otp.Ticks#

class Ticks(data=None, symbol=utils.adaptive_to_default, db=utils.adaptive_to_default, start=utils.adaptive, end=utils.adaptive, tick_type=utils.adaptive, timezone_for_time=None, offset=utils.adaptive, **inplace_data)#

Bases:

Data source that generates ticks.

By default ticks are placed with the 1 millisecond offset from each other starting from the start of the query interval.

The offset for each tick can be changed using the special reserved field name offset, that specifies the time offset from the query start time. offset can be an integer, datetime offset object or otp.timedelta.

Parameters

data (dict, list or pandas.DataFrame, optional) –
Ticks values
- dict – <field_name>: <values>
- list – [[<field_names>], [<first_tick_values>], …, [<n_tick_values>]]
- DataFrame
- None – inplace_data will be used
symbol (str, list of str, Source, query, eval query) – Symbol(s) from which data should be taken.
db (str) – Database to use for tick generation
start (datetime.datetime, otp.datetime, onetick.py.adaptive) – Timestamp for data generation
end (datetime.datetime, otp.datetime, onetick.py.adaptive) – Timestamp for data generation
tick_type (str) – tick type for data generation
timezone_for_time (str) – timezone for data generation
offset (int, datetime offset or otp.timedelta or list of such values or None) – Specifies the time offset for each tick from the query start time. Should be specified as the list of values, one for each tick, or as a single value that will be the same for all ticks. Special value None will disable changing timestamps for each tick, so all timestamps will be set to the query start time. Can’t be used at the same time with the column offset.
**inplace_data (list) – <field_name>: list(<field_values>)

Examples

Pass the data as a dictionary:

>>> d = otp.Ticks({'A': [1, 2, 3], 'B': [4, 5, 6]})
>>> otp.run(d)
                     Time  A  B
0 2003-12-01 00:00:00.000  1  4
1 2003-12-01 00:00:00.001  2  5
2 2003-12-01 00:00:00.002  3  6

Pass the data using key-value arguments:

>>> d = otp.Ticks(A=[1, 2, 3], B=[4, 5, 6])
>>> otp.run(d)
                     Time  A  B
0 2003-12-01 00:00:00.000  1  4
1 2003-12-01 00:00:00.001  2  5
2 2003-12-01 00:00:00.002  3  6

Pass the data using list:

>>> d = otp.Ticks([['A', 'B'],
...                [1, 4],
...                [2, 5],
...                [3, 6]])
>>> otp.run(d)
                     Time  A  B
0 2003-12-01 00:00:00.000  1  4
1 2003-12-01 00:00:00.001  2  5
2 2003-12-01 00:00:00.002  3  6

Pass the data using pandas.DataFrame. DataFrame should have a Time column containing datetime objects.

>>> start_datetime = datetime.datetime(2023, 1, 1, 12)
>>> time_array = [start_datetime + otp.Hour(1) + otp.Nano(1)]
>>> a_array = [start_datetime - otp.Day(15) - otp.Nano(7)]
>>> df = pd.DataFrame({'Time': time_array,'A': a_array})
>>> data = otp.Ticks(df)
>>> otp.run(data, start=start_datetime, end=start_datetime + otp.Day(1))
                           Time                             A
0 2023-01-01 13:00:00.000000001 2022-12-17 11:59:59.999999993

Example with setting offset for each tick:

>>> data = otp.Ticks(X=[1, 2, 3], offset=[0, otp.Nano(1), 1])
>>> otp.run(data)
                           Time  X
0 2003-12-01 00:00:00.000000000  1
1 2003-12-01 00:00:00.000000001  2
2 2003-12-01 00:00:00.001000000  3

Remove the offset for all ticks, in this case the timestamp of each tick is set to the start time of the query:

>>> data = otp.Ticks(X=[1, 2, 3], offset=None)
>>> otp.run(data)
        Time  X
0 2003-12-01  1
1 2003-12-01  2
2 2003-12-01  3

Parameter offset allows to set the same value for all ticks:

>>> data = otp.Ticks(X=[1, 2, 3], offset=otp.Nano(13))
>>> otp.run(data)
                           Time  X
0 2003-12-01 00:00:00.000000013  1
1 2003-12-01 00:00:00.000000013  2
2 2003-12-01 00:00:00.000000013  3