List[int]]],
pay_leg: Union[coupon_specs.FixedCouponSpecs,
coupon_specs.FloatCouponSpecs],
receive_leg: Union[coupon_specs.FixedCouponSpecs,
coupon_specs.FloatCouponSpecs],
pay_leg_schedule_fn=None,
pay_leg_schedule=None,
receive_leg_schedule_fn=None,
receive_leg_schedule=None,
config: Union[InterestRateSwapConfig, Dict[str, Any]] = None,
batch_names: Optional[tf.Tensor] = None,
dtype: Optional[types.Dtype] = None,
name: Optional[str] = None):
"""Initializes a batch of IRS contracts.
Args:
start_date: A `DateTensor` of `batch_shape` specifying the dates for the
inception (start of the accrual) of the swap contracts. `batch_shape`
corresponds to the number of instruments being created.
maturity_date: A `DateTensor` broadcastable with `start_date` specifying
the maturity dates for each contract.
pay_leg: An instance of `FixedCouponSpecs` or `FloatCouponSpecs`
specifying the coupon payments for the payment leg of the swap.
receive_leg: An instance of `FixedCouponSpecs` or `FloatCouponSpecs`
specifying the coupon payments for the receiving leg of the swap.
pay_leg_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. Constructs schedule for the pay leg of the swap.
Default value: `None`.
pay_leg_schedule: A `DateTensor` of coupon payment dates for the pay leg.
receive_leg_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. Constructs schedule for the receive leg of the swap.
Default value: `None`.
receive_leg_schedule: A `DateTensor` of coupon payment dates for the
receive leg.
config: Optional `InterestRateSwapConfig` or a dictionary.
If dictionary, then the keys should be the same as the field names of
`InterestRateSwapConfig`.
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 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 'interest_rate_swap'.
"""
self._name = name or "interest_rate_swap"
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
self._model = None # Ignore
self._config = _process_config(config)
if isinstance(pay_leg, dict):
self._discount_curve_type = pay_leg["discount_curve_type"]
self._start_date = start_date
else:
currencies = cashflow_streams.to_list(pay_leg.currency)
self._discount_curve_type = []
if pay_leg.currency != receive_leg.currency:
raise ValueError(
"Pay and receive legs should have the same currency")
for currency in currencies:
if currency in self._config.discounting_curve:
discount_curve = self._config.discounting_curve[
currency]
self._discount_curve_type.append(discount_curve)
else:
# Default discounting is the risk free curve
risk_free = curve_types_lib.RiskFreeCurve(
currency=currency)
self._discount_curve_type.append(risk_free)
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._maturity_date = dateslib.dates_from_tensor(maturity_date)
else:
self._maturity_date = dateslib.convert_to_date_tensor(
maturity_date)
self._pay_leg_schedule_fn = pay_leg_schedule_fn
self._receive_leg_schedule_fn = receive_leg_schedule_fn
self._pay_leg_schedule = pay_leg_schedule
self._receive_leg_schedule = receive_leg_schedule
self._pay_leg = _setup_leg(self._start_date, self._maturity_date,
self._discount_curve_type, pay_leg,
self._pay_leg_schedule_fn,
self._pay_leg_schedule)
self._receive_leg = _setup_leg(self._start_date,
self._maturity_date,
self._discount_curve_type,
receive_leg,
self._receive_leg_schedule_fn,
self._receive_leg_schedule)
self._batch_shape = self._pay_leg.batch_shape
def __init__(self,
coupon_spec: coupon_specs.FixedCouponSpecs,
discount_curve_type: _CurveType,
start_date: types.DateTensor = None,
end_date: types.DateTensor = None,
discount_curve_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 fixed cashflow streams.
Args:
coupon_spec: An instance of `FixedCouponSpecs` 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 supplied,
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`.
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 'fixed_cashflow_stream'.
