def construct_nvida():
from msppy.msp import MSLP
import numpy as np
nvica = MSLP(T=2, sense=-1, bound=100)
for t in range(2):
m = nvica[t]
buy_now, buy_past = m.addStateVar(name='bought', obj=-1.0)
if t == 1:
sold = m.addVar(name='sold', obj=2)
unsatisfied = m.addVar(name='unsatisfied')
recycled = m.addVar(name='recycled', obj=0.5)
m.addConstr(sold + unsatisfied == 0,
uncertainty={'rhs': range(11)})
m.addConstr(sold + recycled == buy_past)
nvica.set_AVaR(l=0.5, a=0.1, method='direct')
return nvica
Markov_states = [
[[5.0]],
[[5.0]],
[[5.0],[8.0]],
[[5.0],[8.0]]
],
transition_matrix = [
[[1]],
[[1]],
[[0.6,0.4]],
[[0.3,0.7],[0.3,0.7]]
]
)
for t in range(T):
m = newsVendor[t]
now, past = m.addStateVar(ub=100, name="stock")
if t > 0:
buy = m.addVar(name="buy", uncertainty_dependent=0)
sell = m.addVar(name="sell", obj=- RetailPrice)
m.addConstr(now == past + buy - sell)
random = m.addVar(lb=-gurobipy.GRB.INFINITY, ub=gurobipy.GRB.INFINITY, name="demand")
m.addConstr(random == 20, uncertainty={'rhs': Demand[t-1]})
m.addConstr(sell <= random)
m.addConstr(sell >= 0.5 * random)
if t == 0:
m.addConstr(now == 5.0)
Extensive(newsVendor).solve()
newsVendor.set_AVaR(a=0.6, l=0.5)
Extensive(newsVendor).solve()
SDDP(newsVendor).solve(max_iterations=100)
m.addConstrs(
now[j] == capm[j] + idio[j]
for j in range(N)
)
for j in range(N):
m.addConstr(past[j] == capm[j], uncertainty_dependent={past[j]:j})
m.addConstr(past[j] == idio[j], uncertainty={past[j]:f(alpha[j],sigma[j])})
m.addConstr(now[N] == (1+rf) * past[N])
AssetMgt.discretize(
n_samples=100,
method='input',
Markov_states=Markov_states,
transition_matrix=transition_matrix,
random_state=888,
)
AssetMgt.set_AVaR(lambda_=lambda_, alpha_=0.25)
AssetMgt_SDDP = SDDP(AssetMgt)
AssetMgt_SDDP.solve(max_iterations=50, n_steps=3, n_processes=3)
evaluation = Evaluation(AssetMgt)
evaluation.run(n_simulations=1000, random_state=666)
evaluationTrue = EvaluationTrue(AssetMgt)
evaluationTrue.run(n_simulations=1000, random_state=666)
result = {
'mean':numpy.mean(evaluation.pv)-100,
'std':numpy.std(evaluation.pv),
'VAR': numpy.quantile(evaluation.pv,0.05)-100,
'skewness': stats.skew(evaluation.pv),
'kurtosis': 3+stats.kurtosis(evaluation.pv),
'Sharpe Ratio':
(numpy.mean(evaluation.pv)-100-0.005513771)/numpy.std(evaluation.pv)
}
