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, **inplace_data)#

Bases:

Data source that generates ticks.

Ticks are placed with the 1 millisecond offset from each other starting from the start of the query interval. It has ability to change distance between ticks using the special reserved field name offset, that specify time offset from a previous tick.

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
**inplace_data (list) – <field_name>: list(<field_values>)

Examples

Pass data in dict

>>> 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 inplace_data

>>> 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 data in 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

Using the offset example

>>> 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

Using pandas.DataFrame. DataFrame should have a Time column containing datetime objects.

>>> start_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