otp.Source.exp_tw_average#
- Source.exp_tw_average(decay, decay_value_type='half_life_index', running=False, bucket_interval=0, bucket_units=None, bucket_time='end', bucket_end_condition=None, boundary_tick_bucket='new', all_fields=False, group_by=None, end_condition_per_group=False)#
EXP_TW_AVERAGE
aggregation.For each bucket, computes the exponentially time-weighted average value of a specified numeric field. The weight of each point in the time series is computed relative to the end time of the bucket, so that the value which is in effect during some infinitely small time interval delta t has weight (delta t)*exp(-Lambda*(end_time - t)), where Lambda is a constant, end_time represents end time of the bucket, and t represents the timestamp of that infinitely small time interval.
- Parameters
decay (float) – Weight decay. If decay_value_type is set to
lambda
, decay provides the value of the Lambda variable in the aforementioned formula. Otherwise, if decay_value_type is set tohalf_life_index
, decay specifies the necessary number of consecutive ticks, the first one of which would have twice less the weight of the last one. The Lambda value is then calculated using this number.decay_value_type (Literal['lambda', 'half_life_index'], default=half_life_index) – The decay value can specified either directly or indirectly, controlled respectively by lambda and half_life_index values of this parameter.
running (bool, default=False) –
Aggregation will be calculated as sliding window.
running
andbucket_interval
parameters determines when new buckets are created.running
= Trueaggregation will be calculated in a sliding window.
bucket_interval
= N (N > 0)Window size will be N. Output tick will be generated when tick “enter” window (arrival event) and when “exit” window (exit event)
bucket_interval
= 0Left boundary of window will be bound to start time. For each tick aggregation will be calculated in [start_time; tick_t].
running
= Falsebuckets partition the [query start time, query end time) interval into non-overlapping intervals of size
bucket_interval
(with the last interval possibly of a smaller size). Ifbucket_interval
is set to 0 a single bucket for the entire interval is created.Note that in non-running mode OneTick unconditionally divides the whole time interval into specified number of buckets. It means that you will always get this specified number of ticks in the result, even if you have less ticks in the input data.
Default: False
bucket_interval (int or Operation or OnetickParameter or
symbol parameter
, default=0) –Determines the length of each bucket (units depends on
bucket_units
).If
Operation
of bool type is passed, acts asbucket_end_condition
.Bucket interval can also be set with integer
OnetickParameter
orsymbol parameter
.bucket_units (Optional[Literal['seconds', 'ticks', 'days', 'months', 'flexible']], default=None) –
Set bucket interval units.
By default, if
bucket_units
andbucket_end_condition
not specified, set to seconds. Ifbucket_end_condition
specified, thenbucket_units
set to flexible.If set to flexible then
bucket_end_condition
must be set.Note that seconds bucket unit doesn’t take into account daylight-saving time of the timezone, so you may not get expected results when using, for example, 24 * 60 * 60 seconds as bucket interval. In such case use days bucket unit instead. See example in
onetick.py.agg.sum()
.bucket_time (Literal['start', 'end'], default=end) –
Control output timestamp.
start
the timestamp assigned to the bucket is the start time of the bucket.
end
the timestamp assigned to the bucket is the end time of the bucket.
bucket_end_condition (condition, default=None) –
An expression that is evaluated on every tick. If it evaluates to “True”, then a new bucket is created. This parameter is only used if
bucket_units
is set to “flexible”.Also can be set via
bucket_interval
parameter by passingOperation
object.boundary_tick_bucket (Literal['new', 'previous'], default=new) –
Controls boundary tick ownership.
previous
A tick on which
bucket_end_condition
evaluates to “true” belongs to the bucket being closed.new
tick belongs to the new bucket.
This parameter is only used if
bucket_units
is set to “flexible”all_fields (Union[bool, str], default=False) –
all_fields
= Trueoutput ticks include all fields from the input ticks
running
= True
an output tick is created only when a tick enters the sliding window
running
= False
fields of first tick in bucket will be used
all_fields
= False andrunning
= Trueoutput ticks are created when a tick enters or leaves the sliding window.
all_fields
= “when_ticks_exit_window” andrunning
= Trueoutput ticks are generated only for exit events, but all attributes from the exiting tick are copied over to the output tick and the aggregation is added as another attribute.
group_by (list, str or expression, default=None) – When specified, each bucket is broken further into additional sub-buckets based on specified field values. If
Operation
is used then GROUP_{i} column is added. Where i is index in group_by list. For example, if Operation is the only element ingroup_by
list then GROUP_0 field will be added.end_condition_per_group (bool, default=False) –
Controls application of
bucket_end_condition
in groups.end_condition_per_group
= Truebucket_end_condition
is applied only to the group defined bygroup_by
end_condition_per_group
= Falsebucket_end_condition
applied across all groups
This parameter is only used if
bucket_units
is set to “flexible”.When set to True, applies to all bucketing conditions. Useful, for example, if you need to specify
group_by
, and you want to group items first, and create buckets after that.
- Return type
Examples
Basic example
>>> data = otp.Ticks({'A': [1.0, 2.0, 3.0, 3.0, 4.0]}) >>> data = data.exp_tw_average('A', decay=2, bucket_interval=2, bucket_units='ticks') >>> otp.run(data) Time A 0 2003-12-01 00:00:00.001 1.000000 1 2003-12-01 00:00:00.003 2.500087 2 2003-12-04 00:00:00.000 4.000000
You can switch to
lambda
asdecay_value_type
>>> data = otp.Ticks({'A': [1.0, 2.0, 3.0, 3.0, 4.0]}) >>> data = data.exp_tw_average( ... 'A', decay=2, decay_value_type='lambda', bucket_interval=2, bucket_units='ticks', ... ) >>> otp.run(data) Time A 0 2003-12-01 00:00:00.001 1.0000 1 2003-12-01 00:00:00.003 2.5005 2 2003-12-04 00:00:00.000 4.0000
See also
EXP_TW_AVERAGE OneTick event processor