otp.ObSnapshotFlat#

ObSnapshotFlat(running=False, bucket_interval=0, bucket_time='end', bucket_units=None, bucket_end_condition=None, end_condition_per_group=False, group_by=None, groups_to_display='all', max_levels=None, book_uncross_method=None, dq_events_that_clear_book=None, show_full_detail=False, max_initialization_days=1, state_key_max_inactivity_sec=None, size_max_fractional_digits=0, include_market_order_ticks=None, db=None, symbol=<class 'onetick.py.utils.types.adaptive'>, tick_type=<class 'onetick.py.utils.types.adaptive'>, start=<class 'onetick.py.utils.types.adaptive'>, end=<class 'onetick.py.utils.types.adaptive'>, date=None, schema_policy=<class 'onetick.py.utils.types.adaptive'>, guess_schema=None, identify_input_ts=False, back_to_first_tick=0, keep_first_tick_timestamp=None, max_back_ticks_to_prepend=1, where_clause_for_back_ticks=None, symbols=None, presort=<class 'onetick.py.utils.types.adaptive'>, batch_size=None, concurrency=<class 'onetick.py.utils.types.default'>, schema=None, symbol_date=None, **kwargs)#

Construct a source providing order book flat snapshot for a given db. This is just a shortcut for DataSource + ob_snapshot_flat().

Parameters

running (bool, default=False) –
See Aggregation buckets guide to see examples of how this parameter works.

Specifies if the aggregation will be calculated as a 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 set to query start time. For each tick aggregation will be calculated in the interval [start_time; tick_t] from query start time to the tick’s timestamp (inclusive).
- running = False (default)
  
  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
bucket_interval (int or float 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 also be set as a float value if bucket_units is set to seconds. Note that values less than 0.001 (1 millisecond) are not supported.

Bucket interval can be set via some of the datetime offset objects: otp.Milli, 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', '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.
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.
groups_to_display (Literal['all', 'previous'], default=all) – Specifies for which sub-buckets (groups) ticks should be shown for each bucket interval. By default all groups are shown at the end of each bucket interval. If this parameter is set to event_in_last_bucket, only the groups that received at least one tick within a given bucket interval are shown.
max_levels (int, default=None) – Number of order book levels (between 1 and 100_000) that need to be computed. If empty, all levels will be computed.
book_uncross_method (Literal['REMOVE_OLDER_CROSSED_LEVELS'], default=None) – When set to “REMOVE_OLDER_CROSSED_LEVELS”, all ask levels that have price lower or equal to the price of a new bid tick get removed from the book, and all bid levels that have price higher or equal to the price of a new ask tick get removed from the book.
dq_events_that_clear_book (List[str], default=None) – A list of names of data quality events arrival of which should clear the order book.
show_full_detail (bool, default=False) – When set to “true” and if the state key of the input ticks consists of some fields besides PRICE, output ticks will contain all fields from the input ticks for each price level. When set to “false” only PRICE, UPDATE_TIME, SIZE, LEVEL, and BUY_SELL_FLAG fields will be populated. Note: setting this flag to “true” has no effect on a time series that does not have a state key.
max_initialization_days (int, default=1) – This parameter specifies how many days back book event processors should go in order to find the latest full state of the book. The query will not go back resulting number of days if it finds initial book state earlier. When book event processors are used after VIRTUAL_OB EP, this parameter should be set to 0. When set, this parameter takes precedence over the configuration parameter BOOKS.MAX_INITIALIZATION_DAYS.
state_key_max_inactivity_sec (int, default=None) – If set, specifies in how many seconds after it was added a given state key should be automatically removed from the book.
size_max_fractional_digits (int, default=0) – Specifies maximum number of digits after dot in SIZE, if SIZE can be fractional.
include_market_order_ticks (bool, default=None) –
If set, market order ticks (they have price NaN) are included into the order book, and are at the order book’s top level.

