AnalyticalConeBase
- class qf_lib.common.utils.confidence_interval.analytical_cone_base.AnalyticalConeBase[source]
Bases:
object
Methods:
calculate_simple_cone_for_process
(mu, sigma, ...)Creates a simple cone starting from a given date using the solution to the stochastic equation: S(t) = S(0)*exp( (mu-0.5*sigma^2)*t + sigma*N(0,1)*sqrt(t) )
get_expected_value
(mu, sigma, ...)For the mu and sigma calculated based on log returns:
- calculate_simple_cone_for_process(mu: float, sigma: float, number_of_std: float, number_of_steps: int, starting_value=1) PricesSeries [source]
Creates a simple cone starting from a given date using the solution to the stochastic equation: S(t) = S(0)*exp( (mu-0.5*sigma^2)*t + sigma*N(0,1)*sqrt(t) )
- Parameters:
mu – mean return of the process. expressed in the frequency of samples (not annualised)
sigma – std of returns of the process. expressed in the frequency of samples (not annualised)
number_of_std – corresponds to the randomness of the stochastic process. reflects number of standard deviations to get expected values for. For example 1.0 means 1 standard deviation above the expected value.
number_of_steps – length of the cone that we are creating
starting_value – corresponds to the starting price of the instrument
- Returns:
expected values
- Return type:
PriceSeries
- static get_expected_value(mu, sigma, starting_price, number_of_steps, random_element) float [source]
- For the mu and sigma calculated based on log returns:
S(t) = S(0)*exp( (mu-0.5*sigma^2)*t + sigma*N(0,1)*sqrt(t))
- Parameters:
mu – mean of the distribution of returns
sigma – standard deviation of the returns
starting_price – price of the stock at the beginning of the cone
number_of_steps – horizon for which the expected value is calculated
random_element – corresponds to the N(0,1). is expressed in number of standard deviations. Use 1 to model 1std up move, Use 0 to model expected vale of the stock
- Returns:
Expected value of the stock after number_of_steps given the input parameters
- Return type:
float