otp.Source.agg#

Source.agg(aggs, running=False, all_fields=False, bucket_interval=0, bucket_time='end', bucket_units=None, bucket_end_condition=None, end_condition_per_group=False, boundary_tick_bucket='new', group_by=None)#

Applies composition of otp.agg aggregations

Parameters

aggs (dict of aggregations) – aggregation dict: key - output column name; value - aggregation
running (bool, default=False) –
Aggregation will be calculated as sliding window. running and bucket_interval parameters determines when new buckets are created.
- running = True
  
  aggregation 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 = 0
    
    Left boundary of window will be bound to start time. For each tick aggregation will be calculated in [start_time; tick_t].
- running = False
  
  buckets 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). If bucket_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
all_fields (Union[Literal[True, False, 'first', 'last', 'high', 'low'], onetick.py.aggregations.high_low.HighTick, onetick.py.aggregations.high_low.LowTick], default=False) –
- If all_fields False - output tick will have only aggregation fields.
- If all_fields False and running True - output ticks are created when a tick enters or leaves the sliding window.
- If all_fields True - an output tick is generated only for arrival events, but all attributes from the input tick causing an arrival event are copied over to the output tick and the aggregation is added as another attribute.
- If all_fields set to “first”, “last”, “high”, or “low” - explicitly set tick selection policy for all fields values. For “high” and “low” “PRICE” field will be selected as an input. Otherwise, you will get the runtime error. If all_fields is set to one of these values, running can’t be True.
- If all_fields is aggregation HighTick or LowTick - set tick selection policy for all fields values to “high” or “low” accordingly. But instead of “PRICE” the field selected as input will be set as aggregation’s first parameter.
bucket_interval (int or Operation or OnetickParameter or symbol parameter or datetime offset object, default=0) –
Determines the length of each bucket (units depends on bucket_units).

If Operation of bool type is passed, acts as bucket_end_condition.

Bucket interval can be set via datetime offset objects like otp.Second, otp.Minute, otp.Hour, otp.Day, otp.Month. In this case you could omit setting bucket_units parameter.

Bucket interval can also be set with integer OnetickParameter or symbol parameter.
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_units (Optional[Literal['seconds', 'ticks', 'days', 'months', 'flexible']], default=None) –
Set bucket interval units.

By default, if bucket_units and bucket_end_condition not specified, set to seconds. If bucket_end_condition specified, then bucket_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_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 passing Operation object.
end_condition_per_group (bool, default=False) –
Controls application of bucket_end_condition in groups.
- end_condition_per_group = True
  
  bucket_end_condition is applied only to the group defined by group_by
- end_condition_per_group = False
  
  bucket_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.
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”
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 in group_by list then GROUP_0 field will be added.

Examples

By default the whole data is aggregated:

>>> data = otp.Ticks(X=[1, 2, 3, 4])
>>> data = data.agg({'X_SUM': otp.agg.sum('X')})
>>> otp.run(data)
        Time  X_SUM
0 2003-12-04     10

Multiple aggregations can be applied at the same time:

>>> data = otp.Ticks(X=[1, 2, 3, 4])
>>> data = data.agg({'X_SUM': otp.agg.sum('X'),
...                  'X_MEAN': otp.agg.average('X')})
>>> otp.run(data)
        Time  X_SUM  X_MEAN
0 2003-12-04     10     2.5

Aggregation can be used in running mode:

>>> data = otp.Ticks(X=[1, 2, 3, 4])
>>> data = data.agg({'CUM_SUM': otp.agg.sum('X')}, running=True)
>>> otp.run(data)
                     Time  CUM_SUM
0 2003-12-01 00:00:00.000        1
1 2003-12-01 00:00:00.001        3
2 2003-12-01 00:00:00.002        6
3 2003-12-01 00:00:00.003       10

Aggregation can be split in buckets:

>>> data = otp.Ticks(X=[1, 2, 3, 4])
>>> data = data.agg({'X_SUM': otp.agg.sum('X')}, bucket_interval=2, bucket_units='ticks')
>>> otp.run(data)
                     Time  X_SUM
0 2003-12-01 00:00:00.001      3
1 2003-12-01 00:00:00.003      7

Running aggregation can be used with buckets too. In this case (all_fields=False and running=True) output ticks are created when a tick enters or leaves the sliding window (that’s why for this example there are 8 output ticks for 4 input ticks):

>>> data = otp.Ticks(X=[1, 2, 3, 4], offset=[0, 1000, 1500, 3600])
>>> data = data.agg(dict(X_MEAN=otp.agg.average("X"),
...                      X_STD=otp.agg.stddev("X")),
...                 running=True, bucket_interval=2)
>>> otp.run(data)
                     Time  X_MEAN     X_STD
0 2003-12-01 00:00:00.000     1.0  0.000000
1 2003-12-01 00:00:01.000     1.5  0.500000
2 2003-12-01 00:00:01.500     2.0  0.816497
3 2003-12-01 00:00:02.000     2.5  0.500000
4 2003-12-01 00:00:03.000     3.0  0.000000
5 2003-12-01 00:00:03.500     NaN       NaN
6 2003-12-01 00:00:03.600     4.0  0.000000
7 2003-12-01 00:00:05.600     NaN       NaN

