otp.Source.implied_vol#

Source.implied_vol(column, running=False, all_fields=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, groups_to_display='all', interest_rate=None, price_field='PRICE', option_price_field='OPTION_PRICE', method='newton', precision='1.0e-5', value_for_non_converge='nan_val', option_type_field='', strike_price_field='', days_in_year=365, days_till_expiration_field='', expiration_date_field='')#

IMPLIED_VOL aggregation.

For each bucket, computes implied volatility value for the last tick in the bucket, based on the Black-Scholes option pricing model.

This EP requires a time series of ticks, having the PRICE and OPTION_PRICE attributes.

It also requires several parameters to compute the implied volatility. Those are, OPTION_TYPE, STRIKE_PRICE, EXPIRATION_DATE or DAYS_TILL_EXPIRATION and INTEREST_RATE. Each parameter can be specified either via a symbol parameter with the same name, or via a tick field, by specifying name of that field as an EP parameter. Besides, interest_rate can also be specified as aggregation parameter. In either case OPTION_TYPE must have either CALL value, or PUT. EXPIRATION_DATE is in YYYYMMDD format, a string in case of a symbol parameter and an integer in case of a tick attribute.

Parameters

column (str or Column or Operation) – String with the name of the column to be aggregated or Column object. Operation object can also be used – in this case the results of this operation for each tick are aggregated (see example in common aggregation examples).
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
all_fields (Union[bool, str], default=False) –
See Aggregation buckets guide to see examples of how this parameter works.
- all_fields = True
  
  output ticks include all fields from the input ticks
  - running = True
  an output tick is created only when a tick enters the sliding window
  - running = False
  fields of first tick in bucket will be used
- all_fields = False and running = True
  
  output ticks are created when a tick enters or leaves the sliding window.
- all_fields = “when_ticks_exit_window” and running = True
  
  output ticks are generated only for exit events, but all attributes from the exiting tick are copied over to the output tick and the aggregation is added as another attribute.
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', '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.
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.
interest_rate (Union[float, int, onetick.py.core.column.Column, str, NoneType], default=None) –
The risk-free interest rate.

Could be set via a tick field, by specifying name of that field as string or passing Column object.
price_field (Union[str, onetick.py.core.column.Column], default=PRICE) – The name of the field carrying the price value.
option_price_field (Union[str, onetick.py.core.column.Column], default=OPTION_PRICE) – The name of the field carrying the option price value.
method (str, default=newton) –
Allowed values are newton, newton_with_fallback and bisections.

Choose between newton and bisections for finding successively better approximations to the implied volatility value.

Choose newton_with_fallback to automatically fall back to bisections method when newton fails to converge.
precision (float, default=1.0e-5) – Precision of the implied volatility value.
value_for_non_converge (str, default=nan_val) –
Allowed values are nan_val and closest_found_val, where closest_found_val stands for the volatility value for which the difference between calculated option price and input option price is minimal.

Choose between nan_val and closest_found_val as implied volatility value, when the root-finding method does not converge within the specified precision.
option_type_field (Union[str, onetick.py.core.column.Column], default=) – Specifies name of the field, which carries the option type (either CALL or PUT).
strike_price_field (Union[str, onetick.py.core.column.Column], default=) – Specifies name of the field, which carries the strike price of the option.
days_in_year (int, default=365) – Specifies number of days in a year (say, 365 or 252 (business days, etc.). Used with days_till_expiration parameter to compute the fractional years till expiration.
days_till_expiration_field (Union[str, onetick.py.core.column.Column], default=) – Specifies name of the field, which carries number of days till expiration of the option.
expiration_date_field (Union[str, onetick.py.core.column.Column], default=) – Specifies name of the field, which carries the expiration date of the option, in YYYYMMDD format.

Return type

Source

Examples

Basic example:

>>> data = otp.DataSource('SOME_DB', symbol='AAA', tick_type='TT')  
>>> data = data.implied_vol(
...     interest_rate=0.05, option_type_field=data['OPTION_TYPE'],
...     strike_price_field=data['STRIKE_PRICE'], days_till_expiration_field=data['DAYS_TILL_EXPIRATION'],
... )  
>>> otp.run(data)  
        Time     VALUE
0 2003-12-04  0.889491

Specifying interest_rate and strike_price as symbol parameters:

>>> sym = otp.Ticks({
...     'SYMBOL_NAME': ['TEST'],
...     'INTEREST_RATE': [0.05],
...     'STRIKE_PRICE': [100.0],
... })  
>>> data = otp.DataSource('SOME_DB', symbol='AAA', tick_type='TT')  
>>> data = data.implied_vol(
...     option_type_field=data['OPTION_TYPE'], days_till_expiration_field=data['DAYS_TILL_EXPIRATION'],
... )  
>>> otp.run(data)  
        Time     VALUE
0 2003-12-04  0.889491