otp.agg.portfolio_price#

agg.portfolio_price(running=False, bucket_interval=0, bucket_time='end', bucket_units=None, bucket_end_condition=None, end_condition_per_group=False, boundary_tick_bucket='new', group_by=None, weight_field_name='', side='both', weight_type='absolute', symbols=None)#

PORTFOLIO_PRICE aggregation.

For each bucket, computes weighted portfolio price.

Parameters

column (str or Column) – column to be aggregated
running (bool, default=False) –
Aggregation will be calculated as 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 bound to start time. For each tick aggregation will be calculated in [start_time; tick_t].
- running = False
  
  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 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 be set via datetime offset objects like 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', 'ticks', '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.
boundary_tick_bucket (Literal['new', 'previous'], default=new) –
Controls boundary tick ownership.
- previous
  
  A tick on which bucket_end_condition evaluates to “true” belongs to the bucket being closed.
- new
  
  tick belongs to the new bucket.
This parameter is only used if bucket_units is set to “flexible”
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.
weight_field_name (Optional, str or Column, default=) –
The name of the field that contains the current value of weight for a member of the portfolio that contributed the tick.

You can also specify weight through the value of symbol parameter WEIGHT.

If weight_field_name is specified, all ticks should have the field pointed by this parameter and the value of this field is used as the weight.

If weights are not specified in any of these ways, and you are running a single-stage query, the weights take the default value 1.
side (Literal['long', 'short', 'both'], default=both) – When set to long, the price of the portfolio is computed only for the input time series with weight > 0. When set to short, the price of the portfolio is computed only for the input time series with weight < 0. When set to both, the price of the portfolio is computed for all input time series.
weight_type (Literal['absolute', 'relative'], default=absolute) – When set to absolute, the portfolio price is computed as the sum of input_field_value*weight across all members of the portfolio. When set to relative, the portfolio price is computed as the sum of input_field_value*weight/sum_of_all_weights across all members of the portfolio.
symbols (str, list of str, Source, query, eval query, onetick.query.GraphQuery., default=None) – Symbol(s) from which data should be taken.

Examples

Basic example, by default this EP takes PRICE column as input

>>> data = otp.DataSource(
...     'NYSE_TAQ', symbol='AAPL', tick_type='TRD',
...     start=otp.dt(2022, 3, 1), end=otp.dt(2022, 3, 2),
... )
>>> data = data.portfolio_price()
>>> otp.run(data)
        Time  VALUE  NUM_SYMBOLS
0 2022-03-02    1.4            1

Getting portfolio price for multiple symbols:

>>> data = otp.DataSource('NYSE_TAQ', tick_type='TRD', start=otp.dt(2022, 3, 1), end=otp.dt(2022, 3, 2))
>>> data = data.portfolio_price(symbols=['AAPL', 'AAP'])
>>> otp.run(data)
        Time  VALUE  NUM_SYMBOLS
0 2022-03-02  46.81            2

Applying PORTFOLIO_PRICE on custom column

>>> data = otp.Ticks(X=[10.0, 12.5, 11.0, 10.2, 15])
>>> data = data.portfolio_price('X')
>>> otp.run(data)
        Time  VALUE  NUM_SYMBOLS
0 2003-12-04   15.0            1

Specifying weights via column ‘WEIGHS’

>>> data = otp.Ticks(PRICE=[10.0, 12.5, 11.0, 10.2, 15], WEIGHTS=[1, 2, -1, 2, 2])
>>> data = data.portfolio_price(weight_field_name=data['WEIGHTS'])
>>> otp.run(data)
        Time  VALUE  NUM_SYMBOLS
0 2003-12-04   30.0            1