"""
self._name = name or "fixed_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)
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)
# Update coupon frequency
coupon_frequency = _get_attr(coupon_spec, "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"))
businessday_rule = coupon_spec.businessday_rule
# Business day roll convention and the end of month flag
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
fixed_rate = tf.convert_to_tensor(_get_attr(coupon_spec, "fixed_rate"),
dtype=self._dtype,
name="fixed_rate")
# TODO(b/160446193): Calendar is ignored and weekends only is used
calendar = dateslib.create_holiday_calendar(
weekend_mask=dateslib.WeekendMask.SATURDAY_SUNDAY)
daycount_fn = market_data_utils.get_daycount_fn(
_get_attr(coupon_spec, "daycount_convention"), self._dtype)
self._settlement_days = tf.convert_to_tensor(
_get_attr(coupon_spec, "settlement_days"),
dtype=tf.int32,
name="settlement_days")
if schedule is not None:
if isinstance(start_date, tf.Tensor):
coupon_dates = dateslib.dates_from_tensor(schedule)
else:
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)
self._batch_shape = tf.shape(coupon_dates.ordinal())[:-1]
payment_dates = coupon_dates[..., 1:]
daycount_fractions = daycount_fn(
start_date=coupon_dates[..., :-1],
end_date=coupon_dates[..., 1:])
coupon_rate = tf.expand_dims(fixed_rate, axis=-1)
self._num_cashflows = tf.shape(payment_dates.ordinal())[-1]
self._payment_dates = payment_dates
self._notional = notional
self._daycount_fractions = daycount_fractions
self._coupon_rate = coupon_rate
self._calendar = coupon_rate
self._fixed_rate = tf.convert_to_tensor(fixed_rate, dtype=self._dtype)
self._daycount_fn = daycount_fn
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 supplied,
the size of the list should be the same as the number of priced
instruments. Defines discount curves for the 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.
When passed as an integet `Tensor`, should be of shape
`batch_shape + [3]` and contain `[year, month, day]` for each date.
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.
When passed as an integet `Tensor`, should be of shape
`batch_shape + [3]` and contain `[year, month, day]` for each date.
Default value: `None`
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`.
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`.
When passed as an integet `Tensor`, should be of shape
`batch_shape + [3]` and contain `[year, month, day]` for each 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`.
When passed as an integet `Tensor`, should be of shape
`batch_shape + [3]` and contain `[year, month, day]` for each 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 including the start and
end dates of the cashflows.
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:
if isinstance(start_date, tf.Tensor):
coupon_dates = dateslib.dates_from_tensor(schedule)
else:
coupon_dates = dateslib.convert_to_date_tensor(schedule)
# Extract starting date for the cashflow
self._start_date = coupon_dates[..., 0]
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)
# Extract starting date for the cashflow
self._start_date = coupon_dates[..., 0]
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,
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 fixings(
self, date: types.DateTensor, fixing_type: curve_types.RateIndexCurve
) -> Tuple[tf.Tensor, daycount_conventions.DayCountConventions]:
"""Returns past fixings of the market rates at the specified dates.
The fixings are represented asannualized simple rates. When fixings are not
provided for a curve, they are assumed to be zero for any date. Otherwise,
it is assumed that the fixings are a left-continuous piecewise-constant
of time with jumps being the supplied fixings.
Args:
date: The dates at which the fixings are computed. Should precede the
valuation date. When passed as an integet `Tensor`, should be of shape
`batch_shape + [3]` and contain `[year, month, day]` for each date.
fixing_type: Rate index curve type for which the fixings are computed.
Returns:
A `Tensor` of the same shape of `date` and of `self.dtype` dtype.
Represents fixings at the requested `date`.
"""
index_type = fixing_type.index.type.value
currency = fixing_type.currency.value
if isinstance(date, tf.Tensor):
# When the input is a Tensor, `dateslib.convert_to_date_tensor` assumes
# that the ordinals are passed. Instead we assume that the inputs
# are of shape `batch_shape + [3]` and are interpreted as pairs
# [year, month, day]
date = dateslib.dates_from_tensor(date)
else:
date = dateslib.convert_to_date_tensor(date)
try:
curve_data = self._market_data_dict["rates"][currency][index_type]
fixing_dates = curve_data["fixing_dates"]
fixing_rates = curve_data["fixing_rates"]
except KeyError:
return tf.zeros(tf.shape(date.ordinal()),
dtype=self._dtype,
name="fixings"), None
if isinstance(fixing_dates, tf.Tensor):
fixing_dates = dateslib.dates_from_tensor(fixing_dates)
else:
fixing_dates = dateslib.convert_to_date_tensor(fixing_dates)
if "fixing_daycount" not in curve_data:
raise ValueError(
f"`fixing_daycount` should be specified for {index_type}.")
