IndexFixing#

class rateslib.data.fixings.IndexFixing(*, index_lag, index_method, date, value=NoInput.blank, identifier=NoInput.blank)#

Bases: _BaseFixing

An index fixing value for settlement of indexed cashflows.

Parameters:

index_lag (int) – The number months by which the reference date is lagged to derive an index value.
index_method (IndexMethod) – The method used for calculating the index value. See IndexMethod.
date (datetime) – The date of relevance for the index fixing, which is its reference value date.
value (float, Dual, Dual2, Variable, optional) – The initial value for the fixing to adopt. Most commonly this is not given and it is determined from a timeseries of published FX rates.
identifier (str, optional) – The string name of the timeseries to be loaded by the Fixings object.

Examples

In [1]: fixings.add("UK-CPI", Series(index=[dt(2000, 1, 1), dt(2000, 2, 1)], data=[100, 110.0]))

In [2]: index_fix = IndexFixing(date=dt(2000, 4, 15), identifier="UK-CPI", index_lag=3, index_method=IndexMethod.Daily)

In [3]: index_fix.value
Out[3]: np.float64(104.66666666666667)

Attributes Summary

`date`	The date of relevance for the fixing, e.g. the publication date of an IBORFixing.
`identifier`	The string name of the timeseries to be loaded by the Fixings object.
`index_lag`	The number months by which the reference date is lagged to derive an index value.
`index_method`	The `IndexMethod` used for calculating the index value.
`value`	The fixing value.

Methods Summary

reset([state])

Sets the value attribute to NoInput, which allows it to be redetermined from a timeseries.

Attributes Documentation

date#: The date of relevance for the fixing, e.g. the publication date of an IBORFixing.

identifier#: The string name of the timeseries to be loaded by the Fixings object.

index_lag#: The number months by which the reference date is lagged to derive an index value.

index_method#: The IndexMethod used for calculating the index value.

value#

The fixing value.

If this value is rateslib.enums.generics.NoInput, then each request will attempt a lookup from a timeseries to obtain a new fixing value.

Once this value is determined it is restated indefinitely, unless _BaseFixing.reset() is called.

Methods Documentation

reset(state=NoInput.blank)#

Sets the value attribute to NoInput, which allows it to be redetermined from a timeseries.

Examples

In [4]: fx_fixing1 = FXFixing(publication=dt(2021, 1, 1), fx_index="eurusd", identifier="A")

In [5]: fx_fixing2 = FXFixing(publication=dt(2021, 1, 1), fx_index="gbpusd", identifier="B")

In [6]: fixings.add("A_eurusd", Series(index=[dt(2021, 1, 1)], data=[1.1]), state=100)

In [7]: fixings.add("B_gbpusd", Series(index=[dt(2021, 1, 1)], data=[1.4]), state=200)

# data is populated from the available Series
In [8]: fx_fixing1.value
Out[8]: np.float64(1.1)

In [9]: fx_fixing2.value
Out[9]: np.float64(1.4)

# fixings are reset according to the data state
In [10]: fx_fixing1.reset(state=100)

In [11]: fx_fixing2.reset(state=100)

# only the private data for fixing1 is removed because of its link to the data state
In [12]: fx_fixing1._value
Out[12]: <NoInput.blank: 0>

In [13]: fx_fixing2._value
Out[13]: np.float64(1.4)

Parameters:: state (int, optional) – If given only fixings whose state matches this value will be reset. If no state is given then the value will be reset.
Return type:: None

IndexFixing#

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