derivitec.tradables.types.EqAutocallableWorstOfOptionModelParams

class derivitec.tradables.types.EqAutocallableWorstOfOptionModelParams(pricing_method=Method.FiniteDifference, num_simulations=10000, num_time_points=100, num_strike_points=100, strike_stdev=7.0, max_init_time_step=3, time_step_size=5, min_number_of_time_steps=40, min_log_spot=-5, max_log_spot=5, grid_factor=10, min_number_of_spot_points=100)[source]
__init__(pricing_method=Method.FiniteDifference, num_simulations=10000, num_time_points=100, num_strike_points=100, strike_stdev=7.0, max_init_time_step=3, time_step_size=5, min_number_of_time_steps=40, min_log_spot=-5, max_log_spot=5, grid_factor=10, min_number_of_spot_points=100)[source]
Parameters:

  • pricing_method – The pricing method

  • num_simulations – The number of simulations for Monte Carlo method

  • num_time_points – The number of time points for Monte Carlo method

  • num_strike_points – The number of strike points for MOnte Carlo method

  • strike_stdev – The strike stdev for Monte Carlo method

  • max_init_time_step – The maximum initial time step for Monte Carlo method

  • time_step_size – The time step size for finite difference method

  • min_number_of_time_steps – The minimum number of time steps for finite difference method

  • min_log_spot – The minimum log spot for finite difference method

  • max_log_spot – The maximum log spot for finite difference method

  • grid_factor – The grid factor for finite difference method

  • min_number_of_spot_points – The minimum number of spot points for finite difference method

Methods

IsKeyValuePairListType(key_class_instance, ...)

__init__([pricing_method, num_simulations, ...])

json_params()

JSON param names.

rebuild(content)

Recursive rebuild.

recursive_decompose(item)

Helper function to recursively convert some types to a suitable format for json.

to_dict()

typename()