Option Pricer

The option pricer module provides classes for pricing options using different stochastic volatility models.

class quantflow.options.pricer.OptionPricer(model: ~quantflow.options.pricer.M, ttm: dict[int, ~quantflow.options.pricer.MaturityPricer] = <factory>, n: int = 128, max_moneyness_ttm: float = 1.5)

Pricer for options

Methods:

call_price	Price a single call option
maturity	Get a MaturityPricer from cache or create a new one and return it
plot3d	Plot the implied vols surface
reset	Clear the ttm cache

Attributes:

max_moneyness_ttm	Max time-adjusted moneyness to calculate prices
model	The stochastic process used for pricing
n	NUmber of discretization points for the marginal distribution
ttm	Cache for MaturityPricer for different time to maturity

call_price(ttm: float, moneyness: float) → float

Price a single call option

Parameters:

• ttm – Time to maturity

• moneyness – Moneyness as log(Strike/Forward)

This method will use the cache to get the maturity pricer if possible

maturity(ttm: float, **kwargs: Any) → MaturityPricer

Get a MaturityPricer from cache or create a new one and return it

plot3d(max_moneyness_ttm: float = 1.0, support: int = 51, ttm: ndarray[tuple[int, ...], dtype[floating[Any]]] | None = None, **kwargs: Any) → Any

Plot the implied vols surface

It requires plotly to be installed

reset() → None

Clear the ttm cache

max_moneyness_ttm: float = 1.5

Max time-adjusted moneyness to calculate prices

model: M

The stochastic process used for pricing

n: int = 128

NUmber of discretization points for the marginal distribution

ttm: dict[int, MaturityPricer]

Cache for MaturityPricer for different time to maturity

class quantflow.options.pricer.MaturityPricer(ttm: float, std: float, moneyness: FloatArray, call: FloatArray, name: str = '')

Result of option pricing for a given Time to Maturity

Methods:

black	Calculate the Maturity Result for the Black model with same std
call_price	Price a single call option
interp	get interpolated prices
max_moneyness_ttm	Calculate the implied volatility
plot	Plot the results

Attributes:

call	Call prices for the given :attr`.moneyness`
df	Get a dataframe with the results
implied_vols	Calculate the implied volatility
intrinsic_value	Intrinsic value of the option
moneyness	Moneyness as log Strike/Forward
moneyness_ttm	Time adjusted moneyness array
name	Name of the model
std	Standard deviation model of log returns
time_value	Time value of the option
ttm	Time to Maturity

black() → MaturityPricer

Calculate the Maturity Result for the Black model with same std

call_price(moneyness: float) → float

Price a single call option

interp(moneyness: ndarray[tuple[int, ...], dtype[floating[Any]]]) → MaturityPricer

get interpolated prices

max_moneyness_ttm(max_moneyness_ttm: float = 1.0, support: int = 51) → MaturityPricer

Calculate the implied volatility

plot(series: str = 'implied_vol', **kwargs: Any) → Any

Plot the results

call: ndarray[tuple[int, ...], dtype[floating[Any]]]

Call prices for the given :attr`.moneyness`

property df: DataFrame

Get a dataframe with the results

property implied_vols: ndarray[tuple[int, ...], dtype[floating[Any]]]

Calculate the implied volatility

property intrinsic_value: ndarray[tuple[int, ...], dtype[floating[Any]]]

Intrinsic value of the option

moneyness: ndarray[tuple[int, ...], dtype[floating[Any]]]

Moneyness as log Strike/Forward

property moneyness_ttm: ndarray[tuple[int, ...], dtype[floating[Any]]]

Time adjusted moneyness array

name: str

Name of the model

std: float

Standard deviation model of log returns

property time_value: ndarray[tuple[int, ...], dtype[floating[Any]]]

Time value of the option

ttm: float

Time to Maturity