otp.Source.skip_bad_tick#

Source.skip_bad_tick(field, discard_on_match=False, jump_threshold=2.0, num_neighbor_ticks=5, use_absolute_values=False, inplace=False)#

Discards ticks based on whether the value of the attribute specified by field differs from the value of the same attribute in the surrounding ticks more times than a given threshold. Uses SKIP_BAD_TICK EP.

Parameters

field (str, Column) – Name of the field (must be present in the input tick descriptor).
discard_on_match (bool) – When set to True only ticks that did not match the filter are propagated, otherwise ticks that satisfy the filter condition are propagated.
jump_threshold (float) –
A threshold to determine if a tick is “good” or “bad.”

Good ticks are the ticks whose field value differs less than jump_threshold times from the field’s value of less than or half of the surrounding num_neighbor_ticks ticks.
num_neighbor_ticks (int) – The number of ticks before this tick and after this tick to compare a tick against.
use_absolute_values (bool) – When set to True, use absolute values of numbers when checking whether they are within the jump threshold.
inplace (bool) – The flag controls whether operation should be applied inplace or not. If inplace=True, then it returns nothing. Otherwise, method returns a new modified object.
self (Source) –

Return type

Source or None

Examples

Keep ticks whose price did not jump by more than 20% relative to the surrounding ticks:

>>> data = otp.Ticks(X=[10, 11, 15, 11, 9, 10])
>>> data = data.skip_bad_tick(field="X", jump_threshold=1.2, num_neighbor_ticks=1)
>>> otp.run(data)
                     Time   X
0 2003-12-01 00:00:00.000  10
1 2003-12-01 00:00:00.001  11
2 2003-12-01 00:00:00.003  11
3 2003-12-01 00:00:00.005  10

Same example, but with passing column as field parameter:

>>> data = otp.Ticks(X=[10, 11, 15, 11, 9, 10])
>>> data = data.skip_bad_tick(field=data["X"], jump_threshold=1.2, num_neighbor_ticks=1)
>>> otp.run(data)
                     Time   X
0 2003-12-01 00:00:00.000  10
1 2003-12-01 00:00:00.001  11
2 2003-12-01 00:00:00.003  11
3 2003-12-01 00:00:00.005  10

If you want to keep only “bad ticks”, which don’t match the filter, set discard_on_match parameter to True:

>>> data = otp.Ticks(X=[10, 11, 15, 11, 9, 10])
>>> data = data.skip_bad_tick(field=data["X"], jump_threshold=1.2, num_neighbor_ticks=1, discard_on_match=True)
>>> otp.run(data)
                     Time   X
0 2003-12-01 00:00:00.002  15
1 2003-12-01 00:00:00.004   9

In case, if you need to compare values on an absolute basis, set use_absolute_values parameter to True:

>>> data = otp.Ticks(X=[10, -11, -15, 11, 9, 10])
>>> data = data.skip_bad_tick(field=data["X"], jump_threshold=1.2, num_neighbor_ticks=1, use_absolute_values=True)
>>> otp.run(data)
                     Time   X
0 2003-12-01 00:00:00.000  10
1 2003-12-01 00:00:00.001  -11
2 2003-12-01 00:00:00.003  11
3 2003-12-01 00:00:00.005  10