RollingVarianceSwapStrategy

class sigtech.framework.strategies.rolling_variance_swap.RollingVarianceSwapStrategy

Baseclasses: RollingOptionsStrategyBase

Subclasses: RollingVarianceSwap

A strategy that manages a rolling variance swap.

Keyword arguments:

• underlying: Name of the underlying asset.

• option_group: Name of the corresponding option group. Either the underlying or the option_group is required.

• use_option_replication: If True, the variance is estimated using a basket of options.

• vega_scaled: Specifies if the notional should be scaled by the vega of the strategy.

• lower_strike_fraction: The lower boundary of the strikes for replication (%ATM). Only relevant when use_option_replication is True.

• upper_strike_fraction: The upper boundary of the strikes for replication (%ATM). Only relevant when use_option_replication is True.

• one_sided_option_num: Number of replicating options in the basket on each side of ATM. Only relevant when use_option_replication is True.

Example object creation:

import datetime as dtm

rvs = sig.RollingVarianceSwapStrategy(currency='USD',
                                    maturity='3M',
                                    start_date=dtm.date(2019, 1, 4),
                                    rolling_frequencies=['1M'],
                                    option_group='SPX INDEX OTC OPTION GROUP',
                                    target_quantity=-1.0,
                                    use_option_replication=False)

currency: Optional[str]

lower_strike_fraction: Optional[float]

nominal_weight: Optional[float]

one_sided_option_num: Optional[int]

option_group: Optional[str]

property rolling_table

Builds the strategy’s rolling table. :return: Built rolling table as a pandas DataFrame.

underlying: Optional[str]

upper_strike_fraction: Optional[float]

use_option_replication: Optional[bool]

vega_scaled: Optional[bool]

roll_options(dt)

Set variance swap position.

Parameters:

dt – Decision datetime.