By default, if you run aggregation with buckets and group_by, then a bucket will be taken first, and after that grouping and aggregation will be performed:

>>> ticks = otp.Ticks(
...     {
...         'QTY': [10, 2, 30, 4, 50],
...         'TRADER': ['A', 'B', 'A', 'B', 'A']
...     }
... )
>>> ticks = ticks.agg(
...     {'SUM_QTY': otp.agg.sum('QTY')}, group_by='TRADER',
...     bucket_interval=3, bucket_units='ticks',
...     running=True, all_fields=True,
... )
>>> otp.run(ticks)
                     Time  TRADER  QTY  SUM_QTY
0 2003-12-01 00:00:00.000       A   10       10
1 2003-12-01 00:00:00.001       B    2        2
2 2003-12-01 00:00:00.002       A   30       40
3 2003-12-01 00:00:00.003       B    4        6
4 2003-12-01 00:00:00.004       A   50       80

In the row with index 4, the result of summing up the trades for trader “A” turned out to be 80, instead of 90. We first took a bucket of 3 ticks, then within it took the group with trader “A” (2 ticks remained) and added up the volumes. To prevent this behaviour, and group ticks first, set parameter end_condition_per_group to True:

>>> ticks = otp.Ticks(
...     {
...         'QTY': [10, 2, 30, 4, 50],
...         'TRADER': ['A', 'B', 'A', 'B', 'A']
...     }
... )
>>> ticks = ticks.agg(
...     {'SUM_QTY': otp.agg.sum('QTY')}, group_by='TRADER',
...     bucket_interval=3, bucket_units='ticks',
...     running=True, all_fields=True,
...     end_condition_per_group=True,
... )
>>> otp.run(ticks)
                     Time  TRADER  QTY  SUM_QTY
0 2003-12-01 00:00:00.000       A   10       10
1 2003-12-01 00:00:00.001       B    2        2
2 2003-12-01 00:00:00.002       A   30       40
3 2003-12-01 00:00:00.003       B    4        6
4 2003-12-01 00:00:00.004       A   50       90

If all_fields=True an output tick is generated only for arrival events, but all attributes from the input tick causing an arrival event are copied over to the output tick and the aggregation is added as another attribute:

>>> data = otp.Ticks(X=[1, 2, 3, 4], offset=[0, 1000, 1500, 3600])
>>> data = data.agg(dict(X_MEAN=otp.agg.average("X"),
...                      X_STD=otp.agg.stddev("X")),
...                 all_fields=True, running=True)
>>> otp.run(data)
                     Time  X  X_MEAN     X_STD
0 2003-12-01 00:00:00.000  1     1.0  0.000000
1 2003-12-01 00:00:01.000  2     1.5  0.500000
2 2003-12-01 00:00:01.500  3     2.0  0.816497
3 2003-12-01 00:00:03.600  4     2.5  1.118034

all_fields parameter can be used when there is need to have all original fields in the output:

>>> ticks = otp.Ticks(X=[3, 4, 1, 2])
>>> data = ticks.agg(dict(X_MEAN=otp.agg.average("X"),
...                       X_STD=otp.agg.stddev("X")),
...                  all_fields=True)
>>> otp.run(data)
        Time  X  X_MEAN     X_STD
0 2003-12-04  3     2.5  1.118034

There are different politics for all_fields parameter:

>>> data = ticks.agg(dict(X_MEAN=otp.agg.average("X"),
...                       X_STD=otp.agg.stddev("X")),
...                  all_fields="last")
>>> otp.run(data)
        Time  X  X_MEAN     X_STD
0 2003-12-04  2     2.5  1.118034

For low/high policies the field selected as input is set this way:

>>> data = ticks.agg(dict(X_MEAN=otp.agg.average("X"),
...                       X_STD=otp.agg.stddev("X")),
...                  all_fields=otp.agg.low_tick(data["X"]))
>>> otp.run(data)
        Time  X  X_MEAN     X_STD
0 2003-12-04  1     2.5  1.118034

Example of using ‘flexible’ buckets. Here every bucket consists of consecutive upticks.

>>> trades = otp.Ticks(PRICE=[194.65, 194.65, 194.65, 194.75, 194.75, 194.51, 194.70, 194.71, 194.75, 194.71])
>>> trades = trades.agg({'COUNT': otp.agg.count(),
...                     'FIRST_TIME': otp.agg.first('Time'),
...                     'LAST_TIME': otp.agg.last('Time')},
...                     bucket_units='flexible',
...                     bucket_end_condition=trades['PRICE'] < trades['PRICE'][-1])
>>> otp.run(trades)
                     Time  COUNT              FIRST_TIME               LAST_TIME
0 2003-12-01 00:00:00.005      5 2003-12-01 00:00:00.000 2003-12-01 00:00:00.004
1 2003-12-01 00:00:00.009      4 2003-12-01 00:00:00.005 2003-12-01 00:00:00.008
2 2003-12-04 00:00:00.000      1 2003-12-01 00:00:00.009 2003-12-01 00:00:00.009