*Last updated: 2023-03-16.*
# tf_quant_finance.experimental.instruments.CMSCashflowStream
View source
Represents a batch of cashflows indexed to a CMS rate.
```python
tf_quant_finance.experimental.instruments.CMSCashflowStream(
start_date, end_date, coupon_spec, dtype=None, name=None
)
```
#### Example:
```python
import numpy as np
import tensorflow as tf
import tf_quant_finance as tff
dates = tff.datetime
instruments = tff.experimental.instruments
start_date = dates.convert_to_date_tensor([(2021, 1, 1)])
maturity_date = dates.convert_to_date_tensor([(2023, 1, 1)])
valuation_date = dates.convert_to_date_tensor([(2021, 1, 1)])
p3m = dates.periods.months(3)
p6m = dates.periods.months(6)
p1y = dates.periods.year()
fix_spec = instruments.FixedCouponSpecs(
coupon_frequency=p6m,
currency='usd',
notional=1.,
coupon_rate=0.0, # Not needed
daycount_convention=instruments.DayCountConvention.ACTUAL_365,
businessday_rule=dates.BusinessDayConvention.NONE)
flt_spec = instruments.FloatCouponSpecs(
coupon_frequency=p3m,
reference_rate_term=p3m,
reset_frequency=p3m,
currency='usd',
notional=1.,
businessday_rule=dates.BusinessDayConvention.NONE,
coupon_basis=0.,
coupon_multiplier=1.,
daycount_convention=instruments.DayCountConvention.ACTUAL_365)
cms_spec = instruments.CMSCouponSpecs(
coupon_frequency=p3m,
tenor=p1y,
float_leg=flt_spec,
fixed_leg=fix_spec,
notional=1.,
coupon_basis=0.,
coupon_multiplier=1.,
businessday_rule=None,
daycount_convention=instruments.DayCountConvention.ACTUAL_365)
cms = instruments.CMSCashflowStream(
start_date, maturity_date, [cms_spec], dtype=dtype)
curve_dates = valuation_date + dates.periods.years([0, 1, 2, 3, 5])
reference_curve = instruments.RateCurve(
curve_dates,
np.array([
0.02, 0.02, 0.025, 0.03, 0.035
], dtype=np.float64),
valuation_date=valuation_date,
dtype=np.float64)
market = instruments.InterestRateMarket(
reference_curve=reference_curve, discount_curve=reference_curve)
price = cms.price(valuation_date, market)
# Expected result: 55512.6295434207
```
#### Args:
* `start_date`: A rank 1 `DateTensor` specifying the starting dates of the
accrual of the first coupon of the cashflow stream. The shape of the
input correspond to the numbercof streams being created.
* `end_date`: A rank 1 `DateTensor` 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`: A list of `CMSCouponSpecs` specifying the details of the
coupon payment for the cashflow stream. The length of the list should
be the same as the number of streams being created. Each coupon within
the list must have the same daycount_convention and businessday_rule.
* `dtype`: `tf.Dtype`. If supplied the dtype for the real variables or ops
either supplied to the FloatingCashflowStream object or created by the
object.
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'.
## Methods
price
View source
```python
price(
valuation_date, market, model=None, pricing_context=None, name=None
)
```
Returns the present value of the stream on the valuation date.
#### Args:
* `valuation_date`: A scalar `DateTensor` specifying the date on which
valuation is being desired.
* `market`: A namedtuple of type `InterestRateMarket` which contains the
necessary information for pricing the cashflow stream.
* `model`: An optional input of type `InterestRateModelType` to specify which
model to use for pricing.
Default value: `None` in which case `NORMAL_RATE` model is used.
* `pricing_context`: An optional input to provide additional parameters (such
as model parameters) relevant for pricing.
* `name`: Python str. The name to give to the ops created by this function.
Default value: `None` which maps to 'price'.
#### Returns:
A Rank 1 `Tensor` of real type containing the modeled price of each stream
contract based on the input market data.