otp.agg.option_price#

option_price(running=False, bucket_interval=0, bucket_time='end', bucket_units='seconds', bucket_end_condition=None, boundary_tick_bucket='new')#

This aggregation requires several parameters to compute the option price. Those are, OPTION_TYPE, STRIKE_PRICE, EXPIRATION_DATE or DAYS_TILL_EXPIRATION, VOLATILITY, and INTEREST_RATE. Each parameter can be specified, either via a symbol parameter with the same name or via a tick field, by specifying the name of that field as an EP parameter, as follows. Besides, VOLATIITY and INTEREST_RATE can also be specified as parameters. In either case, the OPTION_TYPE value must be set to either CALL 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. Additionally, NUMBER_OF_STEPS should be specified in case of Cox-Ross-Rubinstein method.

Parameters

volatility (float) – The historical volatility of the asset’s returns.
interest_rate (float) – The risk-free interest rate.
compute_model (str) – Allowed values are BS and CRR. Choose between Black–Scholes (BS) and Cox-Ross-Rubinstein (CRR) models for computing call/put option price. Default: BS
number_of_steps (int) – Specifies the number of time steps between the valuation and expiration dates. This is a mandatory parameter for CRR model.
compute_delta (bool) – Specifies whether Delta is to be computed or not. This parameter is used only in case of BS model. Default: False
compute_gamma (bool) – Specifies whether Gamma is to be computed or not. This parameter is used only in case of BS model. Default: False
compute_theta (bool) – Specifies whether Theta is to be computed or not. This parameter is used only in case of BS model. Default: False
compute_vega (bool) – Specifies whether Vega is to be computed or not. This parameter is used only in case of BS model. Default: False
compute_rho (bool) – Specifies whether Rho is to be computed or not. This parameter is used only in case of BS model. Default: False
volatility_field_name (str) – Specifies name of the field, which carries the historical volatility of the asset’s returns. Default: empty
interest_rate_field_name (str) – Specifies name of the field, which carries the risk-free interest rate. Default: empty
option_type_field_name (str) – Specifies name of the field, which carries the option type (either CALL or PUT). Default: empty
strike_price_field_name (str) – Specifies name of the field, which carries the strike price of the option. Default: empty
days_in_year (int) – 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. Default: 365
days_till_expiration_field_name (str) – Specifies name of the field, which carries number of days till expiration of the option. Default: empty
expiration_date_field_name (str) – Specifies name of the field, which carries the expiration date of the option, in YYYYMMDD format. Default: empty
running (bool) –
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 wil be generated when tick “enter” window (arrival event) and when “exit” window (exit event)
  - bucket_interval = 0
    
    Left boundary of window will be binded 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.
Default: False - create totally independent buckets. Number of buckets = (end - start) / bucket_interval’)
bucket_interval (int) – Determines the length of each bucket (units depends on bucket_units).
bucket_time (Literal['start', '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 (Literal['seconds', 'ticks', 'days', 'months', 'flexible']) –
Set bucket interval units.

If set to flexible bucket_end_criteria must be set.
bucket_end_condition (condition) – 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”
boundary_tick_bucket (Literal['new', 'previous']) –
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”

Note

Currently, this aggregation can be used only with .apply() method.

Examples

Black–Scholes with parameters passed through symbol params and calculated delta:

>>> symbol = otp.Tick(SYMBOL_NAME='SYMB')
>>> symbol['OPTION_TYPE'] = 'CALL'
>>> symbol['STRIKE_PRICE'] = 100.0
>>> symbol['DAYS_TILL_EXPIRATION'] = 30
>>> symbol['VOLATILITY'] = 0.25
>>> symbol['INTEREST_RATE'] = 0.05
>>> data = otp.Ticks(PRICE=[100.7, 101.1, 99.5], symbol=symbol)
>>> data = otp.agg.option_price(compute_delta=True).apply(data)
>>> data()['SYMB']
        Time     VALUE    DELTA
0 2003-12-04  2.800999  0.50927
>>> data.schema
{'VALUE': <class 'float'>, 'DELTA': <class 'float'>}

Cox-Ross-Rubinstein with parameters passed through fields:

>>> data = otp.Ticks(
...     PRICE=[100.7, 101.1, 99.5],
...     OPTION_TYPE=['CALL']*3,
...     STRIKE_PRICE=[100.0]*3,
...     DAYS_TILL_EXPIRATION=[30]*3,
...     VOLATILITY=[0.25]*3,
...     INTEREST_RATE=[0.05]*3,
... )
>>> data = otp.agg.option_price(
...     compute_model='CRR',
...     number_of_steps=5,
...     option_type_field_name='OPTION_TYPE',
...     strike_price_field_name='STRIKE_PRICE',
...     days_till_expiration_field_name='DAYS_TILL_EXPIRATION',
...     volatility_field_name='VOLATILITY',
...     interest_rate_field_name='INTEREST_RATE',
... ).apply(data)
>>> data()
        Time     VALUE
0 2003-12-04  2.937537

Black–Scholes with some parameters passed through parameters:

>>> data = otp.Ticks(
...     PRICE=[100.7, 101.1, 99.5],
...     OPTION_TYPE=['CALL']*3,
...     STRIKE_PRICE=[100.0]*3,
...     DAYS_TILL_EXPIRATION=[30]*3,
... )
>>> data = otp.agg.option_price(
...     option_type_field_name='OPTION_TYPE',
...     strike_price_field_name='STRIKE_PRICE',
...     days_till_expiration_field_name='DAYS_TILL_EXPIRATION',
...     volatility=0.25,
...     interest_rate=0.05,
... ).apply(data)
>>> data()
        Time     VALUE
0 2003-12-04  2.800999

Currently, this aggregation can’t be used with .agg() method.

>>> data = otp.Ticks(
...     PRICE=[100.7, 101.1, 99.5],
...     OPTION_TYPE=['CALL']*3,
...     STRIKE_PRICE=[100.0]*3,
...     DAYS_TILL_EXPIRATION=[30]*3,
... )
>>> data = data.agg({'RESULT': otp.agg.option_price(
...     option_type_field_name='OPTION_TYPE',
...     strike_price_field_name='STRIKE_PRICE',
...     days_till_expiration_field_name='DAYS_TILL_EXPIRATION',
...     volatility=0.25,
...     interest_rate=0.05,
... )})
Traceback (most recent call last):
    ...
NotImplementedError: Currently .agg() method does not support <class 'onetick.py.aggregations.other.OptionPrice'>