Python force_positive_regression Examples

Example #1

File: asset.py Project: fulmicoton/strongchicken

 def test_american_put(self,):
     american_put = AmericanPut(0., 1. * UNIT.YEAR, 40., self.black_scholes)
     optimized_asset = longstaff_schwartz.optimize(american_put,
                                             self.assumption_set,
                                             nb_simulations=100000,
                                             regression=force_positive_regression(polynomial_regression))
     assert optimized_asset.optimization_value.is_compatible(4.472)
     print optimized_asset.optimization_value
     assert optimized_asset.valuation(self.assumption_set, nb_simulations=100000).is_compatible(4.472)

Example #2

File: tutorial1_2_price.py Project: fulmicoton/strongchicken

import numpy as np
from strongchicken.uncertainties import AssumptionSet, BlackScholes, UncertaintySystem
from strongchicken.utils import *
from strongchicken.assets import AmericanPut, longstaff_schwartz
from strongchicken.assets.regression import polynomial_regression, force_positive_regression


market = BlackScholes(36.0, 0.06 * INV.YEAR, 0.2 * INV.SQRT.YEAR)

american_put = AmericanPut(0.0, 1.0 * UNIT.YEAR, 40.0, market)

uncertainty_system = UncertaintySystem([market])
assumption_set = AssumptionSet(uncertainty_system, interest_rate=0.06 * INV.YEAR)

optimized_asset = longstaff_schwartz.optimize(
    american_put, assumption_set, nb_simulations=100000, regression=force_positive_regression(polynomial_regression)
)
print optimized_asset.optimization_value
print optimized_asset.valuation(assumption_set, nb_simulations=1000000)