fixing_daycount = curve_data["fixing_daycount"]
fixing_daycount = daycount_conventions.DayCountConventions(
fixing_daycount)
fixing_rates = tf.convert_to_tensor(fixing_rates, dtype=self._dtype)
fixing_dates_ordinal = fixing_dates.ordinal()
date_ordinal = date.ordinal()
# Broadcast fixing dates for tf.searchsorted
batch_shape = tf.shape(date_ordinal)[:-1]
# Broadcast valuation date batch shape for tf.searchsorted
fixing_dates_ordinal += tf.expand_dims(tf.zeros(batch_shape,
dtype=tf.int32),
axis=-1)
inds = tf.searchsorted(fixing_dates_ordinal, date_ordinal)
inds = tf.maximum(inds, 0)
inds = tf.minimum(inds, tf.shape(fixing_dates_ordinal)[-1] - 1)
return tf.gather(fixing_rates, inds), fixing_daycount
def __init__(self,
short_position: types.BoolTensor,
currency: Union[types.CurrencyProtoType,
List[types.CurrencyProtoType]],
expiry_date: types.DateTensor,
equity: List[str],
contract_amount: types.FloatTensor,
strike: types.FloatTensor,
is_call_option: List[bool],
business_day_convention: types.BusinessDayConventionProtoType,
calendar: types.BankHolidaysProtoType,
settlement_days: Optional[types.IntTensor] = 0,
discount_curve_type: curve_types_lib.CurveType = None,
discount_curve_mask: types.IntTensor = None,
equity_mask: types.IntTensor = None,
config: Union[AmericanOptionConfig, Dict[str, Any]] = None,
batch_names: Optional[types.StringTensor] = None,
dtype: Optional[types.Dtype] = None,
name: Optional[str] = None):
"""Initializes the batch of American Equity Options.
Args:
short_position: Whether the price is computed for the contract holder.
Default value: `True` which means that the price is for the contract
holder.
currency: The denominated currency.
expiry_date: A `DateTensor` specifying the dates on which the options
expire.
equity: A string name of the underlyings.
contract_amount: A `Tensor` of real dtype and shape compatible with
with `short_position`.
strike: `Tensor` of real dtype and shape compatible with
with `short_position`. Option strikes.
is_call_option: A bool `Tensor` of shape compatible with with
`short_position`. Indicates which options are of call type.
business_day_convention: A business count convention.
calendar: A calendar to specify the weekend mask and bank holidays.
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`.
equity_mask: An optional integer `Tensor` of values ranging from
`0` to `len(equity) - 1` and of shape `batch_shape`. Identifies
a mapping between `equity` list and the underlying instruments.
Default value: `None`.
config: Optional `AmericanOptionConfig` or a dictionary. If dictionary,
then the keys should be the same as the field names of
`AmericanOptionConfig`.
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 'AmericanOption'.
"""
self._name = name or "AmericanOption"
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._contract_amount = tf.convert_to_tensor(
contract_amount, dtype=self._dtype, name="contract_amount")
self._strike = tf.convert_to_tensor(strike, dtype=self._dtype,
name="strike")
self._is_call_option = tf.convert_to_tensor(
is_call_option, dtype=tf.bool, name="strike")
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(expiry_date, types.IntTensor):
self._expiry_date = dateslib.dates_from_tensor(expiry_date)
else:
self._expiry_date = dateslib.convert_to_date_tensor(expiry_date)
self._settlement_days = settlement_days
self._roll_convention = roll_convention
# Get discount and reference curves
self._currency = cashflow_streams.to_list(currency)
self._equity = cashflow_streams.to_list(equity)
if len(self._currency) != len(self._equity):
if len(self._currency) > 1 and len(self._equity) > 1:
raise ValueError(
"Number of currencies and equities should be the same "
"but it is {0} and {1}".format(len(self._currency),
len(self._equity)))
config = _process_config(config)
[
self._model,
self._num_samples,
self._seed,
self._num_exercise_times,
self._num_calibration_samples
] = _get_config_values(config)
if discount_curve_type is None:
discount_curve_type = []
for currency in self._currency:
if currency in config.discounting_curve:
curve_type = config.discounting_curve[currency]
else:
# Default discounting curve
curve_type = curve_types_lib.RiskFreeCurve(
currency=currency)
discount_curve_type.append(curve_type)
# Get masks for discount curves and vol surfaces
[
self._discount_curve_type,
self._discount_curve_mask
] = cashflow_streams.process_curve_types(discount_curve_type,
discount_curve_mask)
[
self._equity,
self._equity_mask,
] = equity_utils.process_equities(self._equity, equity_mask)
# Get batch shape
self._batch_shape = tf.shape(strike)
