otp.Source.mkt_activity#

Source.mkt_activity(calendar_name=None, inplace=False)#

Adds a string field named MKT_ACTIVITY to each tick in the input tick stream.

The value of this field is set to the union of session flags that apply for the security at the time of the tick, as specified in the calendar sections of the reference database (see Reference Database Guide).

Session flags may differ between databases, but the following letters have reserved meaning: L - half day, H - holiday, W - weekend, R - regular.

The calendar can either be specified explicitly by name (the CALENDAR sections of the reference database are assumed to contain a calendar with such a name), or default to the security- or exchange-level calendars for the queried symbol (the SYMBOL_CALENDAR or EXCH_CALENDAR sections of the reference database).

The latter case requires a non-zero symbol date to be specified for queried symbols (see parameter symbol_date in otp.run).

Parameters

calendar_name (str, Column) –
The calendar name to choose for the respective calendar from the CALENDAR sections of the reference database. It can be a string constant or the name of the field with per-tick calendar name.

When this parameter is not specified, default security- or exchange-level calendars configured for the queried database and symbol are used (but symbol date must be specified in this case).
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

Note

When applying mkt_activity() to aggregated data, please take into account that session flags may change during the aggregation bucket. The field MKT_ACTIVITY, in this case, will represent the session flags at the time assigned to the bucket, which may be different from the session flags at some times during the bucket.

Examples

By default, security- or exchange-level calendars configured for the queried database and symbol are used (but symbol date must be specified in this case):

>>> data = otp.DataSource(...)  
>>> data = data.mkt_activity()  
>>> otp.run(data, date=otp.date(2022, 1, 1), symbol_date=otp.date(2022, 1, 1))  

Otherwise, parameter calendar_name must be specified:

>>> data = otp.DataSource(...)  
>>> data = data.mkt_activity(calendar_name='WNY')  
>>> otp.run(data, date=otp.date(2022, 1, 1))  

Parameter calendar_name can also be specified as a column. In this case calendar name can be different for each tick:

>>> data = otp.DataSource(...)  
>>> data = data.mkt_activity(calendar_name=data['CALENDAR_NAME'])  
>>> otp.run(data, date=otp.date(2022, 1, 1))  

In this example you can see how market activity status is changing during the days. We are getting first and last tick of the group each time the type of market activity is changed. You can see regular trades (R) from 9:30 to 16:00, and a holiday (H) on 2018-02-07.

>>> data = otp.DataSource('TRAIN_A_PRL_TRD', tick_type='TRD', symbols='MSFT')  
>>> data = data.mkt_activity('FRED')  
>>> data = data[['PRICE', 'SIZE', 'MKT_ACTIVITY']]  
>>> first = data.first(1, bucket_interval=(data['MKT_ACTIVITY'] != data['MKT_ACTIVITY'][-1]))  
>>> last = data.last(1, bucket_interval=(data['MKT_ACTIVITY'] != data['MKT_ACTIVITY'][-1]))  
>>> data = otp.merge([first, last])  
>>> df = otp.run(data,  
...              start=otp.dt(2018, 2, 1), end=otp.dt(2018, 2, 9),
...              symbol_date=otp.dt(2018, 2, 1), timezone='EST5EDT')
>>> df[['Time', 'MKT_ACTIVITY']]  
                      Time MKT_ACTIVITY
2018-02-01 01:31:44.466
2018-02-01 09:29:59.996
2018-02-01 09:30:00.225            R
2018-02-01 15:59:58.857            R
2018-02-01 16:00:01.858
2018-02-02 09:29:50.366
2018-02-02 09:30:01.847            R
2018-02-02 15:59:59.829            R
2018-02-02 16:00:01.782
2018-02-05 09:29:43.084
2018-02-05 09:30:00.301            R
2018-02-05 15:59:59.974            R
2018-02-05 16:00:02.438
2018-02-06 09:29:27.279
2018-02-06 09:30:00.045            R
2018-02-06 15:59:59.903            R
2018-02-06 16:01:03.524
2018-02-07 09:29:56.739
2018-02-07 09:30:00.365            H
2018-02-07 15:59:59.940            H
2018-02-07 16:00:00.187
2018-02-08 09:29:28.446
2018-02-08 09:30:00.658            F
2018-02-08 15:59:59.564            F
2018-02-08 16:00:02.355
2018-02-08 19:59:57.061