Default is False.
db (str, list of str, otp.DB, default=None) – Name(s) of the database or the database object(s).
symbol (str, list of str, Source, query, eval query, default= onetick.py.adaptive) – Symbol(s) from which data should be taken.
tick_type (str, list of str, default= onetick.py.adaptive) – Tick type of the data. If not specified, all ticks from db will be taken. If ticks can’t be found or there are many databases specified in db then default is “TRD”.
start (datetime.datetime, otp.datetime, onetick.py.adaptive, default= onetick.py.adaptive) – Start of the interval from which the data should be taken. Default is onetick.py.adaptive, making the final query deduce the time limits from the rest of the graph.
end (datetime.datetime, otp.datetime, onetick.py.adaptive, default= onetick.py.adaptive) – End of the interval from which the data should be taken. Default is onetick.py.adaptive, making the final query deduce the time limits from the rest of the graph.
date (datetime.datetime, otp.datetime, default=None) – Allows to specify a whole day instead of passing explicitly start and end parameters. If it is set along with the start and end parameters then last two are ignored.
schema_policy (‘tolerant’, ‘tolerant_strict’, ‘fail’, ‘fail_strict’, ‘manual’, ‘manual_strict’, default= onetick.py.adaptive) –
Schema deduction policy:
- ’tolerant’ (default) The resulting schema is a combination of schema and database schema. If the database schema can be deduced, it’s checked to be type-compatible with a schema, and ValueError is raised if checks are failed. Also, with this policy database is scanned 5 days back to find the schema. It is useful when database is misconfigured or in case of holidays.
- ’tolerant_strict’ The resulting schema will be schema if it’s not empty. Otherwise, database schema is used. If the database schema can be deduced, it’s checked if it lacks fields from the schema and it’s checked to be type-compatible with a schema and ValueError is raised if checks are failed. Also, with this policy database is scanned 5 days back to find the schema. It is useful when database is misconfigured or in case of holidays.
- ’fail’ The same as ‘tolerant’, but if the database schema can’t be deduced, raises an Exception.
- ’fail_strict’ The same as ‘tolerant_strict’, but if the database schema can’t be deduced, raises an Exception.
- ’manual’ The resulting schema is a combination of schema and database schema. Compatibility with database schema will not be checked.
- ’manual_strict’ The resulting schema will be exactly schema. Compatibility with database schema will not be checked. If some fields specified in schema do not exist in the database, their values will be set to some default value for a type (0 for integers, NaNs for floats, empty string for strings, epoch for datetimes).
Default value is onetick.py.adaptive (if deprecated parameter guess_schema is not set). If guess_schema is set to True then value is ‘fail’, if False then ‘manual’. If schema_policy is set to None then default value is ‘tolerant’.

Default value can be changed with otp.config.default_schema_policy configuration parameter.

If you set schema manually, while creating DataSource instance, and don’t set schema_policy, it will be automatically set to manual.
guess_schema (bool, default=None) –

Deprecated since version 1.3.16.

Use schema_policy parameter instead.

If guess_schema is set to True then schema_policy value is ‘fail’, if False then ‘manual’.
identify_input_ts (bool, default=False) – If set to False, the fields SYMBOL_NAME and TICK_TYPE are not appended to the output ticks.
back_to_first_tick (int, offset, otp.expr, Operation, default=0) – Determines how far back to go looking for the latest tick before start time. If one is found, it is inserted into the output time series with the timestamp set to start time. Note: it will be rounded to int, so otp.Millis(999) will be 0 seconds.
keep_first_tick_timestamp (str, default=None) – If set, new field with this name will be added to source. This field contains original timestamp of the tick that was taken from before the start time of the query. For all other ticks value in this field will be equal to the value of Time field. This parameter is ignored if back_to_first_tick is not set.
max_back_ticks_to_prepend (int, default=1) – When the back_to_first_tick interval is specified, this parameter determines the maximum number of the most recent ticks before start_time that will be prepended to the output time series. Their timestamp will be changed to start_time.
where_clause_for_back_ticks (onetick.py.core.column_operations.base.Raw, default=None) – A logical expression that is computed only for the ticks encountered when a query goes back from the start time, in search of the ticks to prepend. If it returns false, a tick is ignored.
symbols (str, list of str, Source, query, eval query, onetick.query.GraphQuery., default=None) – Symbol(s) from which data should be taken. Alias for symbol parameter. Will take precedence over it.
presort (bool, default= onetick.py.adaptive) – Add the presort EP in case of bound symbols. Applicable only when symbols is not None. By default, it is set to True if symbols are set and to False otherwise.
batch_size (int, default=None) – Specifies the query batch size for the presort. By default, the value from otp.config.default_batch_size is used.
concurrency (int, default= onetick.py.utils.default) –
Specifies the number of CPU cores to utilize for the presort. By default, the value is inherited from the value of the query where this PRESORT is used.

For the main query it may be specified in the concurrency parameter of run() method (which by default is set to otp.config.default_concurrency).

For the auxiliary queries (like first-stage queries) empty value means OneTick’s default of 1. If otp.config.presort_force_default_concurrency is set then default concurrency value will be set in all PRESORT EPs in all queries.
schema (Optional[Dict[str, type]], default=None) – Dict of <column name> -> <column type> pairs that the source is expected to have. If the type is irrelevant, provide None as the type in question.
symbol_date (otp.datetime or datetime.datetime or int, default=None) – Symbol date or integer in the YYYYMMDD format. Can only be specified if parameters symbols is set.
kwargs (type[str]) – Deprecated. Use schema instead. List of <column name> -> <column type> pairs that the source is expected to have. If the type is irrelevant, provide None as the type in question.

Examples

>>> data = otp.ObSnapshotFlat(db='SOME_DB', tick_type='PRL', symbols='AA', max_levels=1) 
>>> otp.run(data) 
        Time  BID_PRICE1        BID_UPDATE_TIME1  BID_SIZE1  ASK_PRICE1        ASK_UPDATE_TIME1  ASK_SIZE1
0 2003-12-03         5.0 2003-12-01 00:00:00.004          7         2.0 2003-12-01 00:00:00.003          6